diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..c67d33cc4b938a9c5431ad972cbd3fb2521ad003
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,5 @@
+# gitignore
+
+**.ipynb_checkpoints
+**__pycache__
+
diff --git a/README.md b/README.md
index d69989087b88bb7fa93e621789f29f9690b98b20..d07d4eeb8bf46b01338cb8d96dfc4ba9ce26437a 100644
--- a/README.md
+++ b/README.md
@@ -1,92 +1,22 @@
-# keshtgar-etal-2024-cyclone-crh-uncertainties
+# Code repository for the publication "Uncertainties in cloud-radiative heating within an idealized extratropical cyclone".
 
+The preprint is submitted to the Atmospheric Chemistry and Physics (ACP) journal.
+https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1699/
 
+**Author:** Behrooz Keshtgar, IMKTRO, Karlsruhe Institute of Technology, behrooz.keshtgar@kit.edu
 
-## Getting started
+**The repository contains:**
 
-To make it easy for you to get started with GitLab, here's a list of recommended next steps.
+* **sims:** ICON model setup
+  - Baroclinic Life Cycle Simulation (ICON-NWP)
+  - Large Eddy Model Simulations (ICON-LEM)
 
-Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)!
+* **offlineRT:** Procedures for offline radiative transfer calculations using LibRadTran
+  - Scripts for post-processing LEM simulation output for use in offline radiative transfer calculations
+  - Python scripts for post-processing LibRadTran results
+  - Bash scripts for running LibRadTran
+  - Python scripts for preprocessing and data archiving
 
-## Add your files
+* **plots4paper:** jupyter notebooks for figures used in papers, also figure pdfs
 
-- [ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files
-- [ ] [Add files using the command line](https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line) or push an existing Git repository with the following command:
-
-```
-cd existing_repo
-git remote add origin https://gitlab.phaidra.org/climate/keshtgar-etal-2024-cyclone-crh-uncertainties.git
-git branch -M main
-git push -uf origin main
-```
-
-## Integrate with your tools
-
-- [ ] [Set up project integrations](https://gitlab.phaidra.org/climate/keshtgar-etal-2024-cyclone-crh-uncertainties/-/settings/integrations)
-
-## Collaborate with your team
-
-- [ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/)
-- [ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html)
-- [ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically)
-- [ ] [Enable merge request approvals](https://docs.gitlab.com/ee/user/project/merge_requests/approvals/)
-- [ ] [Automatically merge when pipeline succeeds](https://docs.gitlab.com/ee/user/project/merge_requests/merge_when_pipeline_succeeds.html)
-
-## Test and Deploy
-
-Use the built-in continuous integration in GitLab.
-
-- [ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/index.html)
-- [ ] [Analyze your code for known vulnerabilities with Static Application Security Testing(SAST)](https://docs.gitlab.com/ee/user/application_security/sast/)
-- [ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html)
-- [ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/)
-- [ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html)
-
-***
-
-# Editing this README
-
-When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thank you to [makeareadme.com](https://www.makeareadme.com/) for this template.
-
-## Suggestions for a good README
-Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.
-
-## Name
-Choose a self-explaining name for your project.
-
-## Description
-Let people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors.
-
-## Badges
-On some READMEs, you may see small images that convey metadata, such as whether or not all the tests are passing for the project. You can use Shields to add some to your README. Many services also have instructions for adding a badge.
-
-## Visuals
-Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method.
-
-## Installation
-Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection.
-
-## Usage
-Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README.
-
-## Support
-Tell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc.
-
-## Roadmap
-If you have ideas for releases in the future, it is a good idea to list them in the README.
-
-## Contributing
-State if you are open to contributions and what your requirements are for accepting them.
-
-For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self.
-
-You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.
-
-## Authors and acknowledgment
-Show your appreciation to those who have contributed to the project.
-
-## License
-For open source projects, say how it is licensed.
-
-## Project status
-If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers.
+The raw data from ICON simulations are archived n the High-Performance Storage System at the German Climate Computing Center (DKRZ). The post-processed data used in the analysis along with a copy of the Git repository will be published at the LMU open data server.
diff --git a/offlineRT/README.md b/offlineRT/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..3b78e0802962da9d06ad945fc54f99cf46614c05
--- /dev/null
+++ b/offlineRT/README.md
@@ -0,0 +1,23 @@
+This directory contains scripts for pre- and post-processing of input and output data for LibradTran and bash scripts to run the offline radiative transfer calculations.
+
+* **input_for_libradtran.ipynb** This Jupyter notebook generates the input files for LibRadTran from ICON-LEM output files.
+
+* Subdirectories **c_cluster_solar/thermal_...** are for different radiative transfer calculations.
+
+* List of radiative transfer calculations:
+
+ - c_cluster_solar/thermal_ipa3d: 1D radiative transfer calculations with Delta-Eddington two-stream solver and ice-optical parameterizations by Fu and Baum
+ - c_cluster_solar/thermal_mystic: 3D and 1D radiative transfer calculations with the MYSTIC solver
+ - c_cluster_solar/thermal_ipa3d_cg/dl: 1D radiative transfer calculations with the Delta-Eddington two-stream solver for NWP homogeneous grid-box clouds and homogeneous clouds with cloud fraction at a resolution of 2.5 km
+ - solar/thermal_clear_sky: Clear-sky radiative transfer calculations with Delta-Eddington two-stream and MYSTIC solvers
+
+* To run the offline radiative transfer calculations, run the bash script *'step1_makeInpFiles.sh'* in the desired radiative transfer_subdirectory/ccSolar/thermal. This will automatically create input files for all subdomains and time steps to be used by the *'uvspec'* program of LibRadtran.Finally, run *'submit_runs.sh'* to distribute the runs to different nodes. The outputs are radiative heating rates written as ASCII files in the representative subdirectory.
+
+* The python scripts **convert_libradtran_data_to_netcdf().py** process the all-sky and clear-sky radiative heating rate outputs from each radiative transfer calculation and merge the outputs from all subdomains to get the cloud radiative heating over the entire LEM domain and save the result as a netcdf file.
+
+* The python script **pre_process_data_for_archiving.py** is further post-processing the output data for archiving and using in the analysis.
+
+* The python script **estimating_MC_noise_of_MYSTIC.py** estimates the Monte Carlo noise of the MYSTIC solver, please refer to the paper for more information.
+
+
+ 
diff --git a/offlineRT/c_cluster_solar_ipa3d/add_to_submit_file.txt b/offlineRT/c_cluster_solar_ipa3d/add_to_submit_file.txt
new file mode 100644
index 0000000000000000000000000000000000000000..19439a7678e164092aacfea016d57e4018e80baf
--- /dev/null
+++ b/offlineRT/c_cluster_solar_ipa3d/add_to_submit_file.txt
@@ -0,0 +1,12 @@
+#!/bin/bash
+#SBATCH --partition=compute
+#SBATCH --account=bb1135
+#SBATCH --nodes=1
+#SBATCH --mem=800000
+#SBATCH --exclusive
+#SBATCH --output=libther.sh.%j.out
+#SBATCH --error=libther.sh.%j.err
+#---------------------------------
+
+
+wait
diff --git a/offlineRT/c_cluster_solar_ipa3d/ccSolar/VARIABLES b/offlineRT/c_cluster_solar_ipa3d/ccSolar/VARIABLES
new file mode 100644
index 0000000000000000000000000000000000000000..25735e7b4562e303bdec8fbb4ab9169a768f0d3e
--- /dev/null
+++ b/offlineRT/c_cluster_solar_ipa3d/ccSolar/VARIABLES
@@ -0,0 +1,32 @@
+export solver=("mystic" "twostr")
+
+export time_array=("05T1000" "05T1030" "05T1100" "05T1130" "05T1200" "05T1230" "05T1300" "05T1330" "05T1400")
+
+export dom=("1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12" "13" "14" "15" "16" "17" "18" "19" "20" "21" "22" "23" "24" "25" "26" "27" "28" "29" "30" "31" "32" "33" "34" "35" "36")
+
+export dom1=("1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12")
+
+export dom2=("13" "14" "15" "16" "17" "18" "19" "20" "21" "22" "23" "24")
+
+export dom3=("25" "26" "27" "28" "29" "30" "31" "32" "33" "34" "35" "36")
+
+export sza_05T1000=()
+
+export sza_05T1030=()
+
+export sza_05T1100=()
+
+export sza_05T1130=()
+
+export sza_05T1200=()
+
+export sza_05T1230=()
+
+export sza_05T1300=()
+
+export sza_05T1330=()
+
+export sza_05T1400=()
+
+export isim_array=("01" "02" "03" "04")
+#export isim_array=("01" "02" "03" "04" "05" "06" "07" "08" "09" "10")
diff --git a/offlineRT/c_cluster_solar_ipa3d/ccSolar/s_runs1.sh b/offlineRT/c_cluster_solar_ipa3d/ccSolar/s_runs1.sh
new file mode 100755
index 0000000000000000000000000000000000000000..2a329fd1ae5822f0617062c7407b7708de198219
--- /dev/null
+++ b/offlineRT/c_cluster_solar_ipa3d/ccSolar/s_runs1.sh
@@ -0,0 +1,26 @@
+#!/bin/bash
+#SBATCH --partition=compute
+#SBATCH --account=bb1135
+#SBATCH --nodes=1
+#SBATCH --mem=512000
+#SBATCH --exclusive
+#SBATCH --output=libther.sh.%j.out
+#SBATCH --error=libther.sh.%j.err
+
+
+#---------------------------------
+. VARIABLES
+
+pathh=$(cd ../../ && pwd)
+ffname='ipa3d'
+ssource='solar'
+timee=$1
+num=$2
+
+echo "Job started @ $(date)"
+for dm in "${dom1[@]}"
+do	
+/home/b/b381185/libRadtran/bin/uvspec < ${pathh}/c_cluster_${ssource}_${ffname}/${ffname}_${timee}_${dm}_isim${num}/libsetup.inp &
+done
+wait
+echo "Job finished @ $(date)"
diff --git a/offlineRT/c_cluster_solar_ipa3d/ccSolar/s_runs2.sh b/offlineRT/c_cluster_solar_ipa3d/ccSolar/s_runs2.sh
new file mode 100755
index 0000000000000000000000000000000000000000..fb4af3c037a4686e1fa461f586a431290ff2c5ce
--- /dev/null
+++ b/offlineRT/c_cluster_solar_ipa3d/ccSolar/s_runs2.sh
@@ -0,0 +1,26 @@
+#!/bin/bash
+#SBATCH --partition=compute
+#SBATCH --account=bb1135
+#SBATCH --nodes=1
+#SBATCH --mem=512000
+#SBATCH --exclusive
+#SBATCH --output=libther.sh.%j.out
+#SBATCH --error=libther.sh.%j.err
+
+
+#---------------------------------
+. VARIABLES
+
+pathh=$(cd ../../ && pwd)
+ffname='ipa3d'
+ssource='solar'
+timee=$1
+num=$2
+
+echo "Job started @ $(date)"
+for dm in "${dom2[@]}"
+do	
+/home/b/b381185/libRadtran/bin/uvspec < ${pathh}/c_cluster_${ssource}_${ffname}/${ffname}_${timee}_${dm}_isim${num}/libsetup.inp &
+done
+wait
+echo "Job finished @ $(date)"
diff --git a/offlineRT/c_cluster_solar_ipa3d/ccSolar/s_runs3.sh b/offlineRT/c_cluster_solar_ipa3d/ccSolar/s_runs3.sh
new file mode 100755
index 0000000000000000000000000000000000000000..e5d53c3f611f803cf6b9b35cb75085964f2ede7d
--- /dev/null
+++ b/offlineRT/c_cluster_solar_ipa3d/ccSolar/s_runs3.sh
@@ -0,0 +1,26 @@
+#!/bin/bash
+#SBATCH --partition=compute
+#SBATCH --account=bb1135
+#SBATCH --nodes=1
+#SBATCH --mem=512000
+#SBATCH --exclusive
+#SBATCH --output=libther.sh.%j.out
+#SBATCH --error=libther.sh.%j.err
+
+
+#---------------------------------
+. VARIABLES
+
+pathh=$(cd ../../ && pwd)
+ffname='ipa3d'
+ssource='solar'
+timee=$1
+num=$2
+
+echo "Job started @ $(date)"
+for dm in "${dom3[@]}"
+do	
+/home/b/b381185/libRadtran/bin/uvspec < ${pathh}/c_cluster_${ssource}_${ffname}/${ffname}_${timee}_${dm}_isim${num}/libsetup.inp &
+done
+wait
+echo "Job finished @ $(date)"
diff --git a/offlineRT/c_cluster_solar_ipa3d/ccSolar/step1_makeInpFiles.sh b/offlineRT/c_cluster_solar_ipa3d/ccSolar/step1_makeInpFiles.sh
new file mode 100755
index 0000000000000000000000000000000000000000..1a8253c2fd57d4c83c8dd201d6244c51b610b145
--- /dev/null
+++ b/offlineRT/c_cluster_solar_ipa3d/ccSolar/step1_makeInpFiles.sh
@@ -0,0 +1,196 @@
+#!/usr/bin/env bash
+
+. VARIABLES
+
+pathh=$(cd ../../../ && pwd)
+ffname='ipa3d'
+
+echo
+echo "Shell script sucessfully running!"
+echo
+
+echo
+echo "BUILD FOLDER NAMES AND INPUT FILES"
+echo
+
+#First go out of run folder:
+cd ..
+
+SCRIPTDIR=$(pwd)
+
+for it in "${time_array[@]}"
+do
+    echo 'time = ' $it
+
+i=0
+for dm in "${dom[@]}"
+do
+echo 'dom = ' $dm
+if [ $it == 05T1000 ]
+then
+	echo 'sza = ' "${sza_05T1000[$i]}"
+	sza="${sza_05T1000[$i]}"
+elif [ $it == 05T1030 ]
+then
+	echo 'sza = ' "${sza_05T1030[$i]}"
+        sza="${sza_05T1030[$i]}"
+elif [ $it == 05T1100 ]
+then
+	echo 'sza = ' "${sza_05T1100[$i]}"
+        sza="${sza_05T1100[$i]}"
+elif [ $it == 05T1130 ]
+then
+        echo 'sza = ' "${sza_05T1130[$i]}"
+        sza="${sza_05T1130[$i]}"
+elif [ $it == 05T1200 ]
+then
+        echo 'sza = ' "${sza_05T1200[$i]}"
+        sza="${sza_05T1200[$i]}"
+elif [ $it == 05T1230 ]
+then
+        echo 'sza = ' "${sza_05T1230[$i]}"
+        sza="${sza_05T1230[$i]}"
+elif [ $it == 05T1300 ]
+then
+        echo 'sza = ' "${sza_05T1300[$i]}"
+        sza="${sza_05T1300[$i]}"
+elif [ $it == 05T1330 ]
+then
+        echo 'sza = ' "${sza_05T1330[$i]}"
+        sza="${sza_05T1330[$i]}"	
+else
+	echo 'sza = ' "${sza_05T1400[$i]}"
+        sza="${sza_05T1400[$i]}"	
+fi
+i=$i+1
+
+
+    CLOUD_WC_FILE_INP=''$pathh'/wc3d_'$dm'_202201'$it'33Z.dat'
+    CLOUD_IC_FILE_INP=''$pathh'/ic3d_'$dm'_202201'$it'33Z.dat'
+    ZVALUES="$(head -n2 ${CLOUD_WC_FILE_INP} | tail -n1 | cut -d " " -f 3-)"
+
+      for isim in "${isim_array[@]}"
+      do
+      echo
+      echo "isim = " $isim
+      if [ $isim == 01 ]
+      then
+         ice_p='fu'
+         ice_h='#ic_habit rough-aggregate'
+      elif [ $isim == 02 ]
+      then
+         ice_p='baum_v36'
+         ice_h='ic_habit ghm'
+      elif [ $isim == 03 ]
+      then
+         ice_p='baum_v36'
+         ice_h='ic_habit solid-column'
+      else
+         ice_p='baum_v36'
+         ice_h='ic_habit rough-aggregate'
+      fi 	 
+      # Create libradtran output file name:
+      OUT_FILE=$ffname'_'$it'_'$dm'_isim'$isim'.out'
+
+      echo 'OUT_FILE = ' $OUT_FILE
+
+      FOLDER_NAME=$ffname'_'$it'_'$dm'_isim'$isim
+
+      mkdir $FOLDER_NAME
+
+      cd $FOLDER_NAME
+
+      echo 'Entering folder name = ' $FOLDER_NAME
+      # Create output folder for results:
+      mkdir output
+
+INP_FILE_NAME='libsetup.inp'
+LIBRAD="/home/b/b381185/libRadtran"
+
+############################
+# CREATE INPUT FILE HERE !!!
+cat > $INP_FILE_NAME << EOF
+data_files_path $LIBRAD/data/
+atmosphere_file ${pathh}/atmosphere_mean_${dm}_202201${it}33Z.dat
+
+
+albedo 0.07
+source solar
+sza ${sza}
+#phi ${phi}
+mol_abs_param Fu
+wavelength_index 1  7
+output_process sum
+
+# gas profiles
+#mixing_ratio CO2 348
+#mixing_ratio O2 209460
+#mixing_ratio CH4 1.650 
+#mixing_ratio N2O 0.396
+
+mixing_ratio F11 0.0
+mixing_ratio F12 0.0
+mixing_ratio F22 0.0
+mixing_ratio NO2 0.0
+
+mol_modify O4 0.0 DU
+mol_modify BRO 0.0 DU
+mol_modify OCLO 0.0 DU
+mol_modify HCHO 0.0 DU
+mol_modify SO2 0.0 DU
+mol_modify CO 0.0 DU
+mol_modify N2 0.0 DU
+
+#surface
+albedo_library IGBP
+brdf_rpv_type 17
+
+rte_solver rodents
+ipa_3d
+
+heating_rate layer_fd
+mc_backward_output heat K_per_day
+
+mc_sample_grid 420 343 0.227 0.394
+
+mc_basename $SCRIPTDIR/$FOLDER_NAME/output/$OUT_FILE
+
+wc_properties hu interpolate
+ic_properties $ice_p interpolate
+$ice_h
+
+wc_file 3D $CLOUD_WC_FILE_INP
+ic_file 3D $CLOUD_IC_FILE_INP
+
+quiet
+EOF
+### END OF INPUT FILE ###########
+#################################
+cat >ther_HR.sh<<EOF
+#! /bin/bash
+
+#SBATCH --account=bb1135
+#SBATCH --job-name=mystic_dom01.run
+#SBATCH --partition=shared
+#SBATCH --nodes=1
+#SBATCH --output=libther.sh.%j.out
+#SBATCH --error=libther.sh.%j.err
+#SBATCH --exclusive
+
+cd $(pwd)
+echo "Job started @ \$(date)"
+$LIBRAD/bin/uvspec < $INP_FILE_NAME
+echo "Job finished @ \$(date)"
+EOF
+
+        #Leave mysti/mcipa folder:
+        cd ..
+
+        done #over isim
+
+done # over dom
+done # over itime
+
+echo
+echo 
+echo 'The END'
diff --git a/offlineRT/c_cluster_solar_ipa3d/ccSolar/submit_runs.sh b/offlineRT/c_cluster_solar_ipa3d/ccSolar/submit_runs.sh
new file mode 100755
index 0000000000000000000000000000000000000000..77a75ae5ddaa96c9139bf50ee6e6586c6dbc0a8a
--- /dev/null
+++ b/offlineRT/c_cluster_solar_ipa3d/ccSolar/submit_runs.sh
@@ -0,0 +1,17 @@
+#!/usr/bin/env bash
+
+#-------------------------------------------
+. VARIABLES
+
+for tt in "${time_array[@]}"
+do
+for ss in "${isim_array[@]}"
+do
+   echo "submitting runs for = " $tt'_'$ss
+
+   sbatch s_runs1.sh $tt $ss
+   sbatch s_runs2.sh $tt $ss
+   sbatch s_runs3.sh $tt $ss
+
+done 
+done
diff --git a/offlineRT/c_cluster_solar_ipa3d/find_missing_simulations.sh b/offlineRT/c_cluster_solar_ipa3d/find_missing_simulations.sh
new file mode 100755
index 0000000000000000000000000000000000000000..e0ab46047a25e8b61045c504db6dfd7c74e94807
--- /dev/null
+++ b/offlineRT/c_cluster_solar_ipa3d/find_missing_simulations.sh
@@ -0,0 +1,39 @@
+#!/usr/bin/env bash
+
+export time_array=("05T1000" "05T1030" "05T1100" "05T1130" "05T1200" "05T1230" "05T1300" "05T1330" "05T1400")
+
+export dom=("1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12" "13" "14" "15" "16" "17" "18" "19" "20" "21" "22" "23" "24" "25" "26" "27" "28" "29" "30" "31" "32" "33" "34" "35" "36")
+
+export isim_array=("01" "02" "03" "04")
+
+pathh=$(pwd)
+#----------------------------------------------------------------------------------------
+ffname1='ipa3d'
+
+for it in "${time_array[@]}"
+do
+    echo 'time = ' $it
+
+for dm in "${dom[@]}"
+do
+echo 'dom = ' $dm
+
+for isim in "${isim_array[@]}"
+do
+
+if [ -n "$(ls -A $ffname1'_'$it'_'$dm'_isim'$isim/output 2>/dev/null)" ]
+then
+  echo "file exist (${ffname1}_'${it}'_'${dm}'_isim'${isim}'.out.abs.spc')"
+else
+  echo "file does not exist (${ffname1}_'${it}'_'${dm}'_isim'${isim}'.out.abs.spc')"
+  #count=(($i + 1))
+  # let's put these into a file for rerun
+  echo "/home/b/b381185/libRadtran/bin/uvspec < ${pathh}/${ffname1}_${it}_${dm}_isim${isim}/libsetup.inp &" >> submit_ipa3d.sh
+
+fi
+
+done
+done
+done
+
+#-----------------------
diff --git a/offlineRT/c_cluster_solar_ipa3d_cg/add_to_submit_file.txt b/offlineRT/c_cluster_solar_ipa3d_cg/add_to_submit_file.txt
new file mode 100644
index 0000000000000000000000000000000000000000..19439a7678e164092aacfea016d57e4018e80baf
--- /dev/null
+++ b/offlineRT/c_cluster_solar_ipa3d_cg/add_to_submit_file.txt
@@ -0,0 +1,12 @@
+#!/bin/bash
+#SBATCH --partition=compute
+#SBATCH --account=bb1135
+#SBATCH --nodes=1
+#SBATCH --mem=800000
+#SBATCH --exclusive
+#SBATCH --output=libther.sh.%j.out
+#SBATCH --error=libther.sh.%j.err
+#---------------------------------
+
+
+wait
diff --git a/offlineRT/c_cluster_solar_ipa3d_cg/ccSolar/s_runs1.sh b/offlineRT/c_cluster_solar_ipa3d_cg/ccSolar/s_runs1.sh
new file mode 100755
index 0000000000000000000000000000000000000000..0ea4ef71e1133385902366944ae6f0c1548ae4bf
--- /dev/null
+++ b/offlineRT/c_cluster_solar_ipa3d_cg/ccSolar/s_runs1.sh
@@ -0,0 +1,31 @@
+#!/bin/bash
+#SBATCH --partition=compute
+#SBATCH --account=bb1135
+#SBATCH --nodes=1
+###SBATCH --mem=800000
+#SBATCH --exclusive
+#SBATCH --output=libther.sh.%j.out
+#SBATCH --error=libther.sh.%j.err
+
+
+#---------------------------------
+. VARIABLES
+
+pathh=$(cd ../../ && pwd)
+ffname='ipa3d'
+ssource='solar'
+#timee="${time_array[0]}"
+#timee=$1
+num='01'
+#num=$2
+
+echo "Job started @ $(date)"
+for tt in "${time_array[@]}"
+do
+for dm in "${dom[@]}"
+do	
+/home/b/b381185/libRadtran/bin/uvspec < ${pathh}/c_cluster_${ssource}_${ffname}_cg/${ffname}_${tt}_${dm}_isim${num}/libsetup.inp &
+done
+done
+wait
+echo "Job finished @ $(date)"
diff --git a/offlineRT/c_cluster_solar_ipa3d_cg/ccSolar/step1_makeInpFiles.sh b/offlineRT/c_cluster_solar_ipa3d_cg/ccSolar/step1_makeInpFiles.sh
new file mode 100755
index 0000000000000000000000000000000000000000..5f12c2edd0453782a633c26f13c237ae76f4e516
--- /dev/null
+++ b/offlineRT/c_cluster_solar_ipa3d_cg/ccSolar/step1_makeInpFiles.sh
@@ -0,0 +1,194 @@
+#!/usr/bin/env bash
+
+. VARIABLES
+
+pathh=$(cd ../../../ && pwd)
+ffname='ipa3d'
+
+echo
+echo "Shell script sucessfully running!"
+echo
+
+echo
+echo "BUILD FOLDER NAMES AND INPUT FILES"
+echo
+
+#First go out of run folder:
+cd ..
+
+SCRIPTDIR=$(pwd)
+
+for it in "${time_array[@]}"
+do
+    echo 'time = ' $it
+
+i=0
+for dm in "${dom[@]}"
+do
+echo 'dom = ' $dm
+if [ $it == 05T1000 ]
+then
+	echo 'sza = ' "${sza_05T1000[$i]}"
+	sza="${sza_05T1000[$i]}"
+elif [ $it == 05T1030 ]
+then
+	echo 'sza = ' "${sza_05T1030[$i]}"
+        sza="${sza_05T1030[$i]}"
+elif [ $it == 05T1100 ]
+then
+	echo 'sza = ' "${sza_05T1100[$i]}"
+        sza="${sza_05T1100[$i]}"
+elif [ $it == 05T1130 ]
+then
+        echo 'sza = ' "${sza_05T1130[$i]}"
+        sza="${sza_05T1130[$i]}"
+elif [ $it == 05T1200 ]
+then
+        echo 'sza = ' "${sza_05T1200[$i]}"
+        sza="${sza_05T1200[$i]}"
+elif [ $it == 05T1230 ]
+then
+        echo 'sza = ' "${sza_05T1230[$i]}"
+        sza="${sza_05T1230[$i]}"
+elif [ $it == 05T1300 ]
+then
+        echo 'sza = ' "${sza_05T1300[$i]}"
+        sza="${sza_05T1300[$i]}"
+elif [ $it == 05T1330 ]
+then
+        echo 'sza = ' "${sza_05T1330[$i]}"
+        sza="${sza_05T1330[$i]}"	
+else
+	echo 'sza = ' "${sza_05T1400[$i]}"
+        sza="${sza_05T1400[$i]}"	
+fi
+i=$i+1
+
+    CLOUD_WC_FILE_INP=''$pathh'/wc3d_cg_'$dm'_202201'$it'33Z.dat'
+    CLOUD_IC_FILE_INP=''$pathh'/ic3d_cg_'$dm'_202201'$it'33Z.dat'
+    ZVALUES="$(head -n2 ${CLOUD_WC_FILE_INP} | tail -n1 | cut -d " " -f 3-)"
+
+      for isim in "${isim_array[@]}"
+      do
+      echo
+      echo "isim = " $isim
+      if [ $isim == 01 ]
+      then
+         ice_p='fu'
+         ice_h='#ic_habit rough-aggregate'
+      elif [ $isim == 02 ]
+      then
+         ice_p='baum_v36'
+         ice_h='ic_habit ghm'
+      elif [ $isim == 03 ]
+      then
+         ice_p='baum_v36'
+         ice_h='ic_habit solid-column'
+      else
+         ice_p='baum_v36'
+         ice_h='ic_habit rough-aggregate'
+      fi 	 
+      # Create libradtran output file name:
+      OUT_FILE=$ffname'_'$it'_'$dm'_isim'$isim'.out'
+
+      echo 'OUT_FILE = ' $OUT_FILE
+
+      FOLDER_NAME=$ffname'_'$it'_'$dm'_isim'$isim
+
+      mkdir $FOLDER_NAME
+
+      cd $FOLDER_NAME
+
+      echo 'Entering folder name = ' $FOLDER_NAME
+      # Create output folder for results:
+      mkdir output
+
+INP_FILE_NAME='libsetup.inp'
+LIBRAD="/home/b/b381185/libRadtran"
+
+############################
+# CREATE INPUT FILE HERE !!!
+cat > $INP_FILE_NAME << EOF
+data_files_path $LIBRAD/data/
+atmosphere_file ${pathh}/atmosphere_mean_${dm}_202201${it}33Z.dat
+
+albedo 0.07
+source solar
+sza ${sza}
+#phi ${phi}
+mol_abs_param Fu
+wavelength_index 1  7
+output_process sum
+
+# gas profiles
+#mixing_ratio CO2 348
+#mixing_ratio O2 209460
+#mixing_ratio CH4 1.650 
+#mixing_ratio N2O 0.396
+
+mixing_ratio F11 0.0
+mixing_ratio F12 0.0
+mixing_ratio F22 0.0
+mixing_ratio NO2 0.0
+
+mol_modify O4 0.0 DU
+mol_modify BRO 0.0 DU
+mol_modify OCLO 0.0 DU
+mol_modify HCHO 0.0 DU
+mol_modify SO2 0.0 DU
+mol_modify CO 0.0 DU
+mol_modify N2 0.0 DU
+
+#surface
+albedo_library IGBP
+brdf_rpv_type 17
+
+rte_solver twomaxrnd
+ipa_3d
+
+heating_rate layer_fd
+mc_backward_output heat K_per_day
+
+mc_sample_grid 41 33 1.9 3.3
+
+mc_basename $SCRIPTDIR/$FOLDER_NAME/output/$OUT_FILE
+
+wc_properties hu interpolate
+ic_properties $ice_p interpolate
+$ice_h
+
+wc_file 3D $CLOUD_WC_FILE_INP
+ic_file 3D $CLOUD_IC_FILE_INP
+
+quiet
+EOF
+### END OF INPUT FILE ###########
+#################################
+cat >ther_HR.sh<<EOF
+#! /bin/bash
+
+#SBATCH --account=bb1135
+#SBATCH --job-name=mystic_dom01.run
+#SBATCH --partition=shared
+#SBATCH --nodes=1
+#SBATCH --output=libther.sh.%j.out
+#SBATCH --error=libther.sh.%j.err
+#SBATCH --exclusive
+
+cd $(pwd)
+echo "Job started @ \$(date)"
+$LIBRAD/bin/uvspec < $INP_FILE_NAME
+echo "Job finished @ \$(date)"
+EOF
+
+        #Leave mysti/mcipa folder:
+        cd ..
+
+        done #over isim
+
+done # over dom
+done # over itime
+
+echo
+echo 
+echo 'The END'
diff --git a/offlineRT/c_cluster_solar_ipa3d_cg/ccSolar/submit_runs.sh b/offlineRT/c_cluster_solar_ipa3d_cg/ccSolar/submit_runs.sh
new file mode 100755
index 0000000000000000000000000000000000000000..21637528ea5ef932d16c174eb55cf819186a3041
--- /dev/null
+++ b/offlineRT/c_cluster_solar_ipa3d_cg/ccSolar/submit_runs.sh
@@ -0,0 +1,15 @@
+#!/usr/bin/env bash
+
+#-------------------------------------------
+. VARIABLES
+
+for tt in "${time_array[@]}"
+do
+for ss in "${isim_array[@]}"
+do
+   echo "submitting runs for = " $tt'_'$ss
+
+   sbatch s_runs1.sh $tt $ss
+
+done 
+done
diff --git a/offlineRT/c_cluster_solar_ipa3d_cg/find_missing_simulations.sh b/offlineRT/c_cluster_solar_ipa3d_cg/find_missing_simulations.sh
new file mode 100755
index 0000000000000000000000000000000000000000..086f35bc2fe65d973dc41811af4484c941f260d9
--- /dev/null
+++ b/offlineRT/c_cluster_solar_ipa3d_cg/find_missing_simulations.sh
@@ -0,0 +1,39 @@
+#!/usr/bin/env bash
+
+export time_array=("05T1000" "05T1030" "05T1100" "05T1130" "05T1200" "05T1230" "05T1300" "05T1330" "05T1400")
+
+export dom=("1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12" "13" "14" "15" "16" "17" "18" "19" "20" "21" "22" "23" "24" "25" "26" "27" "28" "29" "30" "31" "32" "33" "34" "35" "36")
+
+export isim_array=("01")
+
+pathh=$(pwd)
+#----------------------------------------------------------------------------------------
+ffname1='ipa3d'
+
+for it in "${time_array[@]}"
+do
+    echo 'time = ' $it
+
+for dm in "${dom[@]}"
+do
+echo 'dom = ' $dm
+
+for isim in "${isim_array[@]}"
+do
+
+if [ -n "$(ls -A $ffname1'_'$it'_'$dm'_isim'$isim/output 2>/dev/null)" ]
+then
+  echo "file exist (${ffname1}_'${it}'_'${dm}'_isim'${isim}'.out.abs.spc')"
+else
+  echo "file does not exist (${ffname1}_'${it}'_'${dm}'_isim'${isim}'.out.abs.spc')"
+  #count=(($i + 1))
+  # let's put these into a file for rerun
+  echo "/home/b/b381185/libRadtran/bin/uvspec < ${pathh}/${ffname1}_${it}_${dm}_isim${isim}/libsetup.inp &" >> submit_ipa3d.sh
+
+fi
+
+done
+done
+done
+
+#-----------------------
diff --git a/offlineRT/c_cluster_solar_ipa3d_dl/add_to_submit_file.txt b/offlineRT/c_cluster_solar_ipa3d_dl/add_to_submit_file.txt
new file mode 100644
index 0000000000000000000000000000000000000000..19439a7678e164092aacfea016d57e4018e80baf
--- /dev/null
+++ b/offlineRT/c_cluster_solar_ipa3d_dl/add_to_submit_file.txt
@@ -0,0 +1,12 @@
+#!/bin/bash
+#SBATCH --partition=compute
+#SBATCH --account=bb1135
+#SBATCH --nodes=1
+#SBATCH --mem=800000
+#SBATCH --exclusive
+#SBATCH --output=libther.sh.%j.out
+#SBATCH --error=libther.sh.%j.err
+#---------------------------------
+
+
+wait
diff --git a/offlineRT/c_cluster_solar_ipa3d_dl/ccSolar/s_runs1.sh b/offlineRT/c_cluster_solar_ipa3d_dl/ccSolar/s_runs1.sh
new file mode 100755
index 0000000000000000000000000000000000000000..db6146871e5cb6b3d9632ed2ee04c499e5878dfe
--- /dev/null
+++ b/offlineRT/c_cluster_solar_ipa3d_dl/ccSolar/s_runs1.sh
@@ -0,0 +1,31 @@
+#!/bin/bash
+#SBATCH --partition=compute
+#SBATCH --account=bb1135
+#SBATCH --nodes=1
+###SBATCH --mem=800000
+#SBATCH --exclusive
+#SBATCH --output=libther.sh.%j.out
+#SBATCH --error=libther.sh.%j.err
+
+
+#---------------------------------
+. VARIABLES
+
+pathh=$(cd ../../ && pwd)
+ffname='ipa3d'
+ssource='solar'
+#timee="${time_array[0]}"
+#timee=$1
+num='01'
+#num=$2
+
+echo "Job started @ $(date)"
+for tt in "${time_array[@]}"
+do
+for dm in "${dom[@]}"
+do	
+/home/b/b381185/libRadtran/bin/uvspec < ${pathh}/c_cluster_${ssource}_${ffname}_dl/${ffname}_${tt}_${dm}_isim${num}/libsetup.inp &
+done
+done
+wait
+echo "Job finished @ $(date)"
diff --git a/offlineRT/c_cluster_solar_ipa3d_dl/ccSolar/step1_makeInpFiles.sh b/offlineRT/c_cluster_solar_ipa3d_dl/ccSolar/step1_makeInpFiles.sh
new file mode 100755
index 0000000000000000000000000000000000000000..2d32130b7bb99e2b9878714d0b612fa97204685e
--- /dev/null
+++ b/offlineRT/c_cluster_solar_ipa3d_dl/ccSolar/step1_makeInpFiles.sh
@@ -0,0 +1,194 @@
+#!/usr/bin/env bash
+
+. VARIABLES
+
+pathh=$(cd ../../../ && pwd)
+ffname='ipa3d'
+
+echo
+echo "Shell script sucessfully running!"
+echo
+
+echo
+echo "BUILD FOLDER NAMES AND INPUT FILES"
+echo
+
+#First go out of run folder:
+cd ..
+
+SCRIPTDIR=$(pwd)
+
+for it in "${time_array[@]}"
+do
+    echo 'time = ' $it
+
+i=0
+for dm in "${dom[@]}"
+do
+echo 'dom = ' $dm
+if [ $it == 05T1000 ]
+then
+	echo 'sza = ' "${sza_05T1000[$i]}"
+	sza="${sza_05T1000[$i]}"
+elif [ $it == 05T1030 ]
+then
+	echo 'sza = ' "${sza_05T1030[$i]}"
+        sza="${sza_05T1030[$i]}"
+elif [ $it == 05T1100 ]
+then
+	echo 'sza = ' "${sza_05T1100[$i]}"
+        sza="${sza_05T1100[$i]}"
+elif [ $it == 05T1130 ]
+then
+        echo 'sza = ' "${sza_05T1130[$i]}"
+        sza="${sza_05T1130[$i]}"
+elif [ $it == 05T1200 ]
+then
+        echo 'sza = ' "${sza_05T1200[$i]}"
+        sza="${sza_05T1200[$i]}"
+elif [ $it == 05T1230 ]
+then
+        echo 'sza = ' "${sza_05T1230[$i]}"
+        sza="${sza_05T1230[$i]}"
+elif [ $it == 05T1300 ]
+then
+        echo 'sza = ' "${sza_05T1300[$i]}"
+        sza="${sza_05T1300[$i]}"
+elif [ $it == 05T1330 ]
+then
+        echo 'sza = ' "${sza_05T1330[$i]}"
+        sza="${sza_05T1330[$i]}"	
+else
+	echo 'sza = ' "${sza_05T1400[$i]}"
+        sza="${sza_05T1400[$i]}"	
+fi
+i=$i+1
+
+    CLOUD_WC_FILE_INP=''$pathh'/wc3d_dl_'$dm'_202201'$it'33Z.dat'
+    CLOUD_IC_FILE_INP=''$pathh'/ic3d_dl_'$dm'_202201'$it'33Z.dat'
+    ZVALUES="$(head -n2 ${CLOUD_WC_FILE_INP} | tail -n1 | cut -d " " -f 3-)"
+
+      for isim in "${isim_array[@]}"
+      do
+      echo
+      echo "isim = " $isim
+      if [ $isim == 01 ]
+      then
+         ice_p='fu'
+         ice_h='#ic_habit rough-aggregate'
+      elif [ $isim == 02 ]
+      then
+         ice_p='baum_v36'
+         ice_h='ic_habit ghm'
+      elif [ $isim == 03 ]
+      then
+         ice_p='baum_v36'
+         ice_h='ic_habit solid-column'
+      else
+         ice_p='baum_v36'
+         ice_h='ic_habit rough-aggregate'
+      fi 	 
+      # Create libradtran output file name:
+      OUT_FILE=$ffname'_'$it'_'$dm'_isim'$isim'.out'
+
+      echo 'OUT_FILE = ' $OUT_FILE
+
+      FOLDER_NAME=$ffname'_'$it'_'$dm'_isim'$isim
+
+      mkdir $FOLDER_NAME
+
+      cd $FOLDER_NAME
+
+      echo 'Entering folder name = ' $FOLDER_NAME
+      # Create output folder for results:
+      mkdir output
+
+INP_FILE_NAME='libsetup.inp'
+LIBRAD="/home/b/b381185/libRadtran"
+
+############################
+# CREATE INPUT FILE HERE !!!
+cat > $INP_FILE_NAME << EOF
+data_files_path $LIBRAD/data/
+atmosphere_file ${pathh}/atmosphere_mean_${dm}_202201${it}33Z.dat
+
+albedo 0.07
+source solar
+sza ${sza}
+#phi ${phi}
+mol_abs_param Fu
+wavelength_index 1  7
+output_process sum
+
+# gas profiles
+#mixing_ratio CO2 348
+#mixing_ratio O2 209460
+#mixing_ratio CH4 1.650 
+#mixing_ratio N2O 0.396
+
+mixing_ratio F11 0.0
+mixing_ratio F12 0.0
+mixing_ratio F22 0.0
+mixing_ratio NO2 0.0
+
+mol_modify O4 0.0 DU
+mol_modify BRO 0.0 DU
+mol_modify OCLO 0.0 DU
+mol_modify HCHO 0.0 DU
+mol_modify SO2 0.0 DU
+mol_modify CO 0.0 DU
+mol_modify N2 0.0 DU
+
+#surface
+albedo_library IGBP
+brdf_rpv_type 17
+
+rte_solver rodents
+ipa_3d
+
+heating_rate layer_fd
+mc_backward_output heat K_per_day
+
+mc_sample_grid 41 33 1.9 3.3
+
+mc_basename $SCRIPTDIR/$FOLDER_NAME/output/$OUT_FILE
+
+wc_properties hu interpolate
+ic_properties $ice_p interpolate
+$ice_h
+
+wc_file 3D $CLOUD_WC_FILE_INP
+ic_file 3D $CLOUD_IC_FILE_INP
+
+quiet
+EOF
+### END OF INPUT FILE ###########
+#################################
+cat >ther_HR.sh<<EOF
+#! /bin/bash
+
+#SBATCH --account=bb1135
+#SBATCH --job-name=mystic_dom01.run
+#SBATCH --partition=shared
+#SBATCH --nodes=1
+#SBATCH --output=libther.sh.%j.out
+#SBATCH --error=libther.sh.%j.err
+#SBATCH --exclusive
+
+cd $(pwd)
+echo "Job started @ \$(date)"
+$LIBRAD/bin/uvspec < $INP_FILE_NAME
+echo "Job finished @ \$(date)"
+EOF
+
+        #Leave mysti/mcipa folder:
+        cd ..
+
+        done #over isim
+
+done # over dom
+done # over itime
+
+echo
+echo 
+echo 'The END'
diff --git a/offlineRT/c_cluster_solar_ipa3d_dl/ccSolar/submit_runs.sh b/offlineRT/c_cluster_solar_ipa3d_dl/ccSolar/submit_runs.sh
new file mode 100755
index 0000000000000000000000000000000000000000..21637528ea5ef932d16c174eb55cf819186a3041
--- /dev/null
+++ b/offlineRT/c_cluster_solar_ipa3d_dl/ccSolar/submit_runs.sh
@@ -0,0 +1,15 @@
+#!/usr/bin/env bash
+
+#-------------------------------------------
+. VARIABLES
+
+for tt in "${time_array[@]}"
+do
+for ss in "${isim_array[@]}"
+do
+   echo "submitting runs for = " $tt'_'$ss
+
+   sbatch s_runs1.sh $tt $ss
+
+done 
+done
diff --git a/offlineRT/c_cluster_solar_ipa3d_dl/find_missing_simulations.sh b/offlineRT/c_cluster_solar_ipa3d_dl/find_missing_simulations.sh
new file mode 100755
index 0000000000000000000000000000000000000000..086f35bc2fe65d973dc41811af4484c941f260d9
--- /dev/null
+++ b/offlineRT/c_cluster_solar_ipa3d_dl/find_missing_simulations.sh
@@ -0,0 +1,39 @@
+#!/usr/bin/env bash
+
+export time_array=("05T1000" "05T1030" "05T1100" "05T1130" "05T1200" "05T1230" "05T1300" "05T1330" "05T1400")
+
+export dom=("1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12" "13" "14" "15" "16" "17" "18" "19" "20" "21" "22" "23" "24" "25" "26" "27" "28" "29" "30" "31" "32" "33" "34" "35" "36")
+
+export isim_array=("01")
+
+pathh=$(pwd)
+#----------------------------------------------------------------------------------------
+ffname1='ipa3d'
+
+for it in "${time_array[@]}"
+do
+    echo 'time = ' $it
+
+for dm in "${dom[@]}"
+do
+echo 'dom = ' $dm
+
+for isim in "${isim_array[@]}"
+do
+
+if [ -n "$(ls -A $ffname1'_'$it'_'$dm'_isim'$isim/output 2>/dev/null)" ]
+then
+  echo "file exist (${ffname1}_'${it}'_'${dm}'_isim'${isim}'.out.abs.spc')"
+else
+  echo "file does not exist (${ffname1}_'${it}'_'${dm}'_isim'${isim}'.out.abs.spc')"
+  #count=(($i + 1))
+  # let's put these into a file for rerun
+  echo "/home/b/b381185/libRadtran/bin/uvspec < ${pathh}/${ffname1}_${it}_${dm}_isim${isim}/libsetup.inp &" >> submit_ipa3d.sh
+
+fi
+
+done
+done
+done
+
+#-----------------------
diff --git a/offlineRT/c_cluster_solar_mystic/add_to_submit_file.txt b/offlineRT/c_cluster_solar_mystic/add_to_submit_file.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6aa3cba2250c5ec60bfca8db6b889e3d1db067fb
--- /dev/null
+++ b/offlineRT/c_cluster_solar_mystic/add_to_submit_file.txt
@@ -0,0 +1,12 @@
+#!/bin/bash
+#SBATCH --partition=interactive
+#SBATCH --account=bb1135
+#SBATCH --nodes=1
+#SBATCH --mem=256000
+#SBATCH --exclusive
+#SBATCH --output=libther.sh.%j.out
+#SBATCH --error=libther.sh.%j.err
+#---------------------------------
+
+
+wait
diff --git a/offlineRT/c_cluster_solar_mystic/ccSolar/s_runs1.sh b/offlineRT/c_cluster_solar_mystic/ccSolar/s_runs1.sh
new file mode 100755
index 0000000000000000000000000000000000000000..3a23be714f31e03bf04d3a9aa7ae3451f32c73e4
--- /dev/null
+++ b/offlineRT/c_cluster_solar_mystic/ccSolar/s_runs1.sh
@@ -0,0 +1,31 @@
+#!/bin/bash
+#SBATCH --partition=compute
+#SBATCH --account=bb1135
+#SBATCH --nodes=1
+###SBATCH --mem=256000
+#SBATCH --exclusive
+#SBATCH --output=libther.sh.%j.out
+#SBATCH --error=libther.sh.%j.err
+
+
+#---------------------------------
+. VARIABLES
+
+pathh=$(cd ../../ && pwd)
+ffname1='mystic'
+ffname2='mysti'
+ssource='solar'
+timee=$1
+num=$2
+
+echo "Job started @ $(date)"
+
+for dm in "${dom[@]}"
+do
+
+/home/b/b381185/libRadtran/bin/uvspec < ${pathh}/c_cluster_${ssource}_${ffname1}/${ffname2}_${timee}_${dm}_isim${num}/libsetup.inp &
+
+done
+wait
+
+echo "Job finished @ $(date)"
diff --git a/offlineRT/c_cluster_solar_mystic/ccSolar/s_runs2.sh b/offlineRT/c_cluster_solar_mystic/ccSolar/s_runs2.sh
new file mode 100755
index 0000000000000000000000000000000000000000..0811e955f1043f51013010cd4c44bb35adf4e994
--- /dev/null
+++ b/offlineRT/c_cluster_solar_mystic/ccSolar/s_runs2.sh
@@ -0,0 +1,31 @@
+#!/bin/bash
+#SBATCH --partition=compute
+#SBATCH --account=bb1135
+#SBATCH --nodes=1
+###SBATCH --mem=256000
+#SBATCH --exclusive
+#SBATCH --output=libther.sh.%j.out
+#SBATCH --error=libther.sh.%j.err
+
+
+#---------------------------------
+. VARIABLES
+
+pathh=$(cd ../../ && pwd)
+ffname1='mystic'
+ffname2='mcipa'
+ssource='solar'
+timee=$1
+num=$2
+
+echo "Job started @ $(date)"
+
+for dm in "${dom[@]}"
+do
+
+/home/b/b381185/libRadtran/bin/uvspec < ${pathh}/c_cluster_${ssource}_${ffname1}/${ffname2}_${timee}_${dm}_isim${num}/libsetup.inp &
+
+done
+wait
+
+echo "Job finished @ $(date)"
diff --git a/offlineRT/c_cluster_solar_mystic/ccSolar/step1_makeInpFiles.sh b/offlineRT/c_cluster_solar_mystic/ccSolar/step1_makeInpFiles.sh
new file mode 100755
index 0000000000000000000000000000000000000000..e3c1ee8f576cd71b315bec777b9dbc57cf6f34c9
--- /dev/null
+++ b/offlineRT/c_cluster_solar_mystic/ccSolar/step1_makeInpFiles.sh
@@ -0,0 +1,200 @@
+#!/usr/bin/env bash
+
+. VARIABLES
+
+IDENT=$1
+
+pathh=$(cd ../../../ && pwd)
+ffname='mysti'
+
+if [ "$IDENT" == "mcipa" ]
+then
+    MCIPA="mc_ipa"
+    ffname='mcipa'
+fi
+
+echo
+echo "Shell script sucessfully running!"
+echo
+
+echo
+echo "BUILD FOLDER NAMES AND INPUT FILES"
+echo
+
+#First go out of run folder:
+cd ..
+
+SCRIPTDIR=$(pwd)
+
+for it in "${time_array[@]}"
+do
+    echo 'time = ' $it
+
+i=0
+for dm in "${dom[@]}"
+do
+echo 'dom = ' $dm
+if [ $it == 05T1000 ]
+then
+	echo 'sza = ' "${sza_05T1000[$i]}"
+	sza="${sza_05T1000[$i]}"
+elif [ $it == 05T1030 ]
+then
+	echo 'sza = ' "${sza_05T1030[$i]}"
+        sza="${sza_05T1030[$i]}"
+elif [ $it == 05T1100 ]
+then
+	echo 'sza = ' "${sza_05T1100[$i]}"
+        sza="${sza_05T1100[$i]}"
+elif [ $it == 05T1130 ]
+then
+        echo 'sza = ' "${sza_05T1130[$i]}"
+        sza="${sza_05T1130[$i]}"
+elif [ $it == 05T1200 ]
+then
+        echo 'sza = ' "${sza_05T1200[$i]}"
+        sza="${sza_05T1200[$i]}"
+elif [ $it == 05T1230 ]
+then
+        echo 'sza = ' "${sza_05T1230[$i]}"
+        sza="${sza_05T1230[$i]}"
+elif [ $it == 05T1300 ]
+then
+        echo 'sza = ' "${sza_05T1300[$i]}"
+        sza="${sza_05T1300[$i]}"
+elif [ $it == 05T1330 ]
+then
+        echo 'sza = ' "${sza_05T1330[$i]}"
+        sza="${sza_05T1330[$i]}"	
+else
+	echo 'sza = ' "${sza_05T1400[$i]}"
+        sza="${sza_05T1400[$i]}"	
+fi
+i=$i+1
+
+    CLOUD_WC_FILE_INP=''$pathh'/wc3d_'$dm'_202201'$it'33Z.dat'
+    CLOUD_IC_FILE_INP=''$pathh'/ic3d_'$dm'_202201'$it'33Z.dat'
+    ZVALUES="$(head -n2 ${CLOUD_WC_FILE_INP} | tail -n1 | cut -d " " -f 3-)"
+
+      for isim in "${isim_array[@]}"
+      do
+      echo
+      echo "isim = " $isim
+      #if [ $isim == 01 ]
+      #then
+      #   ice_p='fu'
+      #   ice_h='#ic_habit rough-aggregate'
+      #elif [ $isim == 02 ]
+      #then
+      #   ice_p='baum_v36'
+      #   ice_h='ic_habit ghm'
+      #elif [ $isim == 03 ]
+      #then
+      #   ice_p='baum_v36'
+      #   ice_h='ic_habit solid-column'
+      #else
+      #   ice_p='baum_v36'
+      #   ice_h='ic_habit rough-aggregate'
+      #fi 	 
+      # Create libradtran output file name:
+      OUT_FILE=$ffname'_'$it'_'$dm'_isim'$isim'.out'
+
+      echo 'OUT_FILE = ' $OUT_FILE
+
+      FOLDER_NAME=$ffname'_'$it'_'$dm'_isim'$isim
+
+      mkdir $FOLDER_NAME
+
+      cd $FOLDER_NAME
+
+      echo 'Entering folder name = ' $FOLDER_NAME
+      # Create output folder for results:
+      mkdir output
+
+INP_FILE_NAME='libsetup.inp'
+LIBRAD="/home/b/b381185/libRadtran"
+
+############################
+# CREATE INPUT FILE HERE !!!
+cat > $INP_FILE_NAME << EOF
+data_files_path $LIBRAD/data/
+atmosphere_file ${pathh}/atmosphere_mean_${dm}_202201${it}33Z.dat
+
+albedo 0.07
+source solar
+sza ${sza}
+#phi ${phi}
+mol_abs_param Fu
+wavelength_index 1  7
+output_process sum
+
+# gas profiles
+#mixing_ratio CO2 348
+#mixing_ratio O2 209460
+#mixing_ratio CH4 1.650 
+#mixing_ratio N2O 0.396
+
+mixing_ratio F11 0.0
+mixing_ratio F12 0.0
+mixing_ratio F22 0.0
+mixing_ratio NO2 0.0
+
+mol_modify O4 0.0 DU
+mol_modify BRO 0.0 DU
+mol_modify OCLO 0.0 DU
+mol_modify HCHO 0.0 DU
+mol_modify SO2 0.0 DU
+mol_modify CO 0.0 DU
+mol_modify N2 0.0 DU
+
+#surface
+albedo_library IGBP
+brdf_rpv_type 17
+
+rte_solver mystic
+$MCIPA
+
+mc_forward_output heating K_per_day
+mc_photons 72500000
+
+mc_sample_grid 420 343 0.227 0.394
+
+mc_basename $SCRIPTDIR/$FOLDER_NAME/output/$OUT_FILE
+
+wc_properties hu interpolate
+ic_properties fu interpolate
+
+wc_file 3D $CLOUD_WC_FILE_INP
+ic_file 3D $CLOUD_IC_FILE_INP
+
+EOF
+### END OF INPUT FILE ###########
+#################################
+cat >ther_HR.sh<<EOF
+#! /bin/bash
+
+#SBATCH --account=bb1135
+#SBATCH --job-name=mystic_dom01.run
+#SBATCH --partition=shared
+#SBATCH --nodes=1
+#SBATCH --output=libther.sh.%j.out
+#SBATCH --error=libther.sh.%j.err
+#SBATCH --exclusive
+
+cd $(pwd)
+echo "Job started @ \$(date)"
+$LIBRAD/bin/uvspec < $INP_FILE_NAME
+echo "Job finished @ \$(date)"
+EOF
+
+        #Leave mysti/mcipa folder:
+        cd ..
+
+        done #over isim
+
+done # over dom
+done # over itime
+
+echo
+echo 
+echo 'The END'
diff --git a/offlineRT/c_cluster_solar_mystic/ccSolar/submit_runs.sh b/offlineRT/c_cluster_solar_mystic/ccSolar/submit_runs.sh
new file mode 100755
index 0000000000000000000000000000000000000000..6452e686c08d695e0bfeddcb9098a2ac42e840a3
--- /dev/null
+++ b/offlineRT/c_cluster_solar_mystic/ccSolar/submit_runs.sh
@@ -0,0 +1,16 @@
+#!/usr/bin/env bash
+
+#-------------------------------------------
+. VARIABLES
+
+for tt in "${time_array[@]}"
+do
+for ss in "${isim_array[@]}"
+do
+   echo "submitting runs for = " $tt'_'$ss
+
+   sbatch s_runs1.sh $tt $ss
+   sbatch s_runs2.sh $tt $ss
+
+done 
+done
diff --git a/offlineRT/c_cluster_solar_mystic/find_missing_simulations.sh b/offlineRT/c_cluster_solar_mystic/find_missing_simulations.sh
new file mode 100755
index 0000000000000000000000000000000000000000..2623b6927aa33b794041f597355896a6bd42db07
--- /dev/null
+++ b/offlineRT/c_cluster_solar_mystic/find_missing_simulations.sh
@@ -0,0 +1,68 @@
+#!/usr/bin/env bash
+
+export time_array=("05T1000" "05T1030" "05T1100" "05T1130" "05T1200" "05T1230" "05T1300" "05T1330" "05T1400")
+
+export dom=("1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12" "13" "14" "15" "16" "17" "18" "19" "20" "21" "22" "23" "24" "25" "26" "27" "28" "29" "30" "31" "32" "33" "34" "35" "36")
+
+export isim_array=("01" "02" "03" "04" "05" "06" "07" "08" "09" "10")
+
+pathh=$(pwd)
+#----------------------------------------------------------------------------------------
+ffname1='mysti'
+
+for it in "${time_array[@]}"
+do
+    echo 'time = ' $it
+
+for dm in "${dom[@]}"
+do
+echo 'dom = ' $dm
+
+for isim in "${isim_array[@]}"
+do
+
+if [ -n "$(ls -A $ffname1'_'$it'_'$dm'_isim'$isim/output 2>/dev/null)" ]
+then
+  echo "file exist (${ffname1}_'${it}'_'${dm}'_isim'${isim}'.out.abs.spc')"
+else
+  echo "file does not exist (${ffname1}_'${it}'_'${dm}'_isim'${isim}'.out.abs.spc')"
+  #count=(($i + 1))
+  # let's put these into a file for rerun
+  echo "/home/b/b381185/libRadtran/bin/uvspec < ${pathh}/${ffname1}_${it}_${dm}_isim${isim}/libsetup.inp &" >> submit_mysti.sh
+
+fi
+
+done
+done
+done
+
+#---------------------------- mcipa
+ffname2='mcipa'
+
+for it in "${time_array[@]}"
+do
+    echo 'time = ' $it
+
+for dm in "${dom[@]}"
+do
+echo 'dom = ' $dm
+
+for isim in "${isim_array[@]}"
+do
+
+if [ -n "$(ls -A $ffname2'_'$it'_'$dm'_isim'$isim/output 2>/dev/null)" ]
+then
+  echo "file exist (${ffname2}_'${it}'_'${dm}'_isim'${isim}'.out.abs.spc')"
+else
+  echo "file does not exist (${ffname2}_'${it}'_'${dm}'_isim'${isim}'.out.abs.spc')"
+  #count=(($i + 1))
+  # let's put these into a file for rerun
+  echo "/home/b/b381185/libRadtran/bin/uvspec < ${pathh}/${ffname2}_${it}_${dm}_isim${isim}/libsetup.inp &" >> submit_mcipa.sh
+
+fi
+
+done
+done
+done
+
+#-----------------------
diff --git a/offlineRT/c_cluster_thermal_ipa3d/add_to_submit_file.txt b/offlineRT/c_cluster_thermal_ipa3d/add_to_submit_file.txt
new file mode 100644
index 0000000000000000000000000000000000000000..19439a7678e164092aacfea016d57e4018e80baf
--- /dev/null
+++ b/offlineRT/c_cluster_thermal_ipa3d/add_to_submit_file.txt
@@ -0,0 +1,12 @@
+#!/bin/bash
+#SBATCH --partition=compute
+#SBATCH --account=bb1135
+#SBATCH --nodes=1
+#SBATCH --mem=800000
+#SBATCH --exclusive
+#SBATCH --output=libther.sh.%j.out
+#SBATCH --error=libther.sh.%j.err
+#---------------------------------
+
+
+wait
diff --git a/offlineRT/c_cluster_thermal_ipa3d/ccThermal/VARIABLES b/offlineRT/c_cluster_thermal_ipa3d/ccThermal/VARIABLES
new file mode 100644
index 0000000000000000000000000000000000000000..1373301af56f6a455508ba8eb1ba849c90b31f87
--- /dev/null
+++ b/offlineRT/c_cluster_thermal_ipa3d/ccThermal/VARIABLES
@@ -0,0 +1,11 @@
+export time_array=("05T1000" "05T1030" "05T1100" "05T1130" "05T1200" "05T1230" "05T1300" "05T1330" "05T1400") 
+
+export dom=("1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12" "13" "14" "15" "16" "17" "18" "19" "20" "21" "22" "23" "24" "25" "26" "27" "28" "29" "30" "31" "32" "33" "34" "35" "36")
+
+export dom1=("1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12")
+
+export dom2=("13" "14" "15" "16" "17" "18" "19" "20" "21" "22" "23" "24")
+
+export dom3=("25" "26" "27" "28" "29" "30" "31" "32" "33" "34" "35" "36")
+
+export isim_array=("01" "02" "03" "04")
diff --git a/offlineRT/c_cluster_thermal_ipa3d/ccThermal/s_runs1.sh b/offlineRT/c_cluster_thermal_ipa3d/ccThermal/s_runs1.sh
new file mode 100755
index 0000000000000000000000000000000000000000..09ff6d6846ada44dd8f3dc04dd98b80ca5ec9482
--- /dev/null
+++ b/offlineRT/c_cluster_thermal_ipa3d/ccThermal/s_runs1.sh
@@ -0,0 +1,26 @@
+#!/bin/bash
+#SBATCH --partition=compute
+#SBATCH --account=bb1135
+#SBATCH --nodes=1
+#SBATCH --mem=800000
+#SBATCH --exclusive
+#SBATCH --output=libther.sh.%j.out
+#SBATCH --error=libther.sh.%j.err
+
+
+#---------------------------------
+. VARIABLES
+
+pathh=$(cd ../../ && pwd)
+ffname='ipa3d'
+ssource='thermal'
+timee=$1
+num=$2
+
+echo "Job started @ $(date)"
+for dm in "${dom1[@]}"
+do	
+/home/b/b381185/libRadtran/bin/uvspec < ${pathh}/c_cluster_${ssource}_${ffname}/${ffname}_${timee}_${dm}_isim${num}/libsetup.inp &
+done
+wait
+echo "Job finished @ $(date)"
diff --git a/offlineRT/c_cluster_thermal_ipa3d/ccThermal/s_runs2.sh b/offlineRT/c_cluster_thermal_ipa3d/ccThermal/s_runs2.sh
new file mode 100755
index 0000000000000000000000000000000000000000..03c7b0c058cb3d9d46cb53e427a97ce91a95a70d
--- /dev/null
+++ b/offlineRT/c_cluster_thermal_ipa3d/ccThermal/s_runs2.sh
@@ -0,0 +1,26 @@
+#!/bin/bash
+#SBATCH --partition=compute
+#SBATCH --account=bb1135
+#SBATCH --nodes=1
+#SBATCH --mem=800000
+#SBATCH --exclusive
+#SBATCH --output=libther.sh.%j.out
+#SBATCH --error=libther.sh.%j.err
+
+
+#---------------------------------
+. VARIABLES
+
+pathh=$(cd ../../ && pwd)
+ffname='ipa3d'
+ssource='thermal'
+timee=$1
+num=$2
+
+echo "Job started @ $(date)"
+for dm in "${dom2[@]}"
+do	
+/home/b/b381185/libRadtran/bin/uvspec < ${pathh}/c_cluster_${ssource}_${ffname}/${ffname}_${timee}_${dm}_isim${num}/libsetup.inp &
+done
+wait
+echo "Job finished @ $(date)"
diff --git a/offlineRT/c_cluster_thermal_ipa3d/ccThermal/s_runs3.sh b/offlineRT/c_cluster_thermal_ipa3d/ccThermal/s_runs3.sh
new file mode 100755
index 0000000000000000000000000000000000000000..626a33f16354dd0ca2add666274f0fdeba508391
--- /dev/null
+++ b/offlineRT/c_cluster_thermal_ipa3d/ccThermal/s_runs3.sh
@@ -0,0 +1,26 @@
+#!/bin/bash
+#SBATCH --partition=compute
+#SBATCH --account=bb1135
+#SBATCH --nodes=1
+#SBATCH --mem=800000
+#SBATCH --exclusive
+#SBATCH --output=libther.sh.%j.out
+#SBATCH --error=libther.sh.%j.err
+
+
+#---------------------------------
+. VARIABLES
+
+pathh=$(cd ../../ && pwd)
+ffname='ipa3d'
+ssource='thermal'
+timee=$1
+num=$2
+
+echo "Job started @ $(date)"
+for dm in "${dom3[@]}"
+do	
+/home/b/b381185/libRadtran/bin/uvspec < ${pathh}/c_cluster_${ssource}_${ffname}/${ffname}_${timee}_${dm}_isim${num}/libsetup.inp &
+done
+wait
+echo "Job finished @ $(date)"
diff --git a/offlineRT/c_cluster_thermal_ipa3d/ccThermal/step1_makeInpFiles.sh b/offlineRT/c_cluster_thermal_ipa3d/ccThermal/step1_makeInpFiles.sh
new file mode 100755
index 0000000000000000000000000000000000000000..c19cfeb2720d44da60d7579e2fb604a7f955d8c8
--- /dev/null
+++ b/offlineRT/c_cluster_thermal_ipa3d/ccThermal/step1_makeInpFiles.sh
@@ -0,0 +1,154 @@
+#!/usr/bin/env bash
+
+. VARIABLES
+
+pathh=$(cd ../../../ && pwd)
+ffname='ipa3d'
+
+echo
+echo "Shell script sucessfully running!"
+echo
+
+echo
+echo "BUILD FOLDER NAMES AND INPUT FILES"
+echo
+
+#First go out of run folder:
+cd ..
+
+SCRIPTDIR=$(pwd)
+
+for it in "${time_array[@]}"
+do
+    echo 'time = ' $it
+
+for dm in "${dom[@]}"
+do
+echo 'dom = ' $dm
+
+    CLOUD_WC_FILE_INP=''$pathh'/wc3d_'$dm'_202201'$it'33Z.dat'
+    CLOUD_IC_FILE_INP=''$pathh'/ic3d_'$dm'_202201'$it'33Z.dat'
+    ZVALUES="$(head -n2 ${CLOUD_WC_FILE_INP} | tail -n1 | cut -d " " -f 3-)"
+
+      for isim in "${isim_array[@]}"
+      do
+      echo
+      echo "isim = " $isim
+      if [ $isim == 01 ]
+      then
+         ice_p='fu'
+         ice_h='#ic_habit rough-aggregate'
+      elif [ $isim == 02 ]
+      then
+         ice_p='baum_v36'
+         ice_h='ic_habit ghm'
+      elif [ $isim == 03 ]
+      then
+         ice_p='baum_v36'
+         ice_h='ic_habit solid-column'
+      else
+         ice_p='baum_v36'
+         ice_h='ic_habit rough-aggregate'
+      fi 	 
+      # Create libradtran output file name:
+      OUT_FILE=$ffname'_'$it'_'$dm'_isim'$isim'.out'
+
+      echo 'OUT_FILE = ' $OUT_FILE
+
+      FOLDER_NAME=$ffname'_'$it'_'$dm'_isim'$isim
+
+      mkdir $FOLDER_NAME
+
+      cd $FOLDER_NAME
+
+      echo 'Entering folder name = ' $FOLDER_NAME
+      # Create output folder for results:
+      mkdir output
+
+INP_FILE_NAME='libsetup.inp'
+LIBRAD="/home/b/b381185/libRadtran"
+
+############################
+# CREATE INPUT FILE HERE !!!
+cat > $INP_FILE_NAME << EOF
+data_files_path $LIBRAD/data/
+atmosphere_file ${pathh}/atmosphere_mean_${dm}_202201${it}33Z.dat
+
+albedo 0.009000000000000008
+source thermal  
+mol_abs_param Fu   
+wavelength_index 7  18   
+output_process sum   
+
+# gas profiles
+#mixing_ratio CO2 348
+#mixing_ratio O2 209460
+#mixing_ratio CH4 1.650 
+#mixing_ratio N2O 0.396
+
+mixing_ratio F11 0.0
+mixing_ratio F12 0.0
+mixing_ratio F22 0.0
+mixing_ratio NO2 0.0
+
+mol_modify O4 0.0 DU
+mol_modify BRO 0.0 DU
+mol_modify OCLO 0.0 DU
+mol_modify HCHO 0.0 DU
+mol_modify SO2 0.0 DU
+mol_modify CO 0.0 DU
+mol_modify N2 0.0 DU
+
+#surface
+albedo_library IGBP
+brdf_rpv_type 17
+
+rte_solver rodents
+ipa_3d
+
+heating_rate layer_fd
+mc_backward_output heat K_per_day
+
+mc_sample_grid 420 343 0.227 0.394
+
+mc_basename $SCRIPTDIR/$FOLDER_NAME/output/$OUT_FILE
+
+wc_properties hu interpolate
+ic_properties $ice_p interpolate
+$ice_h
+
+wc_file 3D $CLOUD_WC_FILE_INP
+ic_file 3D $CLOUD_IC_FILE_INP
+
+quiet
+EOF
+### END OF INPUT FILE ###########
+#################################
+cat >ther_HR.sh<<EOF
+#! /bin/bash
+
+#SBATCH --account=bb1135
+#SBATCH --job-name=mystic_dom01.run
+#SBATCH --partition=shared
+#SBATCH --nodes=1
+#SBATCH --output=libther.sh.%j.out
+#SBATCH --error=libther.sh.%j.err
+#SBATCH --exclusive
+
+cd $(pwd)
+echo "Job started @ \$(date)"
+$LIBRAD/bin/uvspec < $INP_FILE_NAME
+echo "Job finished @ \$(date)"
+EOF
+
+        #Leave mysti/mcipa folder:
+        cd ..
+
+        done #over isim
+
+done # over dom
+done # over itime
+
+echo
+echo 
+echo 'The END'
diff --git a/offlineRT/c_cluster_thermal_ipa3d/ccThermal/submit_runs.sh b/offlineRT/c_cluster_thermal_ipa3d/ccThermal/submit_runs.sh
new file mode 100755
index 0000000000000000000000000000000000000000..77a75ae5ddaa96c9139bf50ee6e6586c6dbc0a8a
--- /dev/null
+++ b/offlineRT/c_cluster_thermal_ipa3d/ccThermal/submit_runs.sh
@@ -0,0 +1,17 @@
+#!/usr/bin/env bash
+
+#-------------------------------------------
+. VARIABLES
+
+for tt in "${time_array[@]}"
+do
+for ss in "${isim_array[@]}"
+do
+   echo "submitting runs for = " $tt'_'$ss
+
+   sbatch s_runs1.sh $tt $ss
+   sbatch s_runs2.sh $tt $ss
+   sbatch s_runs3.sh $tt $ss
+
+done 
+done
diff --git a/offlineRT/c_cluster_thermal_ipa3d/find_missing_simulations.sh b/offlineRT/c_cluster_thermal_ipa3d/find_missing_simulations.sh
new file mode 100755
index 0000000000000000000000000000000000000000..e0ab46047a25e8b61045c504db6dfd7c74e94807
--- /dev/null
+++ b/offlineRT/c_cluster_thermal_ipa3d/find_missing_simulations.sh
@@ -0,0 +1,39 @@
+#!/usr/bin/env bash
+
+export time_array=("05T1000" "05T1030" "05T1100" "05T1130" "05T1200" "05T1230" "05T1300" "05T1330" "05T1400")
+
+export dom=("1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12" "13" "14" "15" "16" "17" "18" "19" "20" "21" "22" "23" "24" "25" "26" "27" "28" "29" "30" "31" "32" "33" "34" "35" "36")
+
+export isim_array=("01" "02" "03" "04")
+
+pathh=$(pwd)
+#----------------------------------------------------------------------------------------
+ffname1='ipa3d'
+
+for it in "${time_array[@]}"
+do
+    echo 'time = ' $it
+
+for dm in "${dom[@]}"
+do
+echo 'dom = ' $dm
+
+for isim in "${isim_array[@]}"
+do
+
+if [ -n "$(ls -A $ffname1'_'$it'_'$dm'_isim'$isim/output 2>/dev/null)" ]
+then
+  echo "file exist (${ffname1}_'${it}'_'${dm}'_isim'${isim}'.out.abs.spc')"
+else
+  echo "file does not exist (${ffname1}_'${it}'_'${dm}'_isim'${isim}'.out.abs.spc')"
+  #count=(($i + 1))
+  # let's put these into a file for rerun
+  echo "/home/b/b381185/libRadtran/bin/uvspec < ${pathh}/${ffname1}_${it}_${dm}_isim${isim}/libsetup.inp &" >> submit_ipa3d.sh
+
+fi
+
+done
+done
+done
+
+#-----------------------
diff --git a/offlineRT/c_cluster_thermal_ipa3d_cg/add_to_submit_file.txt b/offlineRT/c_cluster_thermal_ipa3d_cg/add_to_submit_file.txt
new file mode 100644
index 0000000000000000000000000000000000000000..19439a7678e164092aacfea016d57e4018e80baf
--- /dev/null
+++ b/offlineRT/c_cluster_thermal_ipa3d_cg/add_to_submit_file.txt
@@ -0,0 +1,12 @@
+#!/bin/bash
+#SBATCH --partition=compute
+#SBATCH --account=bb1135
+#SBATCH --nodes=1
+#SBATCH --mem=800000
+#SBATCH --exclusive
+#SBATCH --output=libther.sh.%j.out
+#SBATCH --error=libther.sh.%j.err
+#---------------------------------
+
+
+wait
diff --git a/offlineRT/c_cluster_thermal_ipa3d_cg/ccThermal/VARIABLES b/offlineRT/c_cluster_thermal_ipa3d_cg/ccThermal/VARIABLES
new file mode 100644
index 0000000000000000000000000000000000000000..fe5ae23a33f7e08e5bb3b28b4d89f5a0b2840476
--- /dev/null
+++ b/offlineRT/c_cluster_thermal_ipa3d_cg/ccThermal/VARIABLES
@@ -0,0 +1,9 @@
+export time_array=("05T1000" "05T1030" "05T1100" "05T1130" "05T1200" "05T1230" "05T1300" "05T1330" "05T1400") 
+
+export dom=("1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12" "13" "14" "15" "16" "17" "18" "19" "20" "21" "22" "23" "24" "25" "26" "27" "28" "29" "30" "31" "32" "33" "34" "35" "36")
+
+export dom1=("1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12" "13" "14" "15" "16" "17")
+
+export dom2=("18" "19" "20" "21" "22" "23" "24" "25" "26" "27" "28" "29" "30" "31" "32" "33" "34" "35" "36")
+
+export isim_array=("01") ##("02" "03" "04")
diff --git a/offlineRT/c_cluster_thermal_ipa3d_cg/ccThermal/s_runs1.sh b/offlineRT/c_cluster_thermal_ipa3d_cg/ccThermal/s_runs1.sh
new file mode 100755
index 0000000000000000000000000000000000000000..760f1d53357df75668e30123f499577720ae0c67
--- /dev/null
+++ b/offlineRT/c_cluster_thermal_ipa3d_cg/ccThermal/s_runs1.sh
@@ -0,0 +1,31 @@
+#!/bin/bash
+#SBATCH --partition=compute
+#SBATCH --account=bb1135
+#SBATCH --nodes=1
+###SBATCH --mem=800000
+#SBATCH --exclusive
+#SBATCH --output=libther.sh.%j.out
+#SBATCH --error=libther.sh.%j.err
+
+
+#---------------------------------
+. VARIABLES
+
+pathh=$(cd ../../ && pwd)
+ffname='ipa3d'
+ssource='thermal'
+#timee="${time_array[0]}"
+#timee=$1
+num='01'
+#num=$2
+
+echo "Job started @ $(date)"
+for tt in "${time_array[@]}"
+do
+for dm in "${dom[@]}"
+do	
+/home/b/b381185/libRadtran/bin/uvspec < ${pathh}/c_cluster_${ssource}_${ffname}_cg/${ffname}_${tt}_${dm}_isim${num}/libsetup.inp &
+done
+done
+wait
+echo "Job finished @ $(date)"
diff --git a/offlineRT/c_cluster_thermal_ipa3d_cg/ccThermal/step1_makeInpFiles.sh b/offlineRT/c_cluster_thermal_ipa3d_cg/ccThermal/step1_makeInpFiles.sh
new file mode 100755
index 0000000000000000000000000000000000000000..fcfb8001568a4ec0508ed3474ab0dd6cf12e0d67
--- /dev/null
+++ b/offlineRT/c_cluster_thermal_ipa3d_cg/ccThermal/step1_makeInpFiles.sh
@@ -0,0 +1,154 @@
+#!/usr/bin/env bash
+
+. VARIABLES
+
+pathh=$(cd ../../../ && pwd)
+ffname='ipa3d'
+
+echo
+echo "Shell script sucessfully running!"
+echo
+
+echo
+echo "BUILD FOLDER NAMES AND INPUT FILES"
+echo
+
+#First go out of run folder:
+cd ..
+
+SCRIPTDIR=$(pwd)
+
+for it in "${time_array[@]}"
+do
+    echo 'time = ' $it
+
+for dm in "${dom[@]}"
+do
+echo 'dom = ' $dm
+
+    CLOUD_WC_FILE_INP=''$pathh'/wc3d_cg_'$dm'_202201'$it'33Z.dat'
+    CLOUD_IC_FILE_INP=''$pathh'/ic3d_cg_'$dm'_202201'$it'33Z.dat'
+    ZVALUES="$(head -n2 ${CLOUD_WC_FILE_INP} | tail -n1 | cut -d " " -f 3-)"
+
+      for isim in "${isim_array[@]}"
+      do
+      echo
+      echo "isim = " $isim
+      if [ $isim == 01 ]
+      then
+         ice_p='fu'
+         ice_h='#ic_habit rough-aggregate'
+      elif [ $isim == 02 ]
+      then
+         ice_p='baum_v36'
+         ice_h='ic_habit ghm'
+      elif [ $isim == 03 ]
+      then
+         ice_p='baum_v36'
+         ice_h='ic_habit solid-column'
+      else
+         ice_p='baum_v36'
+         ice_h='ic_habit rough-aggregate'
+      fi 	 
+      # Create libradtran output file name:
+      OUT_FILE=$ffname'_'$it'_'$dm'_isim'$isim'.out'
+
+      echo 'OUT_FILE = ' $OUT_FILE
+
+      FOLDER_NAME=$ffname'_'$it'_'$dm'_isim'$isim
+
+      mkdir $FOLDER_NAME
+
+      cd $FOLDER_NAME
+
+      echo 'Entering folder name = ' $FOLDER_NAME
+      # Create output folder for results:
+      mkdir output
+
+INP_FILE_NAME='libsetup.inp'
+LIBRAD="/home/b/b381185/libRadtran"
+
+############################
+# CREATE INPUT FILE HERE !!!
+cat > $INP_FILE_NAME << EOF
+data_files_path $LIBRAD/data/
+atmosphere_file ${pathh}/atmosphere_mean_${dm}_202201${it}33Z.dat
+
+albedo 0.009000000000000008
+source thermal  
+mol_abs_param Fu   
+wavelength_index 7  18   
+output_process sum   
+
+# gas profiles
+#mixing_ratio CO2 348
+#mixing_ratio O2 209460
+#mixing_ratio CH4 1.650 
+#mixing_ratio N2O 0.396
+
+mixing_ratio F11 0.0
+mixing_ratio F12 0.0
+mixing_ratio F22 0.0
+mixing_ratio NO2 0.0
+
+mol_modify O4 0.0 DU
+mol_modify BRO 0.0 DU
+mol_modify OCLO 0.0 DU
+mol_modify HCHO 0.0 DU
+mol_modify SO2 0.0 DU
+mol_modify CO 0.0 DU
+mol_modify N2 0.0 DU
+
+#surface
+albedo_library IGBP
+brdf_rpv_type 17
+
+rte_solver twomaxrnd
+ipa_3d
+
+heating_rate layer_fd
+mc_backward_output heat K_per_day
+
+mc_sample_grid 41 33 1.9 3.3
+
+mc_basename $SCRIPTDIR/$FOLDER_NAME/output/$OUT_FILE
+
+wc_properties hu interpolate
+ic_properties $ice_p interpolate
+$ice_h
+
+wc_file 3D $CLOUD_WC_FILE_INP
+ic_file 3D $CLOUD_IC_FILE_INP
+
+quiet
+EOF
+### END OF INPUT FILE ###########
+#################################
+cat >ther_HR.sh<<EOF
+#! /bin/bash
+
+#SBATCH --account=bb1135
+#SBATCH --job-name=mystic_dom01.run
+#SBATCH --partition=shared
+#SBATCH --nodes=1
+#SBATCH --output=libther.sh.%j.out
+#SBATCH --error=libther.sh.%j.err
+#SBATCH --exclusive
+
+cd $(pwd)
+echo "Job started @ \$(date)"
+$LIBRAD/bin/uvspec < $INP_FILE_NAME
+echo "Job finished @ \$(date)"
+EOF
+
+        #Leave mysti/mcipa folder:
+        cd ..
+
+        done #over isim
+
+done # over dom
+done # over itime
+
+echo
+echo 
+echo 'The END'
diff --git a/offlineRT/c_cluster_thermal_ipa3d_cg/ccThermal/submit_runs.sh b/offlineRT/c_cluster_thermal_ipa3d_cg/ccThermal/submit_runs.sh
new file mode 100755
index 0000000000000000000000000000000000000000..21637528ea5ef932d16c174eb55cf819186a3041
--- /dev/null
+++ b/offlineRT/c_cluster_thermal_ipa3d_cg/ccThermal/submit_runs.sh
@@ -0,0 +1,15 @@
+#!/usr/bin/env bash
+
+#-------------------------------------------
+. VARIABLES
+
+for tt in "${time_array[@]}"
+do
+for ss in "${isim_array[@]}"
+do
+   echo "submitting runs for = " $tt'_'$ss
+
+   sbatch s_runs1.sh $tt $ss
+
+done 
+done
diff --git a/offlineRT/c_cluster_thermal_ipa3d_cg/find_missing_simulations.sh b/offlineRT/c_cluster_thermal_ipa3d_cg/find_missing_simulations.sh
new file mode 100755
index 0000000000000000000000000000000000000000..086f35bc2fe65d973dc41811af4484c941f260d9
--- /dev/null
+++ b/offlineRT/c_cluster_thermal_ipa3d_cg/find_missing_simulations.sh
@@ -0,0 +1,39 @@
+#!/usr/bin/env bash
+
+export time_array=("05T1000" "05T1030" "05T1100" "05T1130" "05T1200" "05T1230" "05T1300" "05T1330" "05T1400")
+
+export dom=("1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12" "13" "14" "15" "16" "17" "18" "19" "20" "21" "22" "23" "24" "25" "26" "27" "28" "29" "30" "31" "32" "33" "34" "35" "36")
+
+export isim_array=("01")
+
+pathh=$(pwd)
+#----------------------------------------------------------------------------------------
+ffname1='ipa3d'
+
+for it in "${time_array[@]}"
+do
+    echo 'time = ' $it
+
+for dm in "${dom[@]}"
+do
+echo 'dom = ' $dm
+
+for isim in "${isim_array[@]}"
+do
+
+if [ -n "$(ls -A $ffname1'_'$it'_'$dm'_isim'$isim/output 2>/dev/null)" ]
+then
+  echo "file exist (${ffname1}_'${it}'_'${dm}'_isim'${isim}'.out.abs.spc')"
+else
+  echo "file does not exist (${ffname1}_'${it}'_'${dm}'_isim'${isim}'.out.abs.spc')"
+  #count=(($i + 1))
+  # let's put these into a file for rerun
+  echo "/home/b/b381185/libRadtran/bin/uvspec < ${pathh}/${ffname1}_${it}_${dm}_isim${isim}/libsetup.inp &" >> submit_ipa3d.sh
+
+fi
+
+done
+done
+done
+
+#-----------------------
diff --git a/offlineRT/c_cluster_thermal_ipa3d_dl/add_to_submit_file.txt b/offlineRT/c_cluster_thermal_ipa3d_dl/add_to_submit_file.txt
new file mode 100644
index 0000000000000000000000000000000000000000..19439a7678e164092aacfea016d57e4018e80baf
--- /dev/null
+++ b/offlineRT/c_cluster_thermal_ipa3d_dl/add_to_submit_file.txt
@@ -0,0 +1,12 @@
+#!/bin/bash
+#SBATCH --partition=compute
+#SBATCH --account=bb1135
+#SBATCH --nodes=1
+#SBATCH --mem=800000
+#SBATCH --exclusive
+#SBATCH --output=libther.sh.%j.out
+#SBATCH --error=libther.sh.%j.err
+#---------------------------------
+
+
+wait
diff --git a/offlineRT/c_cluster_thermal_ipa3d_dl/ccThermal/VARIABLES b/offlineRT/c_cluster_thermal_ipa3d_dl/ccThermal/VARIABLES
new file mode 100644
index 0000000000000000000000000000000000000000..fe5ae23a33f7e08e5bb3b28b4d89f5a0b2840476
--- /dev/null
+++ b/offlineRT/c_cluster_thermal_ipa3d_dl/ccThermal/VARIABLES
@@ -0,0 +1,9 @@
+export time_array=("05T1000" "05T1030" "05T1100" "05T1130" "05T1200" "05T1230" "05T1300" "05T1330" "05T1400") 
+
+export dom=("1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12" "13" "14" "15" "16" "17" "18" "19" "20" "21" "22" "23" "24" "25" "26" "27" "28" "29" "30" "31" "32" "33" "34" "35" "36")
+
+export dom1=("1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12" "13" "14" "15" "16" "17")
+
+export dom2=("18" "19" "20" "21" "22" "23" "24" "25" "26" "27" "28" "29" "30" "31" "32" "33" "34" "35" "36")
+
+export isim_array=("01") ##("02" "03" "04")
diff --git a/offlineRT/c_cluster_thermal_ipa3d_dl/ccThermal/s_runs1.sh b/offlineRT/c_cluster_thermal_ipa3d_dl/ccThermal/s_runs1.sh
new file mode 100755
index 0000000000000000000000000000000000000000..dc2938352c573b17322773840d3c9f6e5a663339
--- /dev/null
+++ b/offlineRT/c_cluster_thermal_ipa3d_dl/ccThermal/s_runs1.sh
@@ -0,0 +1,31 @@
+#!/bin/bash
+#SBATCH --partition=compute
+#SBATCH --account=bb1135
+#SBATCH --nodes=1
+###SBATCH --mem=800000
+#SBATCH --exclusive
+#SBATCH --output=libther.sh.%j.out
+#SBATCH --error=libther.sh.%j.err
+
+
+#---------------------------------
+. VARIABLES
+
+pathh=$(cd ../../ && pwd)
+ffname='ipa3d'
+ssource='thermal'
+#timee="${time_array[0]}"
+#timee=$1
+num='01'
+#num=$2
+
+echo "Job started @ $(date)"
+for tt in "${time_array[@]}"
+do
+for dm in "${dom[@]}"
+do	
+/home/b/b381185/libRadtran/bin/uvspec < ${pathh}/c_cluster_${ssource}_${ffname}_dl/${ffname}_${tt}_${dm}_isim${num}/libsetup.inp &
+done
+done
+wait
+echo "Job finished @ $(date)"
diff --git a/offlineRT/c_cluster_thermal_ipa3d_dl/ccThermal/step1_makeInpFiles.sh b/offlineRT/c_cluster_thermal_ipa3d_dl/ccThermal/step1_makeInpFiles.sh
new file mode 100755
index 0000000000000000000000000000000000000000..5f8aa9483488d1fa882a8e1078fb6348ba14100e
--- /dev/null
+++ b/offlineRT/c_cluster_thermal_ipa3d_dl/ccThermal/step1_makeInpFiles.sh
@@ -0,0 +1,154 @@
+#!/usr/bin/env bash
+
+. VARIABLES
+
+pathh=$(cd ../../../ && pwd)
+ffname='ipa3d'
+
+echo
+echo "Shell script sucessfully running!"
+echo
+
+echo
+echo "BUILD FOLDER NAMES AND INPUT FILES"
+echo
+
+#First go out of run folder:
+cd ..
+
+SCRIPTDIR=$(pwd)
+
+for it in "${time_array[@]}"
+do
+    echo 'time = ' $it
+
+for dm in "${dom[@]}"
+do
+echo 'dom = ' $dm
+
+    CLOUD_WC_FILE_INP=''$pathh'/wc3d_dl_'$dm'_202201'$it'33Z.dat'
+    CLOUD_IC_FILE_INP=''$pathh'/ic3d_dl_'$dm'_202201'$it'33Z.dat'
+    ZVALUES="$(head -n2 ${CLOUD_WC_FILE_INP} | tail -n1 | cut -d " " -f 3-)"
+
+      for isim in "${isim_array[@]}"
+      do
+      echo
+      echo "isim = " $isim
+      if [ $isim == 01 ]
+      then
+         ice_p='fu'
+         ice_h='#ic_habit rough-aggregate'
+      elif [ $isim == 02 ]
+      then
+         ice_p='baum_v36'
+         ice_h='ic_habit ghm'
+      elif [ $isim == 03 ]
+      then
+         ice_p='baum_v36'
+         ice_h='ic_habit solid-column'
+      else
+         ice_p='baum_v36'
+         ice_h='ic_habit rough-aggregate'
+      fi 	 
+      # Create libradtran output file name:
+      OUT_FILE=$ffname'_'$it'_'$dm'_isim'$isim'.out'
+
+      echo 'OUT_FILE = ' $OUT_FILE
+
+      FOLDER_NAME=$ffname'_'$it'_'$dm'_isim'$isim
+
+      mkdir $FOLDER_NAME
+
+      cd $FOLDER_NAME
+
+      echo 'Entering folder name = ' $FOLDER_NAME
+      # Create output folder for results:
+      mkdir output
+
+INP_FILE_NAME='libsetup.inp'
+LIBRAD="/home/b/b381185/libRadtran"
+
+############################
+# CREATE INPUT FILE HERE !!!
+cat > $INP_FILE_NAME << EOF
+data_files_path $LIBRAD/data/
+atmosphere_file ${pathh}/atmosphere_mean_${dm}_202201${it}33Z.dat
+
+albedo 0.009000000000000008
+source thermal  
+mol_abs_param Fu   
+wavelength_index 7  18   
+output_process sum   
+
+# gas profiles
+#mixing_ratio CO2 348
+#mixing_ratio O2 209460
+#mixing_ratio CH4 1.650 
+#mixing_ratio N2O 0.396
+
+mixing_ratio F11 0.0
+mixing_ratio F12 0.0
+mixing_ratio F22 0.0
+mixing_ratio NO2 0.0
+
+mol_modify O4 0.0 DU
+mol_modify BRO 0.0 DU
+mol_modify OCLO 0.0 DU
+mol_modify HCHO 0.0 DU
+mol_modify SO2 0.0 DU
+mol_modify CO 0.0 DU
+mol_modify N2 0.0 DU
+
+#surface
+albedo_library IGBP
+brdf_rpv_type 17
+
+rte_solver rodents
+ipa_3d
+
+heating_rate layer_fd
+mc_backward_output heat K_per_day
+
+mc_sample_grid 41 33 1.9 3.3
+
+mc_basename $SCRIPTDIR/$FOLDER_NAME/output/$OUT_FILE
+
+wc_properties hu interpolate
+ic_properties $ice_p interpolate
+$ice_h
+
+wc_file 3D $CLOUD_WC_FILE_INP
+ic_file 3D $CLOUD_IC_FILE_INP
+
+quiet
+EOF
+### END OF INPUT FILE ###########
+#################################
+cat >ther_HR.sh<<EOF
+#! /bin/bash
+
+#SBATCH --account=bb1135
+#SBATCH --job-name=mystic_dom01.run
+#SBATCH --partition=shared
+#SBATCH --nodes=1
+#SBATCH --output=libther.sh.%j.out
+#SBATCH --error=libther.sh.%j.err
+#SBATCH --exclusive
+
+cd $(pwd)
+echo "Job started @ \$(date)"
+$LIBRAD/bin/uvspec < $INP_FILE_NAME
+echo "Job finished @ \$(date)"
+EOF
+
+        #Leave mysti/mcipa folder:
+        cd ..
+
+        done #over isim
+
+done # over dom
+done # over itime
+
+echo
+echo 
+echo 'The END'
diff --git a/offlineRT/c_cluster_thermal_ipa3d_dl/ccThermal/submit_runs.sh b/offlineRT/c_cluster_thermal_ipa3d_dl/ccThermal/submit_runs.sh
new file mode 100755
index 0000000000000000000000000000000000000000..21637528ea5ef932d16c174eb55cf819186a3041
--- /dev/null
+++ b/offlineRT/c_cluster_thermal_ipa3d_dl/ccThermal/submit_runs.sh
@@ -0,0 +1,15 @@
+#!/usr/bin/env bash
+
+#-------------------------------------------
+. VARIABLES
+
+for tt in "${time_array[@]}"
+do
+for ss in "${isim_array[@]}"
+do
+   echo "submitting runs for = " $tt'_'$ss
+
+   sbatch s_runs1.sh $tt $ss
+
+done 
+done
diff --git a/offlineRT/c_cluster_thermal_ipa3d_dl/find_missing_simulations.sh b/offlineRT/c_cluster_thermal_ipa3d_dl/find_missing_simulations.sh
new file mode 100755
index 0000000000000000000000000000000000000000..086f35bc2fe65d973dc41811af4484c941f260d9
--- /dev/null
+++ b/offlineRT/c_cluster_thermal_ipa3d_dl/find_missing_simulations.sh
@@ -0,0 +1,39 @@
+#!/usr/bin/env bash
+
+export time_array=("05T1000" "05T1030" "05T1100" "05T1130" "05T1200" "05T1230" "05T1300" "05T1330" "05T1400")
+
+export dom=("1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12" "13" "14" "15" "16" "17" "18" "19" "20" "21" "22" "23" "24" "25" "26" "27" "28" "29" "30" "31" "32" "33" "34" "35" "36")
+
+export isim_array=("01")
+
+pathh=$(pwd)
+#----------------------------------------------------------------------------------------
+ffname1='ipa3d'
+
+for it in "${time_array[@]}"
+do
+    echo 'time = ' $it
+
+for dm in "${dom[@]}"
+do
+echo 'dom = ' $dm
+
+for isim in "${isim_array[@]}"
+do
+
+if [ -n "$(ls -A $ffname1'_'$it'_'$dm'_isim'$isim/output 2>/dev/null)" ]
+then
+  echo "file exist (${ffname1}_'${it}'_'${dm}'_isim'${isim}'.out.abs.spc')"
+else
+  echo "file does not exist (${ffname1}_'${it}'_'${dm}'_isim'${isim}'.out.abs.spc')"
+  #count=(($i + 1))
+  # let's put these into a file for rerun
+  echo "/home/b/b381185/libRadtran/bin/uvspec < ${pathh}/${ffname1}_${it}_${dm}_isim${isim}/libsetup.inp &" >> submit_ipa3d.sh
+
+fi
+
+done
+done
+done
+
+#-----------------------
diff --git a/offlineRT/c_cluster_thermal_mystic/add_to_submit_file.txt b/offlineRT/c_cluster_thermal_mystic/add_to_submit_file.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6aa3cba2250c5ec60bfca8db6b889e3d1db067fb
--- /dev/null
+++ b/offlineRT/c_cluster_thermal_mystic/add_to_submit_file.txt
@@ -0,0 +1,12 @@
+#!/bin/bash
+#SBATCH --partition=interactive
+#SBATCH --account=bb1135
+#SBATCH --nodes=1
+#SBATCH --mem=256000
+#SBATCH --exclusive
+#SBATCH --output=libther.sh.%j.out
+#SBATCH --error=libther.sh.%j.err
+#---------------------------------
+
+
+wait
diff --git a/offlineRT/c_cluster_thermal_mystic/ccThermal/VARIABLES b/offlineRT/c_cluster_thermal_mystic/ccThermal/VARIABLES
new file mode 100644
index 0000000000000000000000000000000000000000..24817247af7017b8984cd21bb0624442b439cd45
--- /dev/null
+++ b/offlineRT/c_cluster_thermal_mystic/ccThermal/VARIABLES
@@ -0,0 +1,7 @@
+export time_array=("05T1000" "05T1030" "05T1100" "05T1130" "05T1200" "05T1230" "05T1300" "05T1330" "05T1400") 
+
+export dom=("1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12" "13" "14" "15" "16" "17" "18" "19" "20" "21" "22" "23" "24" "25" "26" "27" "28" "29" "30" "31" "32" "33" "34" "35" "36")
+
+export isim_array=("01" "02" "03" "04" "05" "06" "07" "08" "09" "10")
+
+
diff --git a/offlineRT/c_cluster_thermal_mystic/ccThermal/s_runs1.sh b/offlineRT/c_cluster_thermal_mystic/ccThermal/s_runs1.sh
new file mode 100755
index 0000000000000000000000000000000000000000..cfeb6010fe3a07f5c17e19495ce26a9e41ba0139
--- /dev/null
+++ b/offlineRT/c_cluster_thermal_mystic/ccThermal/s_runs1.sh
@@ -0,0 +1,31 @@
+#!/bin/bash
+#SBATCH --partition=compute
+#SBATCH --account=bb1135
+#SBATCH --nodes=1
+###SBATCH --mem=256000
+#SBATCH --exclusive
+#SBATCH --output=libther.sh.%j.out
+#SBATCH --error=libther.sh.%j.err
+
+
+#---------------------------------
+. VARIABLES
+
+pathh=$(cd ../../ && pwd)
+ffname1='mystic'
+ffname2='mysti'
+ssource='thermal'
+timee=$1
+num=$2
+
+echo "Job started @ $(date)"
+
+for dm in "${dom[@]}"
+do
+
+/home/b/b381185/libRadtran/bin/uvspec < ${pathh}/c_cluster_${ssource}_${ffname1}/${ffname2}_${timee}_${dm}_isim${num}/libsetup.inp &
+
+done
+wait
+
+echo "Job finished @ $(date)"
diff --git a/offlineRT/c_cluster_thermal_mystic/ccThermal/s_runs2.sh b/offlineRT/c_cluster_thermal_mystic/ccThermal/s_runs2.sh
new file mode 100755
index 0000000000000000000000000000000000000000..9463699a30ae5624368beb9f0b428dc7545d3fc8
--- /dev/null
+++ b/offlineRT/c_cluster_thermal_mystic/ccThermal/s_runs2.sh
@@ -0,0 +1,31 @@
+#!/bin/bash
+#SBATCH --partition=compute
+#SBATCH --account=bb1135
+#SBATCH --nodes=1
+###SBATCH --mem=256000
+#SBATCH --exclusive
+#SBATCH --output=libther.sh.%j.out
+#SBATCH --error=libther.sh.%j.err
+
+
+#---------------------------------
+. VARIABLES
+
+pathh=$(cd ../../ && pwd)
+ffname1='mystic'
+ffname2='mcipa'
+ssource='thermal'
+timee=$1
+num=$2
+
+echo "Job started @ $(date)"
+
+for dm in "${dom[@]}"
+do
+
+/home/b/b381185/libRadtran/bin/uvspec < ${pathh}/c_cluster_${ssource}_${ffname1}/${ffname2}_${timee}_${dm}_isim${num}/libsetup.inp &
+
+done
+wait
+
+echo "Job finished @ $(date)"
diff --git a/offlineRT/c_cluster_thermal_mystic/ccThermal/step1_makeInpFiles.sh b/offlineRT/c_cluster_thermal_mystic/ccThermal/step1_makeInpFiles.sh
new file mode 100755
index 0000000000000000000000000000000000000000..54e8d304bbf53385876fc1633df14469afe640a3
--- /dev/null
+++ b/offlineRT/c_cluster_thermal_mystic/ccThermal/step1_makeInpFiles.sh
@@ -0,0 +1,161 @@
+#!/usr/bin/env bash
+
+. VARIABLES
+
+IDENT=$1
+
+pathh=$(cd ../../../ && pwd)
+ffname='mysti'
+
+if [ "$IDENT" == "mcipa" ]
+then
+    MCIPA="mc_ipa"
+    ffname='mcipa'
+fi
+
+echo
+echo "Shell script sucessfully running!"
+echo
+
+echo
+echo "BUILD FOLDER NAMES AND INPUT FILES"
+echo
+
+#First go out of run folder:
+cd ..
+
+SCRIPTDIR=$(pwd)
+
+for it in "${time_array[@]}"
+do
+    echo 'time = ' $it
+
+for dm in "${dom[@]}"
+do
+echo 'dom = ' $dm
+
+    CLOUD_WC_FILE_INP=''$pathh'/wc3d_'$dm'_202201'$it'33Z.dat'
+    CLOUD_IC_FILE_INP=''$pathh'/ic3d_'$dm'_202201'$it'33Z.dat'
+    ZVALUES="$(head -n2 ${CLOUD_WC_FILE_INP} | tail -n1 | cut -d " " -f 3-)"
+
+      for isim in "${isim_array[@]}"
+      do
+      echo
+      echo "isim = " $isim
+      #if [ $isim == 01 ]
+      #then
+      #   ice_p='fu'
+      #   ice_h='#ic_habit rough-aggregate'
+      #elif [ $isim == 02 ]
+      #then
+      #   ice_p='baum_v36'
+      #   ice_h='ic_habit ghm'
+      #elif [ $isim == 03 ]
+      #then
+      #   ice_p='baum_v36'
+      #   ice_h='ic_habit solid-column'
+      #else
+      #   ice_p='baum_v36'
+      #   ice_h='ic_habit rough-aggregate'
+      #fi 	 
+      # Create libradtran output file name:
+      OUT_FILE=$ffname'_'$it'_'$dm'_isim'$isim'.out'
+
+      echo 'OUT_FILE = ' $OUT_FILE
+
+      FOLDER_NAME=$ffname'_'$it'_'$dm'_isim'$isim
+
+      mkdir $FOLDER_NAME
+
+      cd $FOLDER_NAME
+
+      echo 'Entering folder name = ' $FOLDER_NAME
+      # Create output folder for results:
+      mkdir output
+
+INP_FILE_NAME='libsetup.inp'
+LIBRAD="/home/b/b381185/libRadtran"
+
+############################
+# CREATE INPUT FILE HERE !!!
+cat > $INP_FILE_NAME << EOF
+data_files_path $LIBRAD/data/
+atmosphere_file ${pathh}/atmosphere_mean_${dm}_202201${it}33Z.dat
+
+albedo 0.009000000000000008
+source thermal  
+mol_abs_param Fu   
+wavelength_index 7  18   
+output_process sum   
+
+# gas profiles
+#mixing_ratio CO2 348
+#mixing_ratio O2 209460
+#mixing_ratio CH4 1.650 
+#mixing_ratio N2O 0.396
+
+mixing_ratio F11 0.0
+mixing_ratio F12 0.0
+mixing_ratio F22 0.0
+mixing_ratio NO2 0.0
+
+mol_modify O4 0.0 DU
+mol_modify BRO 0.0 DU
+mol_modify OCLO 0.0 DU
+mol_modify HCHO 0.0 DU
+mol_modify SO2 0.0 DU
+mol_modify CO 0.0 DU
+mol_modify N2 0.0 DU
+
+#surface
+albedo_library IGBP
+brdf_rpv_type 17
+
+rte_solver mystic
+$MCIPA
+
+mc_forward_output heating K_per_day
+mc_photons 72500000
+
+mc_sample_grid 420 343 0.227 0.394
+
+mc_basename $SCRIPTDIR/$FOLDER_NAME/output/$OUT_FILE
+
+wc_properties hu interpolate
+ic_properties fu interpolate
+
+wc_file 3D $CLOUD_WC_FILE_INP
+ic_file 3D $CLOUD_IC_FILE_INP
+
+quiet
+EOF
+### END OF INPUT FILE ###########
+#################################
+cat >ther_HR.sh<<EOF
+#! /bin/bash
+
+#SBATCH --account=bb1135
+#SBATCH --job-name=mystic_dom01.run
+#SBATCH --partition=shared
+#SBATCH --nodes=1
+#SBATCH --output=libther.sh.%j.out
+#SBATCH --error=libther.sh.%j.err
+#SBATCH --exclusive
+
+cd $(pwd)
+echo "Job started @ \$(date)"
+$LIBRAD/bin/uvspec < $INP_FILE_NAME
+echo "Job finished @ \$(date)"
+EOF
+
+        #Leave mysti/mcipa folder:
+        cd ..
+
+        done #over isim
+
+done # over dom
+done # over itime
+
+echo
+echo 
+echo 'The END'
diff --git a/offlineRT/c_cluster_thermal_mystic/ccThermal/submit_runs.sh b/offlineRT/c_cluster_thermal_mystic/ccThermal/submit_runs.sh
new file mode 100755
index 0000000000000000000000000000000000000000..6452e686c08d695e0bfeddcb9098a2ac42e840a3
--- /dev/null
+++ b/offlineRT/c_cluster_thermal_mystic/ccThermal/submit_runs.sh
@@ -0,0 +1,16 @@
+#!/usr/bin/env bash
+
+#-------------------------------------------
+. VARIABLES
+
+for tt in "${time_array[@]}"
+do
+for ss in "${isim_array[@]}"
+do
+   echo "submitting runs for = " $tt'_'$ss
+
+   sbatch s_runs1.sh $tt $ss
+   sbatch s_runs2.sh $tt $ss
+
+done 
+done
diff --git a/offlineRT/c_cluster_thermal_mystic/find_missing_simulations.sh b/offlineRT/c_cluster_thermal_mystic/find_missing_simulations.sh
new file mode 100755
index 0000000000000000000000000000000000000000..2623b6927aa33b794041f597355896a6bd42db07
--- /dev/null
+++ b/offlineRT/c_cluster_thermal_mystic/find_missing_simulations.sh
@@ -0,0 +1,68 @@
+#!/usr/bin/env bash
+
+export time_array=("05T1000" "05T1030" "05T1100" "05T1130" "05T1200" "05T1230" "05T1300" "05T1330" "05T1400")
+
+export dom=("1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12" "13" "14" "15" "16" "17" "18" "19" "20" "21" "22" "23" "24" "25" "26" "27" "28" "29" "30" "31" "32" "33" "34" "35" "36")
+
+export isim_array=("01" "02" "03" "04" "05" "06" "07" "08" "09" "10")
+
+pathh=$(pwd)
+#----------------------------------------------------------------------------------------
+ffname1='mysti'
+
+for it in "${time_array[@]}"
+do
+    echo 'time = ' $it
+
+for dm in "${dom[@]}"
+do
+echo 'dom = ' $dm
+
+for isim in "${isim_array[@]}"
+do
+
+if [ -n "$(ls -A $ffname1'_'$it'_'$dm'_isim'$isim/output 2>/dev/null)" ]
+then
+  echo "file exist (${ffname1}_'${it}'_'${dm}'_isim'${isim}'.out.abs.spc')"
+else
+  echo "file does not exist (${ffname1}_'${it}'_'${dm}'_isim'${isim}'.out.abs.spc')"
+  #count=(($i + 1))
+  # let's put these into a file for rerun
+  echo "/home/b/b381185/libRadtran/bin/uvspec < ${pathh}/${ffname1}_${it}_${dm}_isim${isim}/libsetup.inp &" >> submit_mysti.sh
+
+fi
+
+done
+done
+done
+
+#---------------------------- mcipa
+ffname2='mcipa'
+
+for it in "${time_array[@]}"
+do
+    echo 'time = ' $it
+
+for dm in "${dom[@]}"
+do
+echo 'dom = ' $dm
+
+for isim in "${isim_array[@]}"
+do
+
+if [ -n "$(ls -A $ffname2'_'$it'_'$dm'_isim'$isim/output 2>/dev/null)" ]
+then
+  echo "file exist (${ffname2}_'${it}'_'${dm}'_isim'${isim}'.out.abs.spc')"
+else
+  echo "file does not exist (${ffname2}_'${it}'_'${dm}'_isim'${isim}'.out.abs.spc')"
+  #count=(($i + 1))
+  # let's put these into a file for rerun
+  echo "/home/b/b381185/libRadtran/bin/uvspec < ${pathh}/${ffname2}_${it}_${dm}_isim${isim}/libsetup.inp &" >> submit_mcipa.sh
+
+fi
+
+done
+done
+done
+
+#-----------------------
diff --git a/offlineRT/convert_libradtran_data_to_netcdf_ipa3d.py b/offlineRT/convert_libradtran_data_to_netcdf_ipa3d.py
new file mode 100644
index 0000000000000000000000000000000000000000..81bd13a3878f635eb6f672efb08a3607b7accf65
--- /dev/null
+++ b/offlineRT/convert_libradtran_data_to_netcdf_ipa3d.py
@@ -0,0 +1,194 @@
+# Behrooz Keshtgar, KIT
+
+# loading modules
+
+import xarray as xr
+import numpy as np
+import pandas as pd
+import warnings
+warnings.filterwarnings("ignore")
+
+
+print('start converting ipa3d outputs to netcdf files')
+# Dictionary for loading simulations
+simdict = {
+         'LC1-LES-471x667km-lon25-lat40-300m-0006' : {'res':'300m', 'radiation':4, 'mphy':4}, # Only cloud radiation
+         'LC1-LES-471x667km-lon30-lat53-300m-0005' : {'res':'300m', 'radiation':4, 'mphy':4}, # Only cloud radiation
+         'LC1-LES-471x667km-lon40-lat44-300m-0003' : {'res':'300m', 'radiation':4, 'mphy':4}, # Only cloud radiation
+         'LC1-LES-471x667km-lon50-lat48-300m-0004' : {'res':'300m', 'radiation':4, 'mphy':4}, # Only cloud radiation
+          }
+
+# here change the simulation path
+simm = list(simdict.keys())[0]
+path = '/work/bb1135/icon_output/'+simm+'/input4libradtran/libradtran/'
+#---------------------------------------
+# number of model layers
+height = np.arange(0,150,1)
+nz = 150
+nx = 420
+ny = 343
+# cutting boundaries
+nx2 = 344
+ny2 = 281
+
+# solver names for clear-sky hr
+solvers_name=['rodents'] 
+k = 0
+#---------------------------------------
+# loop over time steps
+for time in ['05T1000','05T1030','05T1100','05T1130','05T1200','05T1230','05T1300','05T1330','05T1400']:   
+    # loop over solvers
+    for solver in ['ipa3d']:
+        # loop over sources (thermal/solar)
+        for source in ['thermal','solar']:
+            # loop over sim types
+            for nsim in ['01','02','03','04']: # 01: ICE FU, 02: ICE Baum-ghm, 03: ICE Baum-sc, 04: ICE Baum-rg
+                print('working on datasets for:', time+'/'+solver+'/'+source+'/'+nsim)
+                # list conatining paths to the results
+                simpath = []
+                # list holding names of the files
+                sims=[]
+                # list holding names of the files for clear-sky results
+                sims2=[]
+                # loop over subdomains
+                for dom in range(1,37):
+                    siim = solvers_name[k]+'_'+time+'_'+str(dom)+'_isim'+nsim
+                    sims2.append(siim)
+                    sim = solver+'_'+time+'_'+str(dom)+'_isim'+nsim
+                    simpath.append(path+'c_cluster_'+source+'_'+solver+'/'+sim+'/')
+                    sims.append(sim)
+
+                # empty list to store heating rates
+                thr_3d = []
+                for i in range (len(simpath)):
+                    print('****loading ascii file:',sims[i])
+                    hr = (pd.read_table(simpath[i]+'output/'+sims[i]+'.out.abs.spc',header=None,sep='\s+',usecols=[4]).values)[:,0]
+                    #---------------------------------------
+                    temp = np.zeros((nx, ny, nz))
+                    for i in range(nz):
+                        temp[:,:,i] = np.reshape(hr[nx*ny*i:nx*ny*i+nx*ny],(nx,ny))
+                    # cutting boundaries
+                    temp2 = temp[38:420-38,31:343-31,:]
+                    thr_3d.append(temp2*1.4) #  *cp/cv factor in ICON
+
+                # loading clear sky heating rates
+                cshr = []
+                print('****loading clear-sky hr')
+                for i in range (len(sims2)):
+                    cshr_t = (pd.read_table(path+source+'_clear_sky/output/'+sims2[i].replace('isim'+nsim,'cs_hr')+'.out',header=None,sep='\s+',usecols=[1]).values)[:,0]
+                    tmp = cshr_t*1.4 # x cp/cv factor in ICON
+                    # broadcasting to 3D structure
+                    tmp2 = np.broadcast_to(tmp,(nx2,ny2,nz)).copy()
+                    cshr.append(tmp2)
+
+                print('****create dataset')
+                # creating datasets
+
+                ds = xr.Dataset({
+                #'half_levels': xr.DataArray(z1d, dims=('height')),
+                'ddt_dom1': xr.DataArray(thr_3d[0], dims=('lon','lat','height')),
+                'ddt_dom2': xr.DataArray(thr_3d[1], dims=('lon','lat','height')),
+                'ddt_dom3': xr.DataArray(thr_3d[2], dims=('lon','lat','height')),
+                'ddt_dom4': xr.DataArray(thr_3d[3], dims=('lon','lat','height')),
+                'ddt_dom5': xr.DataArray(thr_3d[4], dims=('lon','lat','height')),
+                'ddt_dom6': xr.DataArray(thr_3d[5], dims=('lon','lat','height')),
+                'ddt_dom7': xr.DataArray(thr_3d[6], dims=('lon','lat','height')),
+                'ddt_dom8': xr.DataArray(thr_3d[7], dims=('lon','lat','height')),
+                'ddt_dom9': xr.DataArray(thr_3d[8], dims=('lon','lat','height')),
+                'ddt_dom10': xr.DataArray(thr_3d[9], dims=('lon','lat','height')),
+                'ddt_dom11': xr.DataArray(thr_3d[10], dims=('lon','lat','height')),
+                'ddt_dom12': xr.DataArray(thr_3d[11], dims=('lon','lat','height')),
+                'ddt_dom13': xr.DataArray(thr_3d[12], dims=('lon','lat','height')),
+                'ddt_dom14': xr.DataArray(thr_3d[13], dims=('lon','lat','height')),
+                'ddt_dom15': xr.DataArray(thr_3d[14], dims=('lon','lat','height')),
+                'ddt_dom16': xr.DataArray(thr_3d[15], dims=('lon','lat','height')),
+                'ddt_dom17': xr.DataArray(thr_3d[16], dims=('lon','lat','height')),
+                'ddt_dom18': xr.DataArray(thr_3d[17], dims=('lon','lat','height')),
+                'ddt_dom19': xr.DataArray(thr_3d[18], dims=('lon','lat','height')),
+                'ddt_dom20': xr.DataArray(thr_3d[19], dims=('lon','lat','height')),
+                'ddt_dom21': xr.DataArray(thr_3d[20], dims=('lon','lat','height')),
+                'ddt_dom22': xr.DataArray(thr_3d[21], dims=('lon','lat','height')),
+                'ddt_dom23': xr.DataArray(thr_3d[22], dims=('lon','lat','height')),
+                'ddt_dom24': xr.DataArray(thr_3d[23], dims=('lon','lat','height')),
+                'ddt_dom25': xr.DataArray(thr_3d[24], dims=('lon','lat','height')),
+                'ddt_dom26': xr.DataArray(thr_3d[25], dims=('lon','lat','height')),
+                'ddt_dom27': xr.DataArray(thr_3d[26], dims=('lon','lat','height')),
+                'ddt_dom28': xr.DataArray(thr_3d[27], dims=('lon','lat','height')),
+                'ddt_dom29': xr.DataArray(thr_3d[28], dims=('lon','lat','height')),
+                'ddt_dom30': xr.DataArray(thr_3d[29], dims=('lon','lat','height')),
+                'ddt_dom31': xr.DataArray(thr_3d[30], dims=('lon','lat','height')),
+                'ddt_dom32': xr.DataArray(thr_3d[31], dims=('lon','lat','height')),
+                'ddt_dom33': xr.DataArray(thr_3d[32], dims=('lon','lat','height')),
+                'ddt_dom34': xr.DataArray(thr_3d[33], dims=('lon','lat','height')),
+                'ddt_dom35': xr.DataArray(thr_3d[34], dims=('lon','lat','height')),
+                'ddt_dom36': xr.DataArray(thr_3d[35], dims=('lon','lat','height')),
+
+                'ddt_cs_dom1': xr.DataArray(cshr[0], dims=('lon','lat','height')),
+                'ddt_cs_dom2': xr.DataArray(cshr[1], dims=('lon','lat','height')),
+                'ddt_cs_dom3': xr.DataArray(cshr[2], dims=('lon','lat','height')),
+                'ddt_cs_dom4': xr.DataArray(cshr[3], dims=('lon','lat','height')),
+                'ddt_cs_dom5': xr.DataArray(cshr[4], dims=('lon','lat','height')),
+                'ddt_cs_dom6': xr.DataArray(cshr[5], dims=('lon','lat','height')),
+                'ddt_cs_dom7': xr.DataArray(cshr[6], dims=('lon','lat','height')),
+                'ddt_cs_dom8': xr.DataArray(cshr[7], dims=('lon','lat','height')),
+                'ddt_cs_dom9': xr.DataArray(cshr[8], dims=('lon','lat','height')),
+                'ddt_cs_dom10': xr.DataArray(cshr[9], dims=('lon','lat','height')),
+                'ddt_cs_dom11': xr.DataArray(cshr[10], dims=('lon','lat','height')),
+                'ddt_cs_dom12': xr.DataArray(cshr[11], dims=('lon','lat','height')),
+                'ddt_cs_dom13': xr.DataArray(cshr[12], dims=('lon','lat','height')),
+                'ddt_cs_dom14': xr.DataArray(cshr[13], dims=('lon','lat','height')),
+                'ddt_cs_dom15': xr.DataArray(cshr[14], dims=('lon','lat','height')),
+                'ddt_cs_dom16': xr.DataArray(cshr[15], dims=('lon','lat','height')),
+                'ddt_cs_dom17': xr.DataArray(cshr[16], dims=('lon','lat','height')),
+                'ddt_cs_dom18': xr.DataArray(cshr[17], dims=('lon','lat','height')),
+                'ddt_cs_dom19': xr.DataArray(cshr[18], dims=('lon','lat','height')),
+                'ddt_cs_dom20': xr.DataArray(cshr[19], dims=('lon','lat','height')),
+                'ddt_cs_dom21': xr.DataArray(cshr[20], dims=('lon','lat','height')),
+                'ddt_cs_dom22': xr.DataArray(cshr[21], dims=('lon','lat','height')),
+                'ddt_cs_dom23': xr.DataArray(cshr[22], dims=('lon','lat','height')),
+                'ddt_cs_dom24': xr.DataArray(cshr[23], dims=('lon','lat','height')),
+                'ddt_cs_dom25': xr.DataArray(cshr[24], dims=('lon','lat','height')),
+                'ddt_cs_dom26': xr.DataArray(cshr[25], dims=('lon','lat','height')),
+                'ddt_cs_dom27': xr.DataArray(cshr[26], dims=('lon','lat','height')),
+                'ddt_cs_dom28': xr.DataArray(cshr[27], dims=('lon','lat','height')),
+                'ddt_cs_dom29': xr.DataArray(cshr[28], dims=('lon','lat','height')),
+                'ddt_cs_dom30': xr.DataArray(cshr[29], dims=('lon','lat','height')),
+                'ddt_cs_dom31': xr.DataArray(cshr[30], dims=('lon','lat','height')),
+                'ddt_cs_dom32': xr.DataArray(cshr[31], dims=('lon','lat','height')),
+                'ddt_cs_dom33': xr.DataArray(cshr[32], dims=('lon','lat','height')),
+                'ddt_cs_dom34': xr.DataArray(cshr[33], dims=('lon','lat','height')),
+                'ddt_cs_dom35': xr.DataArray(cshr[34], dims=('lon','lat','height')),
+                'ddt_cs_dom36': xr.DataArray(cshr[35], dims=('lon','lat','height')),
+
+                },
+                coords={"lat": (["lat"], np.arange(0,ny2)),
+                                "lon": (["lon"], np.arange(0,nx2)),
+                                'height':(["height"],np.arange(0,nz)),})
+
+                # let's merge them together here
+                print('****merging subdomains into the big domain')
+                ds_j = []
+                ds_j2 = []
+                for j in range(1,37,6):
+                    ds_i = xr.concat([ds['ddt_dom'+str(j)+''],ds['ddt_dom'+str(j+1)+''],
+                                      ds['ddt_dom'+str(j+2)+''],ds['ddt_dom'+str(j+3)+''],
+                                      ds['ddt_dom'+str(j+4)+''],ds['ddt_dom'+str(j+5)+'']],dim='lon')
+                    ds_j.append(ds_i)
+
+                    ds_i2 = xr.concat([ds['ddt_cs_dom'+str(j)+''],ds['ddt_cs_dom'+str(j+1)+''],
+                                      ds['ddt_cs_dom'+str(j+2)+''],ds['ddt_cs_dom'+str(j+3)+''],
+                                      ds['ddt_cs_dom'+str(j+4)+''],ds['ddt_cs_dom'+str(j+5)+'']],dim='lon')
+                    ds_j2.append(ds_i2)
+                # concat along y dim
+                ds_f1 = xr.concat([ds_j[0],ds_j[1],ds_j[2],ds_j[3],ds_j[4],ds_j[5]],dim='lat')
+                ds_f2 = xr.concat([ds_j2[0],ds_j2[1],ds_j2[2],ds_j2[3],ds_j2[4],ds_j2[5]],dim='lat')
+                ds_1 = xr.merge([ds_f1,ds_f2])
+                ds_1['ddt_radlw'] = ds_1['ddt_dom1'] - ds_1['ddt_cs_dom1']
+                ds_1.coords['lon'] = np.arange(0,344*6)
+                ds_1.coords['lat'] = np.arange(0,281*6)
+                ds_1 = ds_1.expand_dims('time')
+                print('****save')
+                ds_1.to_netcdf('/work/bb1135/icon_output/'+simm+'/input4libradtran/libradtran/output_netcdf/ds_librad2_'+time+'_'+source+'_'+solver+'_'+nsim+'.nc')
+                print('----------------------------------------------------------------------------')
+
+print('finished')
diff --git a/offlineRT/convert_libradtran_data_to_netcdf_ipa3d_nwp.py b/offlineRT/convert_libradtran_data_to_netcdf_ipa3d_nwp.py
new file mode 100644
index 0000000000000000000000000000000000000000..48a4efd20dcf6f89a0834b5fb37a226504b05a7c
--- /dev/null
+++ b/offlineRT/convert_libradtran_data_to_netcdf_ipa3d_nwp.py
@@ -0,0 +1,196 @@
+# Behrooz Keshtgar, KIT
+
+# loading modules
+
+import xarray as xr
+import numpy as np
+import pandas as pd
+import warnings
+warnings.filterwarnings("ignore")
+
+
+print('start converting ipa3d outputs to netcdf files')
+# path to the libradtran results
+
+# Dictionary for loading simulations
+simdict = {
+         'LC1-LES-471x667km-lon25-lat40-300m-0006' : {'res':'300m', 'radiation':4, 'mphy':4}, # Only cloud radiation
+         'LC1-LES-471x667km-lon30-lat53-300m-0005' : {'res':'300m', 'radiation':4, 'mphy':4}, # Only cloud radiation
+         'LC1-LES-471x667km-lon40-lat44-300m-0003' : {'res':'300m', 'radiation':4, 'mphy':4}, # Only cloud radiation
+         'LC1-LES-471x667km-lon50-lat48-300m-0004' : {'res':'300m', 'radiation':4, 'mphy':4}, # Only cloud radiation
+          }
+
+# here change the simulation path
+simm = list(simdict.keys())[0]
+path = '/work/bb1135/icon_output/'+simm+'/input4libradtran/libradtran/'
+#---------------------------------------
+# number of model layers
+height = np.arange(0,150,1)
+nz = 150
+nx = 41
+ny = 33
+
+# solver names for clear-sky hr
+solvers_name=['rodents']
+k = 0
+
+# NWP clouds with or without fractions, change between suffix (cg/dl)
+cldt = 'cg'
+#---------------------------------------
+# loop over time steps
+for time in ['05T1000','05T1030','05T1100','05T1130','05T1200','05T1230','05T1300','05T1330','05T1400']:
+    # loop over solvers
+    for solver in ['ipa3d']:
+        # loop over sources (thermal/solar)
+        for source in ['thermal','solar']:
+            # loop over sim types
+            for nsim in ['01']: # 01: ICE FU, 02: ICE Baum-ghm, 03: ICE Baum-sc, 04: ICE Baum-rg
+                print('working on datasets for:', time+'/'+solver+'/'+source+'/'+nsim)
+                # list conatining paths to the results
+                simpath = []
+                # list holding names of the files
+                sims=[]
+                # list holding names of the files for clear-sky results
+                sims2=[]
+                # loop over subdomains
+                for dom in range(1,37):
+                    siim = solvers_name[k]+'_'+time+'_'+str(dom)+'_isim'+nsim
+                    sims2.append(siim)
+                    sim = solver+'_'+time+'_'+str(dom)+'_isim'+nsim
+                    simpath.append(path+'c_cluster_'+source+'_'+solver+'_'+cldt+'/'+sim+'/')
+                    sims.append(sim)
+
+                # empty list to store heating rates
+                thr_3d = []
+                for i in range (len(simpath)):
+                    print('****loading ascii file:',sims[i])
+                    #---------------------------------------
+                    hr = (pd.read_table(simpath[i]+'output/'+sims[i]+'.out.abs.spc',header=None,sep='\s+',usecols=[4]).values)[:,0]
+                    #print('****creating 3d structure for file:',sims[i])
+                    temp = np.zeros((nx, ny, nz))
+                    for i in range(nz):
+                        temp[:,:,i] = np.reshape(hr[nx*ny*i:nx*ny*i+nx*ny],(nx,ny))
+                    thr_3d.append(temp*1.4) #  *cp/cv factor in ICON
+
+                # loading clear sky heating rates
+                cshr = []
+                print('****loading clear-sky hr')
+                for i in range (len(sims2)):
+                    cshr_t = (pd.read_table(path+source+'_clear_sky/output/'+sims2[i].replace('isim'+nsim,'cs_hr')+'.out',header=None,sep='\s+',usecols=[1]).values)[:,0]
+                    tmp = cshr_t*1.4 # x cp/cv factor in ICON
+                    # broadcasting to 3D structure
+                    tmp2 = np.broadcast_to(tmp,(nx,ny,nz)).copy()
+                    cshr.append(tmp2)
+
+                print('****create dataset')
+                # creating datasets
+                z1d    = (pd.read_table(path+source+'_clear_sky/output/'+sims2[i].replace('isim'+nsim,'cs_hr')+'.out',header=None,sep='\s+',usecols=[0]).values)[:,0]
+
+                ds = xr.Dataset({
+                'half_levels': xr.DataArray(z1d, dims=('height')),
+                'ddt_dom1': xr.DataArray(thr_3d[0], dims=('lon','lat','height')),
+                'ddt_dom2': xr.DataArray(thr_3d[1], dims=('lon','lat','height')),
+                'ddt_dom3': xr.DataArray(thr_3d[2], dims=('lon','lat','height')),
+                'ddt_dom4': xr.DataArray(thr_3d[3], dims=('lon','lat','height')),
+                'ddt_dom5': xr.DataArray(thr_3d[4], dims=('lon','lat','height')),
+                'ddt_dom6': xr.DataArray(thr_3d[5], dims=('lon','lat','height')),
+                'ddt_dom7': xr.DataArray(thr_3d[6], dims=('lon','lat','height')),
+                'ddt_dom8': xr.DataArray(thr_3d[7], dims=('lon','lat','height')),
+                'ddt_dom9': xr.DataArray(thr_3d[8], dims=('lon','lat','height')),
+                'ddt_dom10': xr.DataArray(thr_3d[9], dims=('lon','lat','height')),
+                'ddt_dom11': xr.DataArray(thr_3d[10], dims=('lon','lat','height')),
+                'ddt_dom12': xr.DataArray(thr_3d[11], dims=('lon','lat','height')),
+                'ddt_dom13': xr.DataArray(thr_3d[12], dims=('lon','lat','height')),
+                'ddt_dom14': xr.DataArray(thr_3d[13], dims=('lon','lat','height')),
+                'ddt_dom15': xr.DataArray(thr_3d[14], dims=('lon','lat','height')),
+                'ddt_dom16': xr.DataArray(thr_3d[15], dims=('lon','lat','height')),
+                'ddt_dom17': xr.DataArray(thr_3d[16], dims=('lon','lat','height')),
+                'ddt_dom18': xr.DataArray(thr_3d[17], dims=('lon','lat','height')),
+                'ddt_dom19': xr.DataArray(thr_3d[18], dims=('lon','lat','height')),
+                'ddt_dom20': xr.DataArray(thr_3d[19], dims=('lon','lat','height')),
+                'ddt_dom21': xr.DataArray(thr_3d[20], dims=('lon','lat','height')),
+                'ddt_dom22': xr.DataArray(thr_3d[21], dims=('lon','lat','height')),
+                'ddt_dom23': xr.DataArray(thr_3d[22], dims=('lon','lat','height')),
+                'ddt_dom24': xr.DataArray(thr_3d[23], dims=('lon','lat','height')),
+                'ddt_dom25': xr.DataArray(thr_3d[24], dims=('lon','lat','height')),
+                'ddt_dom26': xr.DataArray(thr_3d[25], dims=('lon','lat','height')),
+                'ddt_dom27': xr.DataArray(thr_3d[26], dims=('lon','lat','height')),
+                'ddt_dom28': xr.DataArray(thr_3d[27], dims=('lon','lat','height')),
+                'ddt_dom29': xr.DataArray(thr_3d[28], dims=('lon','lat','height')),
+                'ddt_dom30': xr.DataArray(thr_3d[29], dims=('lon','lat','height')),
+                'ddt_dom31': xr.DataArray(thr_3d[30], dims=('lon','lat','height')),
+                'ddt_dom32': xr.DataArray(thr_3d[31], dims=('lon','lat','height')),
+                'ddt_dom33': xr.DataArray(thr_3d[32], dims=('lon','lat','height')),
+                'ddt_dom34': xr.DataArray(thr_3d[33], dims=('lon','lat','height')),
+                'ddt_dom35': xr.DataArray(thr_3d[34], dims=('lon','lat','height')),
+                'ddt_dom36': xr.DataArray(thr_3d[35], dims=('lon','lat','height')),
+
+                'ddt_cs_dom1': xr.DataArray(cshr[0], dims=('lon','lat','height')),
+                'ddt_cs_dom2': xr.DataArray(cshr[1], dims=('lon','lat','height')),
+                'ddt_cs_dom3': xr.DataArray(cshr[2], dims=('lon','lat','height')),
+                'ddt_cs_dom4': xr.DataArray(cshr[3], dims=('lon','lat','height')),
+                'ddt_cs_dom5': xr.DataArray(cshr[4], dims=('lon','lat','height')),
+                'ddt_cs_dom6': xr.DataArray(cshr[5], dims=('lon','lat','height')),
+                'ddt_cs_dom7': xr.DataArray(cshr[6], dims=('lon','lat','height')),
+                'ddt_cs_dom8': xr.DataArray(cshr[7], dims=('lon','lat','height')),
+                'ddt_cs_dom9': xr.DataArray(cshr[8], dims=('lon','lat','height')),
+                'ddt_cs_dom10': xr.DataArray(cshr[9], dims=('lon','lat','height')),
+                'ddt_cs_dom11': xr.DataArray(cshr[10], dims=('lon','lat','height')),
+                'ddt_cs_dom12': xr.DataArray(cshr[11], dims=('lon','lat','height')),
+                'ddt_cs_dom13': xr.DataArray(cshr[12], dims=('lon','lat','height')),
+                'ddt_cs_dom14': xr.DataArray(cshr[13], dims=('lon','lat','height')),
+                'ddt_cs_dom15': xr.DataArray(cshr[14], dims=('lon','lat','height')),
+                'ddt_cs_dom16': xr.DataArray(cshr[15], dims=('lon','lat','height')),
+                'ddt_cs_dom17': xr.DataArray(cshr[16], dims=('lon','lat','height')),
+                'ddt_cs_dom18': xr.DataArray(cshr[17], dims=('lon','lat','height')),
+                'ddt_cs_dom19': xr.DataArray(cshr[18], dims=('lon','lat','height')),
+                'ddt_cs_dom20': xr.DataArray(cshr[19], dims=('lon','lat','height')),
+                'ddt_cs_dom21': xr.DataArray(cshr[20], dims=('lon','lat','height')),
+                'ddt_cs_dom22': xr.DataArray(cshr[21], dims=('lon','lat','height')),
+                'ddt_cs_dom23': xr.DataArray(cshr[22], dims=('lon','lat','height')),
+                'ddt_cs_dom24': xr.DataArray(cshr[23], dims=('lon','lat','height')),
+                'ddt_cs_dom25': xr.DataArray(cshr[24], dims=('lon','lat','height')),
+                'ddt_cs_dom26': xr.DataArray(cshr[25], dims=('lon','lat','height')),
+                'ddt_cs_dom27': xr.DataArray(cshr[26], dims=('lon','lat','height')),
+                'ddt_cs_dom28': xr.DataArray(cshr[27], dims=('lon','lat','height')),
+                'ddt_cs_dom29': xr.DataArray(cshr[28], dims=('lon','lat','height')),
+                'ddt_cs_dom30': xr.DataArray(cshr[29], dims=('lon','lat','height')),
+                'ddt_cs_dom31': xr.DataArray(cshr[30], dims=('lon','lat','height')),
+                'ddt_cs_dom32': xr.DataArray(cshr[31], dims=('lon','lat','height')),
+                'ddt_cs_dom33': xr.DataArray(cshr[32], dims=('lon','lat','height')),
+                'ddt_cs_dom34': xr.DataArray(cshr[33], dims=('lon','lat','height')),
+                'ddt_cs_dom35': xr.DataArray(cshr[34], dims=('lon','lat','height')),
+                'ddt_cs_dom36': xr.DataArray(cshr[35], dims=('lon','lat','height')),
+
+                },
+                coords={"lat": (["lat"], np.arange(0,ny)),
+                                "lon": (["lon"], np.arange(0,nx)),
+                                'height':(["height"],np.arange(0,nz)),})
+
+                # let's merge them together here
+                print('****merging subdomains into the big domain')
+                ds_j = []
+                ds_j2 = []
+                for j in range(1,37,6):
+                    ds_i = xr.concat([ds['ddt_dom'+str(j)+''],ds['ddt_dom'+str(j+1)+''],
+                                      ds['ddt_dom'+str(j+2)+''],ds['ddt_dom'+str(j+3)+''],
+                                      ds['ddt_dom'+str(j+4)+''],ds['ddt_dom'+str(j+5)+'']],dim='lon')
+                    ds_j.append(ds_i)
+
+                    ds_i2 = xr.concat([ds['ddt_cs_dom'+str(j)+''],ds['ddt_cs_dom'+str(j+1)+''],
+                                      ds['ddt_cs_dom'+str(j+2)+''],ds['ddt_cs_dom'+str(j+3)+''],
+                                      ds['ddt_cs_dom'+str(j+4)+''],ds['ddt_cs_dom'+str(j+5)+'']],dim='lon')
+                    ds_j2.append(ds_i2)
+                # concat along y dim
+                ds_f1 = xr.concat([ds_j[0],ds_j[1],ds_j[2],ds_j[3],ds_j[4],ds_j[5]],dim='lat')
+                ds_f2 = xr.concat([ds_j2[0],ds_j2[1],ds_j2[2],ds_j2[3],ds_j2[4],ds_j2[5]],dim='lat')
+                ds_1 = xr.merge([ds_f1,ds_f2])
+                ds_1['ddt_radlw'] = ds_1['ddt_dom1'] - ds_1['ddt_cs_dom1']
+                ds_1.coords['lon'] = np.arange(0,41*6)
+                ds_1.coords['lat'] = np.arange(0,33*6)
+                ds_1 = ds_1.expand_dims('time')
+                print('****save')
+                ds_1.to_netcdf('/work/bb1135/icon_output/'+simm+'/input4libradtran/libradtran/output_netcdf/ds_librad_'+cldt+'_'+time+'_'+source+'_'+solver+'_'+nsim+'.nc')
+                print('----------------------------------------------------------------------------')
+
+print('finished')
diff --git a/offlineRT/convert_libradtran_data_to_netcdf_mystic.py b/offlineRT/convert_libradtran_data_to_netcdf_mystic.py
new file mode 100644
index 0000000000000000000000000000000000000000..2fb892b147ff4d00e26f90f8406e8afc0f242404
--- /dev/null
+++ b/offlineRT/convert_libradtran_data_to_netcdf_mystic.py
@@ -0,0 +1,204 @@
+# Behrooz Keshtgar, KIT
+
+# loading modules
+
+import xarray as xr
+import numpy as np
+import pandas as pd
+import warnings
+warnings.filterwarnings("ignore")
+
+
+print('start converting mystic/mcipa outputs to netcdf files')
+# Dictionary for loading simulations
+simdict = {
+         'LC1-LES-471x667km-lon25-lat40-300m-0006' : {'res':'300m', 'radiation':4, 'mphy':4}, # Only cloud radiation
+         'LC1-LES-471x667km-lon30-lat53-300m-0005' : {'res':'300m', 'radiation':4, 'mphy':4}, # Only cloud radiation
+         'LC1-LES-471x667km-lon40-lat44-300m-0003' : {'res':'300m', 'radiation':4, 'mphy':4}, # Only cloud radiation
+         'LC1-LES-471x667km-lon50-lat48-300m-0004' : {'res':'300m', 'radiation':4, 'mphy':4}, # Only cloud radiation
+          }
+
+# here change the simulation path
+simm = list(simdict.keys())[0]
+path = '/work/bb1135/icon_output/'+simm+'/input4libradtran/libradtran/'
+#---------------------------------------
+# number of model layers
+height = np.arange(0,150,1)
+nz = 150
+nx = 420 
+ny = 343 
+# cutting boundaries
+nx2 = 344
+ny2 = 281
+
+# solver name for clear-sky hr
+solvers_name=['mystic']
+k = 0
+#---------------------------------------
+# loop over time steps
+for time in ['05T1000','05T1030','05T1100','05T1130','05T1200','05T1230','05T1300','05T1330','05T1400']:
+    # loop over solvers
+    for solver in ['mysti','mcipa']:
+        # loop over sources (thermal/solar)
+        for source in ['thermal','solar']:
+            print('working on datasets for:', time+'/'+solver+'/'+source)
+            # loop over subdomains
+            thr1_3d=[]
+            # list holding names of the files for clear-sky results
+            sims2=[]
+            for dom in range(1,37): 
+                # list conatining paths to the results
+                simpath = []
+                # list holding names of the files
+                sims=[]
+                
+                #----------
+                siim = solvers_name[k]+'_'+time+'_'+str(dom)+'_isim01'
+                sims2.append(siim)
+                #----------
+                # loop over number of simulations
+                for nsim in ['01','02','03','04','05','06','07','08','09','10']:
+                    sim = solver+'_'+time+'_'+str(dom)+'_isim'+nsim
+                    simpath.append(path+'c_cluster_'+source+'_mystic/'+sim+'/')
+                    sims.append(sim)
+          
+                # empty list to store heating rates
+                thr_3d = []
+                for i in range (len(simpath)):
+                    print('****loading ascii file:',sims[i])
+                    hr = (pd.read_table(simpath[i]+'output/'+sims[i]+'.out.abs.spc',header=None,sep='\s+',usecols=[4]).values)[:,0]
+                # change to numpy array    
+                tmpp = np.array(thr_3d)
+                # take a mean over 10 simulations
+                temp = (tmpp[0]+tmpp[1]+tmpp[2]+tmpp[3]+tmpp[4]+tmpp[5]+tmpp[6]+tmpp[7]+tmpp[8]+tmpp[9])/10
+                #print('****creating 3d structure')
+                temp1 = np.zeros((nx, ny, nz))
+                for i in range(nz):
+                    temp1[:,:,i] = np.reshape(temp[nx*ny*i:nx*ny*i+nx*ny],(nx,ny))
+                # cutting boundaries
+                temp2 = temp1[38:420-38,31:343-31,:]    
+                thr1_3d.append(temp2*1.4) #  *cp/cv factor in ICON
+                del tmpp
+
+            # loading clear sky heating rates
+            cshr = []
+            print('****loading clear-sky hr')
+            for i in range (len(sims2)):
+                cshr_t = (pd.read_table(path+source+'_clear_sky/output/'+sims2[i].replace('isim'+nsim,'cs_hr')+'.out',header=None,sep='\s+',usecols=[1]).values)[:,0]
+                tmp = cshr_t*1.4 # x cp/cv factor in ICON 
+                # broadcasting to 3D structure
+                tmp2 = np.broadcast_to(tmp,(nx2,ny2,nz)).copy()
+                cshr.append(tmp2)
+                
+            print('****create dataset')
+            # creating datasets
+
+            ds = xr.Dataset({
+            #'half_levels': xr.DataArray(z1d, dims=('height')),
+            'ddt_dom1': xr.DataArray(thr1_3d[0], dims=('lon','lat','height')),
+            'ddt_dom2': xr.DataArray(thr1_3d[1], dims=('lon','lat','height')),
+            'ddt_dom3': xr.DataArray(thr1_3d[2], dims=('lon','lat','height')),
+            'ddt_dom4': xr.DataArray(thr1_3d[3], dims=('lon','lat','height')),
+            'ddt_dom5': xr.DataArray(thr1_3d[4], dims=('lon','lat','height')),
+            'ddt_dom6': xr.DataArray(thr1_3d[5], dims=('lon','lat','height')),
+            'ddt_dom7': xr.DataArray(thr1_3d[6], dims=('lon','lat','height')),
+            'ddt_dom8': xr.DataArray(thr1_3d[7], dims=('lon','lat','height')),
+            'ddt_dom9': xr.DataArray(thr1_3d[8], dims=('lon','lat','height')),
+            'ddt_dom10': xr.DataArray(thr1_3d[9], dims=('lon','lat','height')),
+            'ddt_dom11': xr.DataArray(thr1_3d[10], dims=('lon','lat','height')),
+            'ddt_dom12': xr.DataArray(thr1_3d[11], dims=('lon','lat','height')),
+            'ddt_dom13': xr.DataArray(thr1_3d[12], dims=('lon','lat','height')),
+            'ddt_dom14': xr.DataArray(thr1_3d[13], dims=('lon','lat','height')),
+            'ddt_dom15': xr.DataArray(thr1_3d[14], dims=('lon','lat','height')),
+            'ddt_dom16': xr.DataArray(thr1_3d[15], dims=('lon','lat','height')),
+            'ddt_dom17': xr.DataArray(thr1_3d[16], dims=('lon','lat','height')),
+            'ddt_dom18': xr.DataArray(thr1_3d[17], dims=('lon','lat','height')),
+            'ddt_dom19': xr.DataArray(thr1_3d[18], dims=('lon','lat','height')),
+            'ddt_dom20': xr.DataArray(thr1_3d[19], dims=('lon','lat','height')),
+            'ddt_dom21': xr.DataArray(thr1_3d[20], dims=('lon','lat','height')),
+            'ddt_dom22': xr.DataArray(thr1_3d[21], dims=('lon','lat','height')),
+            'ddt_dom23': xr.DataArray(thr1_3d[22], dims=('lon','lat','height')),
+            'ddt_dom24': xr.DataArray(thr1_3d[23], dims=('lon','lat','height')),
+            'ddt_dom25': xr.DataArray(thr1_3d[24], dims=('lon','lat','height')),
+            'ddt_dom26': xr.DataArray(thr1_3d[25], dims=('lon','lat','height')),
+            'ddt_dom27': xr.DataArray(thr1_3d[26], dims=('lon','lat','height')),
+            'ddt_dom28': xr.DataArray(thr1_3d[27], dims=('lon','lat','height')),
+            'ddt_dom29': xr.DataArray(thr1_3d[28], dims=('lon','lat','height')),
+            'ddt_dom30': xr.DataArray(thr1_3d[29], dims=('lon','lat','height')),
+            'ddt_dom31': xr.DataArray(thr1_3d[30], dims=('lon','lat','height')),
+            'ddt_dom32': xr.DataArray(thr1_3d[31], dims=('lon','lat','height')),
+            'ddt_dom33': xr.DataArray(thr1_3d[32], dims=('lon','lat','height')),
+            'ddt_dom34': xr.DataArray(thr1_3d[33], dims=('lon','lat','height')),
+            'ddt_dom35': xr.DataArray(thr1_3d[34], dims=('lon','lat','height')),
+            'ddt_dom36': xr.DataArray(thr1_3d[35], dims=('lon','lat','height')),
+
+            'ddt_cs_dom1': xr.DataArray(cshr[0], dims=('lon','lat','height')),
+            'ddt_cs_dom2': xr.DataArray(cshr[1], dims=('lon','lat','height')),
+            'ddt_cs_dom3': xr.DataArray(cshr[2], dims=('lon','lat','height')),
+            'ddt_cs_dom4': xr.DataArray(cshr[3], dims=('lon','lat','height')),
+            'ddt_cs_dom5': xr.DataArray(cshr[4], dims=('lon','lat','height')),
+            'ddt_cs_dom6': xr.DataArray(cshr[5], dims=('lon','lat','height')),
+            'ddt_cs_dom7': xr.DataArray(cshr[6], dims=('lon','lat','height')),
+            'ddt_cs_dom8': xr.DataArray(cshr[7], dims=('lon','lat','height')),
+            'ddt_cs_dom9': xr.DataArray(cshr[8], dims=('lon','lat','height')),
+            'ddt_cs_dom10': xr.DataArray(cshr[9], dims=('lon','lat','height')),
+            'ddt_cs_dom11': xr.DataArray(cshr[10], dims=('lon','lat','height')),
+            'ddt_cs_dom12': xr.DataArray(cshr[11], dims=('lon','lat','height')),
+            'ddt_cs_dom13': xr.DataArray(cshr[12], dims=('lon','lat','height')),
+            'ddt_cs_dom14': xr.DataArray(cshr[13], dims=('lon','lat','height')),
+            'ddt_cs_dom15': xr.DataArray(cshr[14], dims=('lon','lat','height')),
+            'ddt_cs_dom16': xr.DataArray(cshr[15], dims=('lon','lat','height')),
+            'ddt_cs_dom17': xr.DataArray(cshr[16], dims=('lon','lat','height')),
+            'ddt_cs_dom18': xr.DataArray(cshr[17], dims=('lon','lat','height')),
+            'ddt_cs_dom19': xr.DataArray(cshr[18], dims=('lon','lat','height')),
+            'ddt_cs_dom20': xr.DataArray(cshr[19], dims=('lon','lat','height')),
+            'ddt_cs_dom21': xr.DataArray(cshr[20], dims=('lon','lat','height')),
+            'ddt_cs_dom22': xr.DataArray(cshr[21], dims=('lon','lat','height')),
+            'ddt_cs_dom23': xr.DataArray(cshr[22], dims=('lon','lat','height')),
+            'ddt_cs_dom24': xr.DataArray(cshr[23], dims=('lon','lat','height')),
+            'ddt_cs_dom25': xr.DataArray(cshr[24], dims=('lon','lat','height')),
+            'ddt_cs_dom26': xr.DataArray(cshr[25], dims=('lon','lat','height')),
+            'ddt_cs_dom27': xr.DataArray(cshr[26], dims=('lon','lat','height')),
+            'ddt_cs_dom28': xr.DataArray(cshr[27], dims=('lon','lat','height')),
+            'ddt_cs_dom29': xr.DataArray(cshr[28], dims=('lon','lat','height')),
+            'ddt_cs_dom30': xr.DataArray(cshr[29], dims=('lon','lat','height')),
+            'ddt_cs_dom31': xr.DataArray(cshr[30], dims=('lon','lat','height')),
+            'ddt_cs_dom32': xr.DataArray(cshr[31], dims=('lon','lat','height')),
+            'ddt_cs_dom33': xr.DataArray(cshr[32], dims=('lon','lat','height')),
+            'ddt_cs_dom34': xr.DataArray(cshr[33], dims=('lon','lat','height')),
+            'ddt_cs_dom35': xr.DataArray(cshr[34], dims=('lon','lat','height')),
+            'ddt_cs_dom36': xr.DataArray(cshr[35], dims=('lon','lat','height')),
+
+            },
+            coords={"lat": (["lat"], np.arange(0,ny2)), 
+                            "lon": (["lon"], np.arange(0,nx2)),
+                            'height':(["height"],np.arange(0,nz)),})
+
+            # let's merge them together here
+            print('****merging subdomains into the big domain')
+            ds_j = []
+            ds_j2 = []
+            # concat along x dim
+            for j in range(1,37,6):
+                ds_i = xr.concat([ds['ddt_dom'+str(j)+''],ds['ddt_dom'+str(j+1)+''],
+                                  ds['ddt_dom'+str(j+2)+''],ds['ddt_dom'+str(j+3)+''],
+                                  ds['ddt_dom'+str(j+4)+''],ds['ddt_dom'+str(j+5)+'']],dim='lon')
+                ds_j.append(ds_i)
+
+                ds_i2 = xr.concat([ds['ddt_cs_dom'+str(j)+''],ds['ddt_cs_dom'+str(j+1)+''],
+                                  ds['ddt_cs_dom'+str(j+2)+''],ds['ddt_cs_dom'+str(j+3)+''],
+                                  ds['ddt_cs_dom'+str(j+4)+''],ds['ddt_cs_dom'+str(j+5)+'']],dim='lon')
+                ds_j2.append(ds_i2)
+            # concat along y dim    
+            ds_f1 = xr.concat([ds_j[0],ds_j[1],ds_j[2],ds_j[3],ds_j[4],ds_j[5]],dim='lat')
+            ds_f2 = xr.concat([ds_j2[0],ds_j2[1],ds_j2[2],ds_j2[3],ds_j2[4],ds_j2[5]],dim='lat')
+            ds_1 = xr.merge([ds_f1,ds_f2])
+            ds_1['ddt_radlw'] = ds_1['ddt_dom1'] - ds_1['ddt_cs_dom1']
+            ds_1.coords['lon'] = np.arange(0,344*6)
+            ds_1.coords['lat'] = np.arange(0,281*6)
+            ds_1 = ds_1.expand_dims('time')
+            print('****save')
+            ds_1.to_netcdf('/work/bb1135/icon_output/'+simm+'/input4libradtran/libradtran/output_netcdf/ds_librad_'+time+'_'+source+'_'+solver+'_01.nc')
+            print('----------------------------------------------------------------------------')
+
+print('finished')            
diff --git a/offlineRT/estimating_MC_noise_of_MYSTIC.py b/offlineRT/estimating_MC_noise_of_MYSTIC.py
new file mode 100644
index 0000000000000000000000000000000000000000..cf32e1c5c99b7bfcd05b2fb01113d918c8f39c26
--- /dev/null
+++ b/offlineRT/estimating_MC_noise_of_MYSTIC.py
@@ -0,0 +1,105 @@
+#@ Behrooz Keshtgar, KIT 2024
+
+# This script is for estimating the Monte Carlo noise of the
+# MYSTIC solver
+
+#### Loading libraries
+import numpy as np
+import matplotlib.pyplot as plt
+import xarray as xr
+
+# function to load 10 mystic calculations for the WCB anticyclonic outflow domain at local hour 14:30
+def load_datasets(source):
+    ds_list = []
+    path = '/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/offline_radiation_calculation_output/lem_crhun_diag/ds_for_calc_mc_noise/'
+    for nsim in ['01','02','03','04','05','06','07','08','09','10']:
+        ds = xr.open_dataset(path+'dom_01_ds_librad_05T1200_'+source+'_mysti_'+nsim+'.nc',
+                             chunks={'height': 1}).isel(height=slice(0,140))
+        ds.attrs['sim_name'] = 'dom_01_ds_librad_05T1200_solar_mysti_'+nsim+''
+        ds_list.append(ds)
+    return ds_list
+
+ds_lib_solar = load_datasets('solar')
+ds_lib_thermal = load_datasets('thermal')
+
+
+# Split the ds_lib into two parts, each containing 5 datasets
+ds_lib_solar_1 = ds_lib_solar[:5]
+ds_lib_solar_2 = ds_lib_solar[5:]
+
+ds_lib_thermal_1 = ds_lib_thermal[:5]
+ds_lib_thermal_2 = ds_lib_thermal[5:]
+
+# take the mean over 5 RT calculations
+ds_lib_solar_1_mean = xr.concat(ds_lib_solar_1, dim='combine').mean('combine')
+ds_lib_solar_2_mean = xr.concat(ds_lib_solar_2, dim='combine').mean('combine')
+
+ds_lib_thermal_1_mean = xr.concat(ds_lib_thermal_1, dim='combine').mean('combine')
+ds_lib_thermal_2_mean = xr.concat(ds_lib_thermal_2, dim='combine').mean('combine')
+
+# shortwave
+set1_sw = (ds_lib_solar_1_mean['ddt_dom1'].isel(time=0))
+set2_sw = (ds_lib_solar_2_mean['ddt_dom1'].isel(time=0))
+
+# longwave
+set1_lw = (ds_lib_thermal_1_mean['ddt_dom1'].isel(time=0))
+set2_lw = (ds_lib_thermal_2_mean['ddt_dom1'].isel(time=0))
+
+# net
+set1_nt = set1_sw + set1_lw
+set2_nt = set2_sw + set2_lw
+
+# function to calculate the rsd between two mystic calculation
+def calc_rsd(data1,data2):
+    # Stack the DataArrays along a new dimension ('combine')
+    combined_data = xr.concat([data1, data2], dim='new')
+
+    # Calculate mean, standard deviation
+    mean_values = combined_data.mean(dim='new')
+    std_values = combined_data.std(dim='new')
+
+    # Calculate the Relative Standard Deviation (RSD) at each pixel
+    rsd_values = ((std_values *100) / np.abs(mean_values)).isel(height=slice(0,80))
+    
+    # load, flatten and get rid of Nan values
+    noise = rsd_values.values.flatten()
+    noise = noise[~np.isnan(noise)]
+    
+    # Calculate quartiles, IQR, and bounds to check how many data points falls within the box plot (certain data) i.e.,
+    # let's find  what percentage of this noise over all grid boxes are certain. 
+    Q1 = np.percentile(noise, 25)
+    Q3 = np.percentile(noise, 75)
+    IQR = Q3 - Q1
+    lower_bound = Q1 - 1.5 * IQR
+    upper_bound = Q3 + 1.5 * IQR
+
+    # Count data points within the box
+    points_within_box = ((noise >= lower_bound) & (noise <= upper_bound)).sum()
+    # Calculate percentage of the data
+    perc = (points_within_box/noise.size)*100
+    
+    return noise, perc
+
+sw_noise, sw_noise_perc = calc_rsd(set1_sw,set2_sw)
+lw_noise, lw_noise_perc = calc_rsd(set1_lw,set2_lw)
+nt_noise, nt_noise_perc = calc_rsd(set1_nt,set2_nt)
+
+# Plot
+
+# Create sample data (replace with your data)
+data = pd.DataFrame({'Shortwave': sw_noise,'Longwave': lw_noise,'Net': nt_noise})
+
+# Visualize the PRSD using a box plot
+plt.figure(figsize=(8, 6))
+plt.tick_params(labelsize=14)
+
+sns.set(style="whitegrid")
+sns.boxplot(data=data,palette='Set3',showfliers=False)
+plt.title('Monte carlo noise for shallow cumulus clouds',fontsize=14)
+plt.ylabel('Relative standard deviation (%)',fontsize=14)
+plt.ylim(0,40)
+
+plt.legend(title='Percentage of certain data points', labels=['Shortwave: '+str(np.round(sw_noise_perc))+'%',
+                                                              'Longwave: '+str(np.round(lw_noise_perc))+'%',
+                                                              'Net: '+str(np.round(nt_noise_perc))+'%'])
+plt.savefig('Fig3_4.png', bbox_inches = 'tight',dpi=300) 
diff --git a/offlineRT/input_for_libradtran.ipynb b/offlineRT/input_for_libradtran.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..93de53b01e9b96f339482f4f7deffb6b3b250cbe
--- /dev/null
+++ b/offlineRT/input_for_libradtran.ipynb
@@ -0,0 +1,1114 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "-*- coding: iso-8859-15 -*-\n",
+    "\n",
+    "   ## I C O N 2 M Y S T I C\n",
+    "   \n",
+    "   ## Generating input files for LibRadTran from ICON-LEM output files\n",
+    "   \n",
+    "   Original code by Leonard Scheck (LMU), 2021.1\n",
+    "   \n",
+    "   Modified by Behrooz Keshtgar (KIT), 2023:\n",
+    "   \n",
+    "   + Convert the standalone Python script into an interactive Jupyter notebook and include only the necessary functions.\n",
+    "   + Generate atmospheric background profiles from ICON-LEM output files\n",
+    "   + Generate NWP homogeneous grid-box clouds and homogeneous clouds with cloud fraction from LEM clouds at the specified resolution\n",
+    "   + Derive atmospheric gas concentration profiles following the routines implemented in the ICON model\n",
+    "   + Remap ICON output from triangular grid to regular lat/lon grid and save them as Netcdf files\n",
+    "\n",
+    "----------------------------------------------------------   \n",
+    "Required inputs to run this Jupyter notebook are \n",
+    "\n",
+    "+ ICON grid file\n",
+    "+ ICON output files including cloud water and ice mass content and their effective radii (tot_qc_dia,tot_qi_dia,reff_qc,reff_qi)\n",
+    "+ ICON output files including atmospheric variables: temperature, pressure, density, ozone density, specific humidity, height and pressure at half levels (temp,pres,rho,O3,qv,z_ifc,pres_ifc)\n",
+    "+ Other variables from ICON simulation including solar zenith angle (sza) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os, sys, time, pickle, netCDF4\n",
+    "from time import perf_counter\n",
+    "from matplotlib import pyplot as plt\n",
+    "import numpy as np\n",
+    "from numba import jit, float64, float32, int32\n",
+    "import xarray as xr\n",
+    "import os\n",
+    "from scipy import interpolate\n",
+    "import pandas as pd\n",
+    "import matplotlib as mpl\n",
+    "import math"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Constants"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# these values are extracted from ICON, gas concentrations values are those used in the baroclinic life cycle simulation\n",
+    "vpp_ch4=np.array([1.25e-01,  683.0, -1.43])\n",
+    "vpp_n2o=np.array([1.20e-02, 1395.0, -1.43])\n",
+    "\n",
+    "vmr_ch4 = 1650.0e-09\n",
+    "vmr_n2o = 396.0e-09\n",
+    "\n",
+    "vmr_o2  = 0.20946  # Volume Mixing Ratio (mol mol–1), mol/mol *1e6 -> ppm\n",
+    "vmr_co2 = 348.0e-6 # 0.000348 mol/mol : (348 ppmv)\n",
+    "\n",
+    "# we need to change their unit to cm**-3\n",
+    "# I used this converter https://www.lenntech.com/calculators/ppm/converter-parts-per-million.htm to convert them\n",
+    "o2  = 2.95*1e-1  # kg/m3\n",
+    "co2 = 647*1e-6   # kg/m3\n",
+    "\n",
+    "R = 287.04 # J⋅kg−1⋅K−1 # dry gas heat constant from ICON\n",
+    "k = 1.38064852*1e-23 #m2 kg s-2 K-1 #stephan-Boltzman constant\n",
+    "\n",
+    "''' # From ICON-ecRad\n",
+    "! Molar masses (g mol-1) of dry air and the various gases above\n",
+    "  real(jprb), parameter :: IAirMolarMass = 28.970\n",
+    "  real(jprb), parameter, dimension(0:NMaxGases) :: IGasMolarMass = (/ &\n",
+    "       & 0.0_jprb,        & ! Gas not present\n",
+    "       & 18.0152833_jprb, & ! H2O\n",
+    "       & 44.011_jprb,     & ! CO2\n",
+    "       & 47.9982_jprb,    & ! O3\n",
+    "       & 44.013_jprb,     & ! N2O\n",
+    "       & 28.0101_jprb,    & ! CO\n",
+    "       & 16.043_jprb,     & ! CH4\n",
+    "       & 31.9988_jprb,    & ! O2\n",
+    "       & 137.3686_jprb,   & ! CFC11\n",
+    "       & 120.914_jprb,    & ! CFC12\n",
+    "       & 86.469_jprb,     & ! HCFC22\n",
+    "       & 153.823_jprb,    & ! CCl4\n",
+    "       & 46.0055_jprb /)    ! NO2\n",
+    "'''       \n",
+    "avo   = 6.02214179e23 # !> [1/mo]    Avogadro constant\n",
+    "\n",
+    "m_d   = 4.810580854822417e-26  # molecular mass of dry air kg\n",
+    "m_o3  = 7.970287262200115e-26  # molecular mass of ozone kg\n",
+    "m_h2o = 2.991508172585458e-26  # molecular mass of h2o kg\n",
+    "m_co2 = 7.308197238577473e-26  # molecular mass of co2 kg\n",
+    "m_o2  = 5.3135241466744e-26    # molecular mass of o2 kg\n",
+    "\n",
+    "# density of water and ice from ICON\n",
+    "rhoh2o =  1000  # kg/m³\n",
+    "rhoice =  916.7 # kg/m³"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ICON routine for interpolating a variable from full level to half level\n",
+    "def interpolate_hl(var1,var2,var3):\n",
+    "    var_hl = var3*0.0\n",
+    "    for jk in range(1,150):\n",
+    "        var_hl[jk,:] = (var1[jk-1,:] * var2[jk-1,:]  * ( var2[jk,:] - var3[jk,:] ) + \n",
+    "                   var1[jk,:] * var2[jk,:] * ( var3[jk,:] - var2[jk-1,:])) / ( var3[jk,:] * (var2[jk,:] - var2[jk-1,:] ) )\n",
+    "        \n",
+    "    var_hl[150,:] = var1[149,:] + (var3[150,:] - var2[149,:]) * (var1[149-1,:] - var1[149,:])/(var2[149-1,:] - var2[149,:])\n",
+    "    \n",
+    "    var_hl[0,:] = var1[0,:] + ( var3[0,:] - var2[0,:] ) * (var1[0,:] - var_hl[1,:]) / (var2[0,:] - var3[1,:] )\n",
+    "        \n",
+    "    return var_hl\n",
+    "\n",
+    "# ICON routine for deriving gas profile according to a tanh profile\n",
+    "def gas_profile(vmr_gas,pres,xp):\n",
+    "    gas_pro = pres*0.0\n",
+    "    zx_m = (vmr_gas+xp[0]*vmr_gas)*0.5\n",
+    "    zx_d = (vmr_gas-xp[0]*vmr_gas)*0.5\n",
+    "    \n",
+    "    gas_pro = (1.-(zx_d/zx_m)*np.tanh(np.log(pres/xp[1]) /xp[2])) * zx_m\n",
+    "    \n",
+    "    return gas_pro\n",
+    "#-----------------------------------------------------------------------------------------------------------\n",
+    "# function for writing ascii files without cloud fraction\n",
+    "def write_mc_cloud_file( beta, reff, dx, dy, zcoord, fname='wc3d.dat', flag=2 ) :\n",
+    "    \"\"\"Write 3D extinction coefficient + effective radius file in libradtran mc_albedo_ascii file format\"\"\"\n",
+    "\n",
+    "    print('write_mc_cloud_file', fname, beta.max(), reff.max(), beta.shape, dx, dy, zcoord[1]-zcoord[0])\n",
+    "\n",
+    "    # flag = 2 --> beta [1/km],          reff [micron] are supplied for each cell\n",
+    "    # flag = 3 --> water content [g/m3], reff [micron] are supplied for each cell\n",
+    "    \n",
+    "    a = np.swapaxes(beta, 0, 1)\n",
+    "    b = np.swapaxes(reff, 0, 1)    \n",
+    "    nx, ny, nz = a.shape #lon,lat,lev\n",
+    "    with open(fname,'w') as f:\n",
+    "        f.write(\"%d %d %d %d\\n\" % (nx,ny,nz,flag))\n",
+    "        f.write(\"%f %f \" % (dx/1e3,dy/1e3))\n",
+    "        f.write( ' '.join([ '%f'%z for z in zcoord/1e3 ]) + '\\n' )\n",
+    "        \n",
+    "        ## For radiative transfer with MYSTIC, it would be okay to skip the points in the domain where there is no cloud.\n",
+    "        \n",
+    "        #f.writelines( [ (\"%d %d %d %f %f\\n\" % (i+1,j+1,k+1,a[i,j,k],b[i,j,k]) if beta[j,i,k] > 1e-9 else '') \\\n",
+    "        #                for i in range(nx) for j in range(ny) for k in range(nz) ] )\n",
+    "        \n",
+    "        #f.writelines( [ (\"%d %d %d %e %d\\n\" % (1,1,k+1,1e-20,20)) \\\n",
+    "        #                for k in range(nz) ] )\n",
+    "        \n",
+    "        ## Keeping everything  \n",
+    "        f.writelines( [ (\"%d %d %d %f %f\\n\" % (i+1,j+1,k+1,a[i,j,k],b[i,j,k])) \\\n",
+    "                        for i in range(nx) for j in range(ny) for k in range(nz) ] )\n",
+    "\n",
+    "# function for writing ascii files with cloud fraction (Behrooz Keshtgar, KIT)       \n",
+    "def write_mc_cloud_file_frac(beta, reff, frac, dx, dy, zcoord, fname='wc3d.dat', flag=2 ) : \n",
+    "    \"\"\"Write 3D extinction coefficient + effective radius file in libradtran mc_albedo_ascii file format\"\"\"\n",
+    "\n",
+    "    print('write_mc_cloud_file_frac')\n",
+    "\n",
+    "    a = np.swapaxes(beta, 0, 1)\n",
+    "    b = np.swapaxes(reff, 0, 1)\n",
+    "    c = np.swapaxes(frac, 0, 1)\n",
+    "    nx, ny, nz = a.shape #lon,lat,lev\n",
+    "    with open(fname,'w') as f:\n",
+    "        f.write(\"%d %d %d %d\\n\" % (nx,ny,nz,flag))\n",
+    "        f.write(\"%f %f \" % (dx/1e3,dy/1e3))\n",
+    "        f.write( ' '.join([ '%f'%z for z in zcoord/1e3 ]) + '\\n' )\n",
+    "        \n",
+    "        f.writelines( [ (\"%d %d %d %f %f %f\\n\" % (i+1,j+1,k+1,a[i,j,k],b[i,j,k],c[i,j,k])) \\\n",
+    "                        for i in range(nx) for j in range(ny) for k in range(nz) ] )\n",
+    "        \n",
+    "def get_subdomain( lat_min, lat_max, lon_min, lon_max, gridfile, datafile1, datafile2, datafile3, datafile4) :\n",
+    "    \"\"\"\n",
+    "    Return model variables and grid for the cells with cells centers in the specified lat-lon rectangle\n",
+    "    :param lat_min:  minimum latitude  [rad]\n",
+    "    :param lat_max:  maximum latitude  [rad]\n",
+    "    :param lon_min:  minimum longitude [rad]\n",
+    "    :param lon_max:  maximum longitude [rad]\n",
+    "    :param args:     argparse object\n",
+    "    :param verbose:  be more verbose\n",
+    "    :return:         grid dictionary, model variables dictionary\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # determine subdomain grid .........................................................................................\n",
+    "\n",
+    "    print('    [icon_subdomain.get_subdomain_data] opening horizontal grid file %s...')\n",
+    "    #grid_full = netCDF4.Dataset( args.gridfile,'r')\n",
+    "    grid_full = gridfile\n",
+    "    #if args.verbose :\n",
+    "    print('    full model grid : %f < lon <%f, %f < lat < %f' % (\n",
+    "    np.array(grid_full.variables['vlon']).min()*180/np.pi, np.array(grid_full.variables['vlon']).max()*180/np.pi,\n",
+    "    np.array(grid_full.variables['vlat']).min()*180/np.pi, np.array(grid_full.variables['vlat']).max()*180/np.pi ))\n",
+    "\n",
+    "    print('*** constructing subdomain grid'); starttime = perf_counter()\n",
+    "\n",
+    "    nvertices_full = len(grid_full.dimensions['vertex'])\n",
+    "    ncells_full    = len(grid_full.dimensions['cell'])\n",
+    "\n",
+    "    cell_indices             = np.zeros( ncells_full,    dtype=np.int32 ) - 1\n",
+    "    vertex_indices           = np.zeros( nvertices_full, dtype=np.int32 ) - 1\n",
+    "    translate_cell_indices   = np.zeros( ncells_full,    dtype=np.int32 ) - 1\n",
+    "    translate_vertex_indices = np.zeros( nvertices_full, dtype=np.int32 ) - 1\n",
+    "\n",
+    "    print('    determining subdomain indices for %f <= lat < %f, %f <= lon < %f' % (lat_min*180/np.pi, lat_max*180/np.pi, lon_min*180/np.pi, lon_max*180/np.pi))\n",
+    "\n",
+    "    clon                = np.array(grid_full.variables['clon'])\n",
+    "    clat                = np.array(grid_full.variables['clat'])\n",
+    "    vlon                = np.array(grid_full.variables['vlon'])\n",
+    "    vlat                = np.array(grid_full.variables['vlat'])\n",
+    "    cell_area           = np.array(grid_full.variables['cell_area'])\n",
+    "    vertex_of_cell      = np.array(grid_full.variables['vertex_of_cell'])      - 1\n",
+    "    neighbor_cell_index = np.array(grid_full.variables['neighbor_cell_index']) - 1\n",
+    "    cells_of_vertex      = np.array(grid_full.variables['cells_of_vertex'])      - 1\n",
+    "    ncells, nvertices = subdomain_indices( clon, clat, vlon, vlat, vertex_of_cell,\n",
+    "                                           lat_min, lat_max, lon_min, lon_max,\n",
+    "                                           cell_indices, vertex_indices, translate_cell_indices, translate_vertex_indices )\n",
+    "    print('    subdomain contains %d of %d cells and %d of %d vertices' % ( ncells, ncells_full, nvertices, nvertices_full ))\n",
+    "    print('    sqrt(area) of first cell : {:.0f}m'.format(np.sqrt(cell_area[0])))\n",
+    "\n",
+    "    print('    converting horizontal grid...')\n",
+    "    grid = dict()\n",
+    "    grid['ncells']    = ncells\n",
+    "    grid['nvertices'] = nvertices\n",
+    "    grid['clat'] = clat[cell_indices[:ncells]]\n",
+    "    grid['clon'] = clon[cell_indices[:ncells]]\n",
+    "    grid['cell_area'] = cell_area[cell_indices[:ncells]]\n",
+    "    grid['vlat'] = vlat[vertex_indices[:nvertices]]\n",
+    "    grid['vlon'] = vlon[vertex_indices[:nvertices]]\n",
+    "    grid['neighbor_cell_index'] = translate_cell_indices[ neighbor_cell_index[:,cell_indices[:ncells]] ]\n",
+    "    grid['vertex_of_cell']      = translate_vertex_indices[ vertex_of_cell[:,cell_indices[:ncells]] ]\n",
+    "    grid['cells_of_vertex']      = translate_cell_indices[ cells_of_vertex[:,vertex_indices[:nvertices]] ]\n",
+    "    grid['full_grid_cell_indices'] = cell_indices[:ncells]\n",
+    "    print('*** subdomain grid construction took %f seconds' % (perf_counter() - starttime))\n",
+    "     \n",
+    "    ########################################################################################\n",
+    "    #modelstate1 = datafile1\n",
+    "    #modelstate2 = datafile2\n",
+    "    #print('    ...which is a NetCDF file...')\n",
+    "    #modelvars_available = list(modelstate1.variables.keys())\n",
+    "    #modelvars_available = modelvars_available+list(modelstate2.variables.keys()) #behrooz\n",
+    "    #nz_full = len(modelstate1.dimensions['height'])\n",
+    "    #ftype='netcdf'\n",
+    "    \n",
+    "    #########################################################################################\n",
+    "    num_file = [datafile1,datafile2,datafile3,datafile4]\n",
+    "    print('    opening model state files')\n",
+    "    if len(num_file) > 1 : # several files separated by ':' have been specified\n",
+    "        print('    ...which are actually several files...')\n",
+    "        #modelvarfiles = args.modelvarsfile.split(':')\n",
+    "        modelstates = []\n",
+    "        modelstates_variables = []\n",
+    "        modelvars_available = []\n",
+    "        for i in range(len(num_file)) :\n",
+    "            print('    --- opening file ', i, ' = ')\n",
+    "            modelstates.append(num_file[i])#(netCDF4.Dataset( mvf, 'r'))\n",
+    "            modelstates_variables.append(list(modelstates[-1].variables.keys()))\n",
+    "            print('                 which contains', modelstates_variables[-1])\n",
+    "            modelvars_available += modelstates_variables[-1]\n",
+    "\n",
+    "            if 'height' in modelstates[-1].dimensions :\n",
+    "                print('                 and has a dimension height of size', end=' ')\n",
+    "                nz_full = len(modelstates[-1].dimensions['height'])\n",
+    "                print(nz_full)\n",
+    "        modelstate = modelstates[0]\n",
+    "    #else :\n",
+    "    #    modelstate = netCDF4.Dataset( args.modelvarsfile, 'r')\n",
+    "    #    print('    ...which is a NetCDF file...')\n",
+    "    #    modelvars_available = list(modelstate.variables.keys())\n",
+    "    #    nz_full = len(modelstate.dimensions['height'])\n",
+    "    ftype='netcdf'\n",
+    "    \n",
+    "    ########\n",
+    "    print('    available variables : ', modelvars_available)\n",
+    "    print('    number of layers    : ', nz_full)\n",
+    "    #modelstates = None\n",
+    "    if modelstates is None : # only one model state file was specified\n",
+    "        modelstates = [ modelstate ]\n",
+    "        modelstates_variables = [ modelvars_available ]\n",
+    "\n",
+    "    # list available output times ......................................................................................\n",
+    "\n",
+    "    if ftype == 'netcdf' :\n",
+    "        outputtimes = modelstate.variables['time'][:]\n",
+    "        print('    output times available :')\n",
+    "        for ti, ot in enumerate(outputtimes) :\n",
+    "            ot_date = int(ot)\n",
+    "            ot_time = ot-ot_date\n",
+    "            ot_hour = int(ot_time*24.0)\n",
+    "            ot_min  = int((ot_time - ot_hour/24.0)*60.0)\n",
+    "            print('      (', ti, ') --- ',  ot_date, ot_hour, ot_min)\n",
+    "        print('    selected time index : ', 0)\n",
+    "    else :\n",
+    "        if 0 > 0 :\n",
+    "            raise ValueError( 'time index > 0 probably not yet supported for grib files...' )\n",
+    "\n",
+    "\n",
+    "    # read subdomain model variables ...................................................................................\n",
+    "    if grid['ncells'] <= 0 :\n",
+    "\n",
+    "        print('    subdomain does not contain any model grid cells... ', end=' ')\n",
+    "        modelvars = {}\n",
+    "\n",
+    "    else :\n",
+    "\n",
+    "        print('    extracting model data... ', end=' ')\n",
+    "\n",
+    "        # dimension check ..............................................................................................\n",
+    "\n",
+    "        if ftype == 'netcdf' :\n",
+    "            ncells_full_model    = len(modelstate.dimensions['ncells'])\n",
+    "            if ncells_full_model != ncells_full :\n",
+    "                print('ERROR: ncells mismatch between grid and model data', ncells_full_model, ncells_full)\n",
+    "        else :\n",
+    "            # we cannot check this easily for grib files -> assume everything is ok...\n",
+    "            ncells_full_model = ncells_full\n",
+    "\n",
+    "        # determine vertical part to be read ...........................................................................\n",
+    "\n",
+    "        nz = nz_full - 0\n",
+    "        #kl=0\n",
+    "        #kh=nz\n",
+    "        print('    using %d of %d layers... ' % ( nz, nz_full ))   \n",
+    "        \n",
+    "        # read variables ...............................................................................................\n",
+    "\n",
+    "        read_in_chunks = True # is faster...\n",
+    "\n",
+    "        modelvars = dict()\n",
+    "        #for v in ['z_ifc','tot_qc_dia','tot_qi_dia','rho','reff_qc_ecrad','reff_qi_ecrad','temp','pres','o3','tot_qv_dia',\n",
+    "        #          'ddt_temp_radlwnw','ddt_temp_radlwcs','ddt_temp_radswnw','ddt_temp_radswcs','pres_ifc','clc','cosz_bz','tsfctrad'] :\n",
+    "        for v in ['z_ifc','tot_qc_dia','tot_qi_dia','rho','reff_qc_ecrad','reff_qi_ecrad'] :    \n",
+    "            if '.' in v :\n",
+    "                vfile, vname = v.split('.')\n",
+    "                print('        - reading [{}->{}]'.format(vfile,vname), end=' ')\n",
+    "            else :\n",
+    "                vname = v\n",
+    "                vfile = v\n",
+    "                print('        - reading [{}]'.format(vname), end=' ')\n",
+    "\n",
+    "\n",
+    "            kl = 0\n",
+    "            if vname != 'z_ifc' :\n",
+    "                kh = kl + nz\n",
+    "            else :\n",
+    "                kh = kl + nz + 1\n",
+    "\n",
+    "            for istate in range(len(modelstates)) :\n",
+    "                if vfile in modelstates_variables[istate] or ftype == 'grib':\n",
+    "                    if len(modelstates) > 1 :\n",
+    "                        print('<file {}>'.format(istate), end=' ')\n",
+    "                    if ftype == 'netcdf' :\n",
+    "                        if read_in_chunks :\n",
+    "                            modelvars[vname] = read_part_of_variable( modelstates[istate], vfile, cell_indices[:ncells], time_index=0 )[:,...]\n",
+    "                        else :\n",
+    "                            modelvars[vname] = modelstates[istate].variables[vfile][0,:,cell_indices[:ncells]][:,...]\n",
+    "                    else :\n",
+    "                        if modelstates[istate][vfile].ndim > 1 :\n",
+    "                            modelvars[vname] = np.transpose( modelstates[istate][vfile][cell_indices[:ncells],:][...,:] )\n",
+    "                        else :\n",
+    "                            modelvars[vname] = np.transpose( modelstates[istate][vfile][cell_indices[:ncells]] )\n",
+    "\n",
+    "            if vname == 'CLC' and modelvars[vname].max() > 1:\n",
+    "                modelvars[vname] /= 100.0\n",
+    "\n",
+    "            print(' with shape ', modelvars[vname].shape)\n",
+    "\n",
+    "            \n",
+    "    print('    get_subdomain: done. ')    \n",
+    "\n",
+    "    return grid, modelvars      \n",
+    "    \n",
+    "def read_part_of_variable( modelvars_full, vname, ci, check=False, time_index=None, verbose=False ) :\n",
+    "\n",
+    "    if len(modelvars_full.variables[vname].shape) == 3 :\n",
+    "        has_timedim = True\n",
+    "    else :\n",
+    "        has_timedim = False\n",
+    "\n",
+    "    nlevels = modelvars_full.variables[vname].shape[-2]\n",
+    "    ncells = ci.size\n",
+    "    ncells_full = modelvars_full.variables[vname].shape[-1]\n",
+    "    modelvar_part = np.zeros( (nlevels,ncells) ) # omit leading time dimension    \n",
+    "\n",
+    "    starttime = perf_counter()\n",
+    "\n",
+    "    nchunks = 50\n",
+    "    ncells_chunk = ncells_full // nchunks\n",
+    "    for ic in range(nchunks) :\n",
+    "        index_min = ic*ncells_chunk\n",
+    "        index_max = (ic+1)*ncells_chunk\n",
+    "        if ic == nchunks-1 : index_max = ncells_full\n",
+    "\n",
+    "        idcs = np.where( (ci >= index_min) & (ci < index_max) )\n",
+    "        nrelevant = len(idcs[0])\n",
+    "        if verbose :\n",
+    "            print('   --- chunk %d [ %d <= index < %d ] : %d relevant' % (ic, index_min, index_max, nrelevant))\n",
+    "        if nrelevant > 0 :\n",
+    "            if has_timedim :\n",
+    "                chunk = np.array(modelvars_full.variables[vname][time_index,:,index_min:index_max])\n",
+    "            else :\n",
+    "                chunk = np.array(modelvars_full.variables[vname][:,index_min:index_max])\n",
+    "            chunk_idcs = np.array(ci[idcs],dtype=int)-index_min\n",
+    "            modelvar_part[:,np.asarray(idcs,dtype=int)[0,:]] = chunk[:,chunk_idcs]\n",
+    "\n",
+    "    print('   --- reading and distributing chunks took %f seconds...' % (perf_counter() - starttime))\n",
+    "\n",
+    "    if check :\n",
+    "        print('ok, checking...')\n",
+    "        if has_timedim :\n",
+    "            fullvar = np.array(modelvars_full.variables[vname][time_index,:,:])\n",
+    "        else :\n",
+    "            fullvar = np.array(modelvars_full.variables[vname][:,:])\n",
+    "        redvar = fullvar[:,ci]\n",
+    "        print('DEVIATION ', (modelvar_part-redvar).min(), (modelvar_part-redvar).max())\n",
+    "        fullvar = ''\n",
+    "        redvar = ''\n",
+    "\n",
+    "    return modelvar_part    \n",
+    "\n",
+    "@jit(nopython=True,nogil=True)\n",
+    "def subdomain_indices( clon, clat, vlon, vlat, vertex_of_cell, lat_min, lat_max, lon_min, lon_max,\n",
+    "                       cell_indices, vertex_indices, translate_cell_indices, translate_vertex_indices ) :\n",
+    "    \"\"\"Save the indices of the cells whose center lies within the given region\n",
+    "       and the indices of vertices forming these cells in cell_indices and vertex_indices.\n",
+    "       Returns number of cells and number of vertices.\"\"\"\n",
+    "\n",
+    "    icell = 0\n",
+    "    for i in range(clon.size) :\n",
+    "        if (clon[i] >= lon_min) and (clon[i] < lon_max) and (clat[i] >= lat_min) and (clat[i] < lat_max) :\n",
+    "          cell_indices[icell] = i\n",
+    "          translate_cell_indices[i] = icell\n",
+    "          icell += 1\n",
+    "          for ii in range(3) :\n",
+    "              if vertex_of_cell[ii,i] > -1 :\n",
+    "                  vertex_indices[ vertex_of_cell[ii,i] ] = 1 # mark as required\n",
+    "\n",
+    "    ivertex = 0\n",
+    "    for i in range(vlon.size) :\n",
+    "        if vertex_indices[i] > 0 :\n",
+    "            vertex_indices[ivertex] = i\n",
+    "            translate_vertex_indices[i] = ivertex\n",
+    "            ivertex += 1\n",
+    "\n",
+    "    return icell, ivertex\n",
+    "\n",
+    "def generate_latlon_grid( lat_min, lon_min, lat_max, lon_max, nlat, nlon, dim=1, first_dim='lon' ) :\n",
+    "    \"\"\"Generate regular latitude-longitude grid with the specified limits and resolution\"\"\"\n",
+    "\n",
+    "    r =    { 'lat_min':lat_min, 'lon_min':lon_min, 'lat_max':lat_max, 'lon_max':lon_max, 'nlat':nlat, 'nlon':nlon,\n",
+    "             'dlat':(lat_max-lat_min)/nlat, 'dlon':(lon_max-lon_min)/nlon,\n",
+    "             'lat':lat_min + (lat_max-lat_min)*np.arange(nlat+1)/float(nlat),\n",
+    "             'lon':lon_min + (lon_max-lon_min)*np.arange(nlon+1)/float(nlon) }\n",
+    "\n",
+    "    if dim == 2 : # create also coordinate 2d-fields\n",
+    "        if first_dim == 'lon' :\n",
+    "            lon2d, lat2d = np.meshgrid( r['lon'], r['lat'], sparse=False, indexing='ij' )\n",
+    "            # lon changes with first index, lat with second\n",
+    "            #print 'TESTLATLON lon2d 1 ', lon2d[0,0], lon2d[1,0], lon2d[0,1]\n",
+    "            #print 'TESTLATLON lat2d 1 ', lat2d[0,0], lat2d[1,0], lat2d[0,1]\n",
+    "        elif first_dim == 'lat' :\n",
+    "            lat2d, lon2d = np.meshgrid( r['lat'], r['lon'], sparse=False, indexing='ij' )\n",
+    "            # lat changes with first index, lon with second\n",
+    "            #print 'TESTLATLON lon2d 2 ', lon2d[0,0], lon2d[1,0], lon2d[0,1]\n",
+    "            #print 'TESTLATLON lat2d 2 ', lat2d[0,0], lat2d[1,0], lat2d[0,1]\n",
+    "        else :\n",
+    "            raise ValueError('generate_latlon_grid: I do not understand first_dim='+first_dim)\n",
+    "\n",
+    "        r.update({ 'lon2d':lon2d, 'lat2d':lat2d })\n",
+    "\n",
+    "    return r\n",
+    "\n",
+    "def tri2latlon( ll_grid, tri_grid, tri_var, method='fine', silent=False, nsearch=3, oversample=1 ) :\n",
+    "    \"\"\"\n",
+    "    Map variable defined on triangular grid onto regular lat-lon grid.\n",
+    "    The lat_min, lon_min values ll_grid correspond to the cell centers.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    nlat, nlon = ll_grid['nlat'], ll_grid['nlon']\n",
+    "    latlon_var = np.zeros((nlat, nlon),dtype=np.float64)\n",
+    "        \n",
+    "    if oversample == 1 :\n",
+    "        latlon_hits = np.zeros((nlat, nlon),dtype=np.int32)\n",
+    "        misses = tri2latlon_fine( latlon_var, latlon_hits,\n",
+    "                                ll_grid['lat_min'], ll_grid['lon_min'], ll_grid['dlat'], ll_grid['dlon'],\n",
+    "                                tri_var.astype(np.float64), tri_grid['clat'].astype(np.float64), tri_grid['clon'].astype(np.float64),\n",
+    "                                tri_grid['vlat'].astype(np.float64), tri_grid['vlon'].astype(np.float64), tri_grid['vertex_of_cell'].astype(np.int32), nsearch )\n",
+    "        if not silent : print('misses in tri2latlon_fine (triangles -> quads) : ', misses)\n",
+    "    \n",
+    "    else :\n",
+    "        # map triangle data onto finer (factor oversample) grid, then coarsen to target resolution\n",
+    "        # (-> latmin and lonmin must be adjusted)\n",
+    "        latlon_hits =  np.zeros((nlat*oversample, nlon*oversample),dtype=np.int32)\n",
+    "        latlon_var_ref = np.zeros((nlat*oversample, nlon*oversample),dtype=np.float64)\n",
+    "\n",
+    "        misses = tri2latlon_fine( latlon_var_ref, latlon_hits,\n",
+    "                                ll_grid['lat_min'] - ll_grid['dlat']/2 + ll_grid['dlat']/(2*oversample),\n",
+    "                                ll_grid['lon_min'] - ll_grid['dlon']/2 + ll_grid['dlon']/(2*oversample),\n",
+    "                                ll_grid['dlat'] / oversample,\n",
+    "                                ll_grid['dlon'] / oversample,\n",
+    "                                tri_var.astype(np.float64), tri_grid['clat'].astype(np.float64), tri_grid['clon'].astype(np.float64),\n",
+    "                                tri_grid['vlat'].astype(np.float64), tri_grid['vlon'].astype(np.float64), tri_grid['vertex_of_cell'].astype(np.int32), nsearch )\n",
+    "        if not silent : print('misses in tri2latlon_fine (triangles -> quads) : ', misses)\n",
+    "\n",
+    "        # average over blocks of size oversample * oversample\n",
+    "        for i in range(oversample) :\n",
+    "            for j in range(oversample) :\n",
+    "                latlon_var += latlon_var_ref[i::oversample,j::oversample]\n",
+    "        latlon_var /= oversample**2\n",
+    "        latlon_var2 = coarsen_regular_2d_grid( latlon_var_ref, oversample )\n",
+    "        d = np.abs(latlon_var-latlon_var2)\n",
+    "        print('Coarsened results: ', d.max(), d.mean(), d.mean()/latlon_var.mean() )\n",
+    "\n",
+    "    return latlon_var\n",
+    "\n",
+    "@jit(nopython=True, nogil=True)\n",
+    "def det2d( ux, uy, vx, vy ) :\n",
+    "    return ux*vy - uy*vx\n",
+    "\n",
+    "@jit(nopython=True, nogil=True)\n",
+    "def point_in_triangle( lat, lon, vlat, vlon, include_edges=False ) :\n",
+    "\n",
+    "    # see http://mathworld.wolfram.com/TriangleInterior.html\n",
+    "    d12 = det2d( vlon[1]-vlon[0], vlat[1]-vlat[0], vlon[2]-vlon[0], vlat[2]-vlat[0] )\n",
+    "    if d12 != 0 :\n",
+    "        a   =   ( det2d( lon, lat, vlon[2]-vlon[0], vlat[2]-vlat[0] ) \\\n",
+    "                - det2d( vlon[0], vlat[0], vlon[2]-vlon[0], vlat[2]-vlat[0] ) ) / d12\n",
+    "        b   = - ( det2d( lon, lat, vlon[1]-vlon[0], vlat[1]-vlat[0] ) \\\n",
+    "                - det2d( vlon[0], vlat[0], vlon[1]-vlon[0], vlat[1]-vlat[0] ) ) / d12\n",
+    "    else :\n",
+    "        a = 0\n",
+    "        b = 0\n",
+    "\n",
+    "    inside = False\n",
+    "    if include_edges :\n",
+    "        if (a>=0) and (b>=0) and (a+b<=1) :\n",
+    "            inside = True\n",
+    "    else :\n",
+    "        if (a>0) and (b>0) and (a+b<1) :\n",
+    "            inside = True\n",
+    "    return inside\n",
+    "\n",
+    "@jit('int32(         float64[:,:], int32[:,:],  float64, float64, float64, float64, float64[:], float64[:], float64[:], float64[:], float64[:], int32[:,:],     int32   )', nopython=True, nogil=True)\n",
+    "def tri2latlon_fine( latlon_var,   latlon_hits, lat_min, lon_min, dlat,    dlon,    tri_var,    clat,       clon,       vlat,       vlon,       vertex_of_cell, nsearch ) :\n",
+    "    \"\"\"Assume lat-lon grid is finer than unstructured grid so that each triangle contains at least one lat-lon quad\"\"\"\n",
+    "\n",
+    "    nlat, nlon = latlon_var.shape\n",
+    "    trilat = np.zeros(3)\n",
+    "    trilon = np.zeros(3)\n",
+    "\n",
+    "    # determine dimensions of first triangle\n",
+    "    cidx=0\n",
+    "    trilat[0] = vlat[vertex_of_cell[0,cidx]]\n",
+    "    trilat[1] = vlat[vertex_of_cell[1,cidx]]\n",
+    "    trilat[2] = vlat[vertex_of_cell[2,cidx]]\n",
+    "    trilon[0] = vlon[vertex_of_cell[0,cidx]]\n",
+    "    trilon[1] = vlon[vertex_of_cell[1,cidx]]\n",
+    "    trilon[2] = vlon[vertex_of_cell[2,cidx]]\n",
+    "    dlat_tri = trilat.max() - trilat.min()\n",
+    "    dlon_tri = trilon.max() - trilon.min()\n",
+    "\n",
+    "    # search area\n",
+    "    nsearch = np.int(np.maximum( dlat_tri/dlat, dlon_tri/dlon ))+1\n",
+    "\n",
+    "    for cidx in range(clon.size) :\n",
+    "\n",
+    "        # find cell containing triangle center\n",
+    "        ilat = np.int( np.round( (clat[cidx]-lat_min)/dlat ) )\n",
+    "        ilon = np.int( np.round( (clon[cidx]-lon_min)/dlon ) )\n",
+    "\n",
+    "        #if ilat < 0 or ilat >= nlat or ilon < 0 or ilon >= nlon :\n",
+    "        if ilat < -nsearch or ilat >= nlat+nsearch or ilon < -nsearch or ilon >= nlon+nsearch :\n",
+    "            continue\n",
+    "\n",
+    "        # get vertices of triangle\n",
+    "        trilat[0] = vlat[vertex_of_cell[0,cidx]]\n",
+    "        trilat[1] = vlat[vertex_of_cell[1,cidx]]\n",
+    "        trilat[2] = vlat[vertex_of_cell[2,cidx]]\n",
+    "        trilon[0] = vlon[vertex_of_cell[0,cidx]]\n",
+    "        trilon[1] = vlon[vertex_of_cell[1,cidx]]\n",
+    "        trilon[2] = vlon[vertex_of_cell[2,cidx]]\n",
+    "\n",
+    "        # irhotify neighbor cells with centers in the same triangle\n",
+    "        for i in range( np.maximum(ilat-nsearch,0), np.minimum(ilat+1+nsearch,nlat) ) :\n",
+    "            for j in range( np.maximum(ilon-nsearch,0), np.minimum(ilon+1+nsearch,nlon) ) :\n",
+    "                if point_in_triangle( i*dlat+lat_min, j*dlon+lon_min, trilat, trilon ) :\n",
+    "                    latlon_hits[ i, j ] += 1\n",
+    "                    latlon_var[  i, j ] += tri_var[cidx]\n",
+    "\n",
+    "    misses = 0\n",
+    "    for ilat in range(nlat) :\n",
+    "        for ilon in range(nlon) :\n",
+    "            if latlon_hits[ ilat, ilon ] == 0 :  # this should not be necessary, but it is...\n",
+    "                if (ilat > 0) and (ilat < nlat-1) and (ilon > 0) and (ilon < nlon-1) :\n",
+    "                    s = latlon_hits[ ilat-1:ilat+2, ilon-1:ilon+2 ].sum()\n",
+    "                    if s > 0 :\n",
+    "                        latlon_var[  ilat, ilon ] = (  latlon_hits[ ilat-1:ilat+2, ilon-1:ilon+2 ] \\\n",
+    "                                                     * latlon_var[ ilat-1:ilat+2, ilon-1:ilon+2 ]).sum() / s\n",
+    "                        misses += 1\n",
+    "\n",
+    "            elif latlon_hits[ ilat, ilon ] > 1 : # this also should not be necessary, but it is...\n",
+    "                latlon_var[  ilat, ilon ] = latlon_hits[ ilat, ilon ] / latlon_hits[ ilat, ilon ]\n",
+    "                latlon_hits[ ilat, ilon ] = 1\n",
+    "\n",
+    "    return misses\n",
+    "\n",
+    "@jit(nopython=True,nogil=True)\n",
+    "def coarsen_regular_2d_grid( arr2d, cfac ) :\n",
+    "    \"\"\"Coarsen 2d array by factor cfac, i.e. average over blocks of size cfac * cfac\"\"\"\n",
+    "    nx, ny = arr2d.shape\n",
+    "    nxc, nyc = nx // cfac, ny // cfac\n",
+    "    arr2dc = np.zeros((nxc,nyc),dtype=arr2d.dtype)\n",
+    "    for ic in range(nxc) :\n",
+    "        for jc in range(nyc) :\n",
+    "            for i in range(cfac) :\n",
+    "                for j in range(cfac) :\n",
+    "                    arr2dc[ic,jc] += arr2d[ic*cfac+i,jc*cfac+j]\n",
+    "            arr2dc[ic,jc] /= cfac**2\n",
+    "    return arr2dc\n",
+    "\n",
+    "##################################################################\n",
+    "#                        MAIN FUNCTION\n",
+    "###################################################################\n",
+    "\n",
+    "def generate_mystic_files(lat_cam,lon_cam,dx,dy,dx1,dy1,lat_marg,lon_marg,dlat,dlon,dlat_px,dlon_px,grid_o,data_1,data_2,data_3,data_4,z_max,t_h,dom,nx,ny):\n",
+    "    \n",
+    "    lat_min, lat_max, lon_min, lon_max = lat_cam - dlat/2, lat_cam + dlat/2, lon_cam - dlon/2, lon_cam + dlon/2\n",
+    "    print('    extracting model state in {}<lat<{}, {}<lon<{}'.format( lat_min, lat_max, lon_min, lon_max ))\n",
+    "\n",
+    "     \n",
+    "    grid, modelvars = get_subdomain( (lat_min - lat_marg) * np.pi/180, (lat_max + lat_marg) * np.pi/180,\n",
+    "                                     (lon_min - lon_marg) * np.pi/180, (lon_max + lon_marg) * np.pi/180,grid_o,data_1,data_2,data_3,data_4)\n",
+    "    \n",
+    "    # find camera column\n",
+    "    idx_cam = np.argmin( (grid['clat']*180.0/np.pi - lat_cam)**2 + (grid['clon']*180.0/np.pi - lon_cam)**2 )\n",
+    "    print('    camera column: {} (clat={}, clon={})'.format( idx_cam, grid['clat'][idx_cam]*180/np.pi, grid['clon'][idx_cam]*180/np.pi ) )\n",
+    "    \n",
+    "    # finding the solar zenith angle at the center of the subdomain\n",
+    "    csza=modelvars['cosz_bz'][0,idx_cam]\n",
+    "    sza = math.degrees(math.acos(csza))\n",
+    "    \n",
+    "    # okay for clouds (heating_rate layer_fd) we should write from index 0:149 -> 0:29. km\n",
+    "    z_hl = modelvars['z_ifc'][:,idx_cam]\n",
+    "    z_icon = z_hl[1:151]\n",
+    "    # full levels\n",
+    "    z_fl = 0.5*( z_hl[1:] + z_hl[:-1] )\n",
+    "    dz = np.abs( modelvars['z_ifc'][1:,:] - modelvars['z_ifc'][:-1,:] )\n",
+    "    #nz = zlayers.size\n",
+    "    nz = z_hl.size -1\n",
+    "    \n",
+    "    print('deriving CH4 and N2O profiles')\n",
+    "    ch4 = gas_profile(vmr_ch4,modelvars['pres_ifc'][:,:],vpp_ch4)\n",
+    "    n2o = gas_profile(vmr_n2o,modelvars['pres_ifc'][:,:],vpp_n2o)\n",
+    "    \n",
+    "    # domain mean values\n",
+    "    ch4_dm = ch4.mean((1))\n",
+    "    n2o_dm = n2o.mean((1))\n",
+    "   \n",
+    "    print ('[icon2mystic] Writing Ch4 and N2O')\n",
+    "    # One should put these profiles in the LibRadTran/data subdirectory to be used in the analysis \n",
+    "    with open('/work/bb1135/icon_output/LC1-LES-471x667km-lon25-lat40-300m-0006/input4libradtran/afglus_ch4_vmr_'+dom+'_'+t_h+'.dat','w') as f:\n",
+    "        f.writelines( [ (\"%e %e \\n\" % (z_hl[i]/1e3,ch4_dm[i])) \\\n",
+    "                        for i in range(z_hl.size)] )\n",
+    "\n",
+    "    with open('/work/bb1135/icon_output/LC1-LES-471x667km-lon25-lat40-300m-0006/input4libradtran/afglus_n2o_vmr_'+dom+'_'+t_h+'.dat','w') as f:\n",
+    "        f.writelines( [ (\"%e %e \\n\" % (z_hl[i]/1e3,n2o_dm[i])) \\\n",
+    "                        for i in range(z_hl.size)] ) \n",
+    "    \n",
+    "    print('Deriving temp qv, o3, density at half levels')\n",
+    "    temp_hl = interpolate_hl(modelvars['temp'],modelvars['pres'],modelvars['pres_ifc'])\n",
+    "    qv_hl   = interpolate_hl(modelvars['tot_qv_dia'],modelvars['pres'],modelvars['pres_ifc']) \n",
+    "    o3_hl   = interpolate_hl(modelvars['o3'],modelvars['pres'],modelvars['pres_ifc'])\n",
+    "    rho_hl  = ((modelvars['pres_ifc'])/(R*temp_hl))\n",
+    "    pres_hl = modelvars['pres_ifc']\n",
+    "    \n",
+    "    ## getting correct units for Ozone and water vapor mass concentrations\n",
+    "    print('min/mean/max of density: ', modelvars['rho'].min(), modelvars['rho'].mean(), modelvars['rho'].max() )\n",
+    "    o3_hl_n = o3_hl* rho_hl # kg/m3\n",
+    "    qv_hl_n = qv_hl* rho_hl # kg/m3\n",
+    "    \n",
+    "    # adjusting units of water and ice mass concentrations\n",
+    "    print('*** computing LWC, IWC, reff_qc, reff_qi...'); starttime = time.perf_counter()\n",
+    "    IWC = np.maximum( modelvars['tot_qi_dia'], 0 ) * modelvars['rho'] # kg/m3\n",
+    "    LWC = np.maximum( modelvars['tot_qc_dia'], 0 ) * modelvars['rho'] # kg/m3\n",
+    "    \n",
+    "    #print('*** computing ice and water optical depth ...')\n",
+    "    tau_lwc = 3*LWC*dz/(2*rhoh2o*modelvars['reff_qc_ecrad'])\n",
+    "    tau_iwc = 3*IWC*dz/(2*rhoice*modelvars['reff_qi_ecrad'])\n",
+    "    \n",
+    "    # adjust the LWC and IWC according to limits used in the LibRadTran\n",
+    "    LWC_n = tau_lwc/((3*dz)/(2*rhoh2o*np.clip(modelvars['reff_qc_ecrad'], 5.0*1e-6, 25.0*1e-6)))\n",
+    "    IWC_n = tau_iwc/((3*dz)/(2*rhoice*np.clip(modelvars['reff_qi_ecrad'], 20.0*1e-6, 60.0*1e-6)))\n",
+    "\n",
+    "    print('[icon2mystic] Mapping model state to rectangular grid...')\n",
+    "    print('    horizontal resolution: {}km --> dlat_px={}deg, dlon_px={}deg'.format( dx, dlat_px, dlon_px ))\n",
+    "\n",
+    "    # Expand subdomains to overlap with neighboring subdomains \n",
+    "    lat_max1 = lat_max + lat_marg\n",
+    "    lat_min1 = lat_min - lat_marg\n",
+    "    lon_max1 = lon_max + lon_marg\n",
+    "    lon_min1 = lon_min - lon_marg\n",
+    "    \n",
+    "    # define lat-lon grid\n",
+    "    nlat = int( (lat_max-lat_min) / dlat_px )\n",
+    "    nlon = int( (lon_max-lon_min) / dlon_px )\n",
+    "    \n",
+    "    nlat1 = int( (lat_max1-lat_min1) / dlat_px )\n",
+    "    nlon1 = int( (lon_max1-lon_min1) / dlon_px )\n",
+    "    print('    lat-lon grid pixel size : nlon={} x nlat={}'.format(nlon,nlat) )\n",
+    "    print('    lat-lon grid extende pixel size : nlon={} x nlat={}'.format(nlon1,nlat1) )\n",
+    "\n",
+    "    print('*** calling generate_lat_lon_grid'); starttime = time.perf_counter()\n",
+    "    # without expansion (atmospheric background profiles)\n",
+    "    latlon_grid  = generate_latlon_grid( lat_min * np.pi/180, lon_min * np.pi/180, lat_max * np.pi/180, lon_max * np.pi/180, nlat, nlon, dim=2 )\n",
+    "    # with expansion (LibRadTran 3D cloud files)\n",
+    "    latlon_grid1  = generate_latlon_grid( lat_min1 * np.pi/180, lon_min1 * np.pi/180, lat_max1 * np.pi/180, lon_max1 * np.pi/180, nlat1, nlon1, dim=2 )\n",
+    "    print('*** generate_lat_lon_grid took %f seconds' % (time.perf_counter() - starttime))\n",
+    "    print( latlon_grid.keys() )\n",
+    "\n",
+    "    # horizontal grid overview plot\n",
+    "    fig, ax = plt.subplots(figsize=(8,8))\n",
+    "    ax.scatter( grid_o['vlon'][:]*180.0/np.pi, grid_o['vlat'][:]*180.0/np.pi, c='g', alpha=0.3, label='original_triangle grid vertices' )\n",
+    "    ax.scatter( grid['vlon']*180.0/np.pi, grid['vlat']*180.0/np.pi, c='b', alpha=0.3, label='triangle grid vertices' )\n",
+    "    ax.scatter( latlon_grid1['lon2d']*180.0/np.pi, latlon_grid1['lat2d']*180.0/np.pi, marker='.', c='r', s=1, alpha=0.3, label='cartesian grid cell centers' )\n",
+    "    ax.scatter( lon_cam, lat_cam, marker='X', c='#666666', s=100, label='camera position' )\n",
+    "    ax.scatter( grid['clon'][idx_cam]*180/np.pi, grid['clat'][idx_cam]*180/np.pi, marker='X', c='k', s=100, label='center of camera column' )\n",
+    "    ax.plot( (lon_min1, lon_max1, lon_max1, lon_min1, lon_min1), (lat_min1, lat_min1, lat_max1, lat_max1, lat_min1), 'k', label='cartesian grid boundary' )\n",
+    "    ax.legend(title='horizontal grid', loc='upper left', bbox_to_anchor=(1., 0., 0.3, 1.0), frameon=False)\n",
+    "    \n",
+    "    print('[icon2mystic] Mapping coordinates to cartesian grid...')\n",
+    "    cartvar = {}\n",
+    "    for v in ['clon','clat'] :\n",
+    "        cartvar[v] = tri2latlon( latlon_grid, grid, grid[v], method='fine', silent=True )\n",
+    "\n",
+    "    print('shape of the remapped data',cartvar['clat'].shape)\n",
+    "    \n",
+    "    print('[icon2mystic] Mapping effective radii to cartesian grid...')\n",
+    "    for v in ['reff_qc_ecrad','reff_qi_ecrad'] :\n",
+    "        cartvar[v] = np.zeros((nlat1,nlon1,nz))\n",
+    "        print('*** calling tri2latlon'); starttime = time.perf_counter()\n",
+    "        for k in range(nz) :        \n",
+    "            cartvar[v][:,:,k] = tri2latlon( latlon_grid1, grid, modelvars[v][k,:], method='fine', silent=True)\n",
+    "        print('*** tri2latlon took %f seconds' % (time.perf_counter() - starttime))\n",
+    "        \n",
+    "    print('[icon2mystic] Mapping LWC and IWC to cartesian grid...')\n",
+    "    modelvars['LWC'] = np.nan_to_num(LWC_n,nan=0.0) # just to make sure we do not have nan values\n",
+    "    modelvars['IWC'] = np.nan_to_num(IWC_n,nan=0.0)\n",
+    "    # remap\n",
+    "    for v in ['LWC','IWC'] :\n",
+    "        cartvar[v] = np.zeros((nlat1,nlon1,nz))\n",
+    "        print('*** calling tri2latlon'); starttime = time.perf_counter()\n",
+    "        for k in range(nz) :        \n",
+    "            cartvar[v][:,:,k] = tri2latlon( latlon_grid1, grid, modelvars[v][k,:], method='fine', silent=True )\n",
+    "        print('*** tri2latlon took %f seconds' % (time.perf_counter() - starttime))\n",
+    "        \n",
+    "    print('[icon2mystic] remap ICON radiative temperature tendencies ...')\n",
+    "    for v in ['ddt_temp_radlwnw','ddt_temp_radlwcs','ddt_temp_radswnw','ddt_temp_radswcs','clc'] :\n",
+    "        cartvar[v] = np.zeros((nlat,nlon,nz))\n",
+    "        print('*** calling tri2latlon'); starttime = time.perf_counter()\n",
+    "        for k in range(nz) :\n",
+    "            cartvar[v][:,:,k] = tri2latlon( latlon_grid, grid, modelvars[v][k,:], method='fine', silent=True )\n",
+    "        print('*** tri2latlon took %f seconds' % (time.perf_counter() - starttime))\n",
+    "    \n",
+    "    # deriving cloud radiative heating\n",
+    "    cartvar['ddt_radlw'] = cartvar['ddt_temp_radlwnw'] - cartvar['ddt_temp_radlwcs']\n",
+    "    cartvar['ddt_radsw'] = cartvar['ddt_temp_radswnw'] - cartvar['ddt_temp_radswcs']\n",
+    "    \n",
+    "    print('creating a dataset and save the data')\n",
+    "    ds = xr.Dataset(data_vars={\"qc\":([\"lat\",\"lon\",'height'],cartvar['LWC'][:,:,::-1]), \n",
+    "                               \"qi\":([\"lat\",\"lon\",'height'],cartvar['IWC'][:,:,::-1]),\n",
+    "                               \"clc\":([\"lat\",\"lon\",'height'],cartvar['clc'][:,:,::-1]),\n",
+    "                               \"ddt_radlwnw\":([\"lat\",\"lon\",'height'],cartvar['ddt_temp_radlwnw'][:,:,::-1]),\n",
+    "                               \"ddt_radlwcs\":([\"lat\",\"lon\",'height'],cartvar['ddt_temp_radlwcs'][:,:,::-1]),\n",
+    "                               \"ddt_radswnw\":([\"lat\",\"lon\",'height'],cartvar['ddt_temp_radswnw'][:,:,::-1]),\n",
+    "                               \"ddt_radswcs\":([\"lat\",\"lon\",'height'],cartvar['ddt_temp_radswcs'][:,:,::-1]),\n",
+    "                               \"ddt_radlw\":([\"lat\",\"lon\",'height'],cartvar['ddt_radlw'][:,:,::-1]),\n",
+    "                               \"ddt_radsw\":([\"lat\",\"lon\",'height'],cartvar['ddt_radsw'][:,:,::-1]),\n",
+    "                               \"rqi\":([\"lat\",\"lon\",'height'],cartvar['reff_qi_ecrad'][:,:,::-1]),\n",
+    "                               \"rqc\":([\"lat\",\"lon\",'height'],cartvar['reff_qc_ecrad'][:,:,::-1]),\n",
+    "                               \"z_fl\":(['height'],z_fl[::-1]),\n",
+    "                           \n",
+    "                           },\n",
+    "                coords={\"lat\": ([\"lat\"], np.arange(0,nlat)), \n",
+    "                        \"lon\": ([\"lon\"], np.arange(0,nlon)),\n",
+    "                        'height':([\"height\"],np.arange(0,nz))})\n",
+    "    \n",
+    "    # save as netcdf file\n",
+    "    ds.to_netcdf('/work/bb1135/icon_output/LC1-LES-471x667km-lon25-lat40-300m-0006/input4libradtran/icon_'+dom+'_'+t_h+'.nc')\n",
+    "    ########################################################################\n",
+    "    # preparing atmospheric background profiles\n",
+    "    print('[icon2mystic] remap atmospheric components to cartesian grid...')\n",
+    "    var_name = ['o3_hl','pres_hl','temp_hl','rho_hl','qv_hl']\n",
+    "    nm = 0\n",
+    "    for v in [o3_hl_n,pres_hl,temp_hl,rho_hl,qv_hl_n] :\n",
+    "        cartvar[var_name[nm]] = np.zeros((nlat,nlon,nz+1))\n",
+    "        print('*** calling tri2latlon'); starttime = time.perf_counter()\n",
+    "        for k in range(nz+1) :        \n",
+    "            cartvar[var_name[nm]][:,:,k] = tri2latlon( latlon_grid, grid, v[k,:], method='fine', silent=True )\n",
+    "        print('*** tri2latlon took %f seconds' % (time.perf_counter() - starttime))\n",
+    "        nm = nm + 1 \n",
+    "    \n",
+    "    # taking domain mean\n",
+    "    for n in ['pres_dm','temp_dm','rho_dm','o3_dm','qv_dm']:\n",
+    "        cartvar[n.replace(\"_dm\", \"_hl\")][cartvar[n.replace(\"_dm\", \"_hl\")] == 0] = np.nan \n",
+    "        cartvar[n] = np.nanmean(cartvar[n.replace(\"_dm\", \"_hl\")],axis=(0,1))\n",
+    "    \n",
+    "    print ('[icon2mystic] Writing background_atmospheric_profile')\n",
+    "    with open('/work/bb1135/icon_output/LC1-LES-471x667km-lon25-lat40-300m-0006/input4libradtran/atmosphere_mean_'+dom+'_'+t_h+'.dat','w') as f:\n",
+    "        # here I will add another level to avoid the crash in radiative transfer with NCA solver\n",
+    "        f.writelines( [ (\"%f %e %e %e %e %e %e %e \\n\" % ((z_hl[0]/1e3)+0.1,cartvar['pres_dm'][0]/1e2,\n",
+    "                                                  cartvar['temp_dm'][0],cartvar['rho_dm'][0]/m_d*1e-6,\n",
+    "                                                  cartvar['o3_dm'][0]/m_o3*1e-6,\n",
+    "                                                  cartvar['o3_dm'][0]*0.0 + (o2/m_o2)*1e-6,\n",
+    "                                                  cartvar['qv_dm'][0]/m_h2o*1e-6,\n",
+    "                                                  cartvar['o3_dm'][0]*0.0 + (co2/m_co2)*1e-6))] )\n",
+    "        \n",
+    "        f.writelines( [ (\"%f %e %e %e %e %e %e %e \\n\" % (z_hl[i]/1e3,cartvar['pres_dm'][i]/1e2,\n",
+    "                                                  cartvar['temp_dm'][i],cartvar['rho_dm'][i]/m_d*1e-6,\n",
+    "                                                  cartvar['o3_dm'][i]/m_o3*1e-6,\n",
+    "                                                  cartvar['o3_dm'][i]*0.0 + (o2/m_o2)*1e-6,\n",
+    "                                                  cartvar['qv_dm'][i]/m_h2o*1e-6,\n",
+    "                                                  cartvar['o3_dm'][i]*0.0 + (co2/m_co2)*1e-6)) \\\n",
+    "                        for i in range(nz+1)] )    \n",
+    "    ########################################################################    \n",
+    "        \n",
+    "    ## Creating NWP homogeneous grid-box clouds and homogeneous clouds with cloud fraction from LEM clouds at the resolution of 2.5 km \n",
+    "    print('  finding total cloudy pixels from both ice and water clouds')\n",
+    "    cartvar['ic_wc_tot'] = cartvar['LWC'] + cartvar['IWC']\n",
+    "    # empty array to store the coarse-grained LWC+IWC and cloud fraction\n",
+    "    for n in ['LWC_cg','IWC_cg','reff_qc_cg','reff_qi_cg','cf_cg','LWC_dl','IWC_dl','reff_qc_dl','reff_qi_dl']:\n",
+    "        cartvar[n] = np.zeros((nx,ny,150))  \n",
+    "    # let's chunk them\n",
+    "    tc1 = np.swapaxes(cartvar['LWC'], 0, 1)\n",
+    "    tc2 = np.swapaxes(cartvar['IWC'], 0, 1)\n",
+    "    tc3 = np.swapaxes(cartvar['reff_qc_ecrad'], 0, 1)\n",
+    "    tc4 = np.swapaxes(cartvar['reff_qi_ecrad'], 0, 1)\n",
+    "    tc5 = np.swapaxes(cartvar['ic_wc_tot'], 0, 1)\n",
+    "\n",
+    "    tm1 = np.array_split(tc1[:,:,:],nx,axis=0) #2.5 km grid spacing\n",
+    "    tm2 = np.array_split(tc2[:,:,:],nx,axis=0)\n",
+    "    tm3 = np.array_split(tc3[:,:,:],nx,axis=0)\n",
+    "    tm4 = np.array_split(tc4[:,:,:],nx,axis=0)\n",
+    "    tm5 = np.array_split(tc5[:,:,:],nx,axis=0)\n",
+    "    \n",
+    "    # empty array \n",
+    "    lc1 = np.zeros((150))\n",
+    "    lc2 = np.zeros((150))\n",
+    "    lc3 = np.zeros((150))\n",
+    "    lc4 = np.zeros((150))\n",
+    "    lc5 = np.zeros((150))\n",
+    "    for i in range(len(tm1)):\n",
+    "        tmm_1 = np.array_split(tm1[i],ny,axis=1)\n",
+    "        tmm_2 = np.array_split(tm2[i],ny,axis=1)\n",
+    "        tmm_3 = np.array_split(tm3[i],ny,axis=1)\n",
+    "        tmm_4 = np.array_split(tm4[i],ny,axis=1)\n",
+    "        tmm_5 = np.array_split(tm5[i],ny,axis=1)\n",
+    "        for j in range(len(tmm_1)):  \n",
+    "            for k in range(150):\n",
+    "                tmmm_1 = tmm_1[j][:,:,k]\n",
+    "                tmmm_2 = tmm_2[j][:,:,k]\n",
+    "                tmmm_3 = tmm_3[j][:,:,k]\n",
+    "                tmmm_4 = tmm_4[j][:,:,k]\n",
+    "                tmmm_5 = tmm_5[j][:,:,k]\n",
+    "\n",
+    "                nln, nlt = tmmm_1.shape\n",
+    "                # cloud water\n",
+    "                cloudy1 = tmmm_1[np.nonzero(tmmm_1)]\n",
+    "                if cloudy1.size > 0 :\n",
+    "                    lc1[k] = cloudy1.mean()\n",
+    "                    cartvar['LWC_cg'][i,j,k] = lc1[k]\n",
+    "                # cloud ice\n",
+    "                cloudy2 = tmmm_2[np.nonzero(tmmm_2)]\n",
+    "                if cloudy2.size > 0 :\n",
+    "                    lc2[k] = cloudy2.mean()\n",
+    "                    cartvar['IWC_cg'][i,j,k] = lc2[k]\n",
+    "                # cloud water reff\n",
+    "                cloudy3 = tmmm_3[np.nonzero(tmmm_3)]\n",
+    "                if cloudy3.size > 0 :\n",
+    "                    lc3[k] = cloudy3.mean()\n",
+    "                    cartvar['reff_qc_cg'][i,j,k] = lc3[k]\n",
+    "                # cloud ice reff\n",
+    "                cloudy4 = tmmm_4[np.nonzero(tmmm_4)]\n",
+    "                if cloudy4.size > 0 :\n",
+    "                    lc4[k] = cloudy4.mean()\n",
+    "                    cartvar['reff_qi_cg'][i,j,k] = lc4[k]\n",
+    "                # cloud fraction\n",
+    "                cloudy5 = tmmm_5[np.nonzero(tmmm_5)]\n",
+    "                if cloudy5.size > 0 :\n",
+    "                    lc5[k] = (cloudy5.size/nln/nlt)\n",
+    "                    cartvar['cf_cg'][i,j,k] = lc5[k]\n",
+    "                # NWP homogeneous grid-box clouds\n",
+    "                cartvar['LWC_dl'][i,j,k] = tmmm_1.mean()\n",
+    "                cartvar['IWC_dl'][i,j,k] = tmmm_2.mean()\n",
+    "                cartvar['reff_qc_dl'][i,j,k] = tmmm_3.mean()\n",
+    "                cartvar['reff_qi_dl'][i,j,k] = tmmm_4.mean()\n",
+    "\n",
+    "    # just to make sure we do not have nan values             \n",
+    "    cartvar['LWC_cg'] = np.nan_to_num(cartvar['LWC_cg'],nan=0.0)\n",
+    "    cartvar['IWC_cg'] = np.nan_to_num(cartvar['IWC_cg'],nan=0.0)\n",
+    "    cartvar['reff_qc_cg'] = np.nan_to_num(cartvar['reff_qc_cg'],nan=0.0)\n",
+    "    cartvar['reff_qi_cg'] = np.nan_to_num(cartvar['reff_qi_cg'],nan=0.0)\n",
+    "    cartvar['LWC_dl'] = np.nan_to_num(cartvar['LWC_dl'],nan=0.0)\n",
+    "    cartvar['IWC_dl'] = np.nan_to_num(cartvar['IWC_dl'],nan=0.0)\n",
+    "    cartvar['reff_qc_dl'] = np.nan_to_num(cartvar['reff_qc_dl'],nan=0.0)\n",
+    "    cartvar['reff_qi_dl'] = np.nan_to_num(cartvar['reff_qi_dl'],nan=0.0)\n",
+    "    cartvar['cf_cg'] = np.nan_to_num(cartvar['cf_cg'],nan=0.0)\n",
+    "    \n",
+    "    # Save (#ice defaut code: 20-60 wc=5-25)\n",
+    "    print('[icon2mystic] Writing MYSTIC files...') \n",
+    "    # NWP homogeneous clouds with cloud fraction\n",
+    "    write_mc_cloud_file_frac(np.swapaxes(cartvar['LWC_cg'][:,:,::-1]*1000,0,1), np.swapaxes(np.clip( cartvar['reff_qc_cg'][:,:,::-1]*1e6,  5.0, 25.0 ),0,1), np.swapaxes(cartvar['cf_cg'][:,:,::-1],0,1), dx1*1e3, dy1*1e3, z_hl[::-1], fname='/work/bb1135/icon_output/LC1-LES-471x667km-lon25-lat40-300m-0006/input4libradtran/wc3d_cg_'+dom+'_'+t_h+'.dat', flag=4 )\n",
+    "    write_mc_cloud_file_frac(np.swapaxes(cartvar['IWC_cg'][:,:,::-1]*1000,0,1), np.swapaxes(np.clip( cartvar['reff_qi_cg'][:,:,::-1]*1e6, 20.0, 60.0 ),0,1), np.swapaxes(cartvar['cf_cg'][:,:,::-1],0,1), dx1*1e3, dy1*1e3, z_hl[::-1], fname='/work/bb1135/icon_output/LC1-LES-471x667km-lon25-lat40-300m-0006/input4libradtran/ic3d_cg_'+dom+'_'+t_h+'.dat', flag=4 )\n",
+    "    # NWP homogeneous grid-box clouds\n",
+    "    write_mc_cloud_file(np.swapaxes(cartvar['LWC_dl'][:,:,::-1]*1000,0,1), np.swapaxes(np.clip( cartvar['reff_qc_dl'][:,:,::-1]*1e6,  5.0, 25.0 ),0,1), dx1*1e3, dy1*1e3, z_hl[::-1], fname='/work/bb1135/icon_output/LC1-LES-471x667km-lon25-lat40-300m-0006/input4libradtran/wc3d_dl_'+dom+'_'+t_h+'.dat', flag=3 )\n",
+    "    write_mc_cloud_file(np.swapaxes(cartvar['IWC_dl'][:,:,::-1]*1000,0,1), np.swapaxes(np.clip( cartvar['reff_qi_dl'][:,:,::-1]*1e6, 20.0, 60.0 ),0,1), dx1*1e3, dy1*1e3, z_hl[::-1], fname='/work/bb1135/icon_output/LC1-LES-471x667km-lon25-lat40-300m-0006/input4libradtran/ic3d_dl_'+dom+'_'+t_h+'.dat', flag=3 )\n",
+    "    # LEM clouds\n",
+    "    write_mc_cloud_file( cartvar['LWC'][:,:,::-1]*1000, np.clip( cartvar['reff_qc_ecrad'][:,:,::-1]*1e6,  5.0, 25.0 ), dx*1e3, dy*1e3, z_hl[::-1], fname='/work/bb1135/icon_output/LC1-LES-471x667km-lon25-lat40-300m-0006/input4libradtran/wc3d_'+dom+'_'+t_h+'.dat', flag=3 )\n",
+    "    write_mc_cloud_file( cartvar['IWC'][:,:,::-1]*1000, np.clip( cartvar['reff_qi_ecrad'][:,:,::-1]*1e6, 20.0, 60.0 ), dx*1e3, dy*1e3, z_hl[::-1], fname='/work/bb1135/icon_output/LC1-LES-471x667km-lon25-lat40-300m-0006/input4libradtran/ic3d_'+dom+'_'+t_h+'.dat', flag=3 )\n",
+    "    \n",
+    "    return sza,cartvar,z_hl"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define inputs and call 'generate_mystic_files' function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Information about defining the grid parameters for remapping and coarse-graining\n",
+    "# Here I follow the routines outlined in the MPI grid generator.\n",
+    "\n",
+    "# ICON-LEM triangular planar grid \n",
+    "\n",
+    "resolution = 300\n",
+    "x_length = 471000\n",
+    "y_length = 667000\n",
+    "\n",
+    "# from planar-grid routine \n",
+    "\n",
+    "edge_length = 1.5196713713 * resolution\n",
+    "x_no_of_columns = int(round(x_length / edge_length))\n",
+    "edge_length = x_length / x_no_of_columns\n",
+    "y_height = edge_length * np.sin(np.deg2rad(60)) # triangle height\n",
+    "y_no_of_rows = int(round(y_length / y_height))\n",
+    "\n",
+    "# lon and lat step\n",
+    "x_lon_range = 6\n",
+    "y_lat_range = 6\n",
+    "x_lon_step  = x_lon_range / (x_no_of_columns*2) \n",
+    "y_lat_step  = y_lat_range / y_no_of_rows\n",
+    "\n",
+    "print('X_increment in degrees:', x_lon_step)\n",
+    "print('Y_increment in degrees:', y_lat_step)\n",
+    "\n",
+    "print('Longitude extension of the LEM domain:', x_lon_step * 2*x_no_of_columns)\n",
+    "print('Latitude extension of the LEM domain:', y_lat_step * y_no_of_rows)\n",
+    "print('Number of total grid cells:', 2 * x_no_of_columns * y_no_of_rows) # should be 3489474\n",
+    "\n",
+    "print('Number of points in the x_direction of the Cartesian grid:',2 * x_no_of_columns)\n",
+    "print('Number of points in the y_direction of the Cartesian grid:',y_no_of_rows)\n",
+    "\n",
+    "print('X_increment in meter',x_length/(6/x_lon_step))\n",
+    "print('y_increment in meter:',y_length/(6/y_lat_step))\n",
+    "\n",
+    "print('Number of NWP boxes fit into 1°x1° resolution of subdomains in the x direction:',1/x_lon_step)\n",
+    "print('Number of NWP boxes fit into 1°x1° resolution of subdomains in the y direction:',1/y_lat_step)\n",
+    "\n",
+    "'''\n",
+    "a = ((471/6)/344)*(344/41)\n",
+    "b = ((667/6)/281)*(281/33)\n",
+    "print(a)\n",
+    "print(b)\n",
+    "print('resolution of derived rectangles at 300 m ',np.sqrt(0.22819767441860464*0.3956109134045077))\n",
+    "print('resolution of derived rectangles at 2500 m ',np.sqrt(1.9146341463414633*3.368686868686869))\n",
+    "'''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ICON grid\n",
+    "grid_o = netCDF4.Dataset('/work/bb1135/icon_output/LC1-LES-471x667km-lon25-lat40-300m-0006/grid_DOM01.nc','r')\n",
+    "\n",
+    "# Margin of lat/lon for extending the subdomains for overlapping \n",
+    "lat_marg = 0.11\n",
+    "lon_marg = 0.11\n",
+    "\n",
+    "# Extention of lat/lon for creating subdomains\n",
+    "dlat = 1 \n",
+    "dlon = 1\n",
+    "\n",
+    "# X and Y increments in km for remapping LEM clouds\n",
+    "dx = 0.227 \n",
+    "dy = 0.394 \n",
+    "\n",
+    "# X and Y increments in km for NWP clouds\n",
+    "dx1 = 1.9 \n",
+    "dy1 = 3.3 \n",
+    "\n",
+    "# number of NWP boxes in 1°x1° subdomains \n",
+    "nx = 41\n",
+    "ny = 33\n",
+    "\n",
+    "# lat/lon increments in degrees\n",
+    "dlat_px = 0.003552397868561279 \n",
+    "dlon_px = 0.002904162633107454 \n",
+    "z_max = 13 # not used\n",
+    "\n",
+    "#############################################\n",
+    "# List of ICON-LEM simulations\n",
+    "\n",
+    "#'LC1-LES-471x667km-lon25-lat40-300m-0006' : {'res':'300', 'radiation':4, 'mphy':0.8},  dom01: shallow cumulus clouds\n",
+    "#'LC1-LES-471x667km-lon40-lat44-300m-0003' : {'res':'300', 'radiation':4, 'mphy':0.8},  dom02: WCB ascent\n",
+    "#'LC1-LES-471x667km-lon30-lat53-300m-0005' : {'res':'300', 'radiation':4, 'mphy':0.8},  dom03: WCB cyclonic outflow\n",
+    "#'LC1-LES-471x667km-lon50-lat48-300m-0004' : {'res':'300', 'radiation':4, 'mphy':0.8},  dom03: WCB anticyclonic outflow"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# looping through time and calling 'generate_mystic_files' function\n",
+    "\n",
+    "# Repeat this for other ICON-LEM simulations by adjusting the path to the simulations output and lat/lon center of the first subdomain\n",
+    "\n",
+    "for t_h in ['20220105T100033Z','20220105T103033Z','20220105T110033Z','20220105T113033Z','20220105T120033Z','20220105T123033Z','20220105T130033Z','20220105T133033Z','20220105T140033Z']:\n",
+    "    \n",
+    "    data_1 = netCDF4.Dataset('/work/bb1135/icon_output/LC1-LES-471x667km-lon25-lat40-300m-0006/icon-cld3d_ML_'+t_h+'.nc','r')\n",
+    "    data_2 = netCDF4.Dataset('/work/bb1135/icon_output/LC1-LES-471x667km-lon25-lat40-300m-0006/icon-atm3d_ML_'+t_h+'.nc','r')\n",
+    "    data_3 = netCDF4.Dataset('/work/bb1135/icon_output/LC1-LES-471x667km-lon25-lat40-300m-0006/icon-ddt_temp_ML_'+t_h+'.nc','r')\n",
+    "    data_4 = netCDF4.Dataset('/work/bb1135/icon_output/LC1-LES-471x667km-lon25-lat40-300m-0006/icon-radbz_ML_'+t_h+'.nc','r')\n",
+    "    \n",
+    "    num = 1\n",
+    "    solar_za = []\n",
+    "    for j in range(6): \n",
+    "        lat_cam = 37.5 + j # Need to be adjusted for other LEM domains\n",
+    "        for i in range(6):\n",
+    "            lon_cam = 22.5 + i # Need to be adjusted for other LEM domains\n",
+    "            print('center:',lon_cam,lat_cam)\n",
+    "            #calling the final function\n",
+    "            ss = generate_mystic_files(lat_cam,lon_cam,dx,dy,dx1,dy1,lat_marg,lon_marg,dlat,dlon,dlat_px,dlon_px,grid_o,data_1,data_2,data_3,data_4,z_max,t_h,str(num),nx,ny)\n",
+    "            solar_za.append(ss)\n",
+    "            num = num + 1\n",
+    "    # Write down solar zenith angles        \n",
+    "    with open('/work/bb1135/icon_output/LC1-LES-471x667km-lon25-lat40-300m-0006/input4libradtran/sza_'+t_h+'.dat', 'w') as f:\n",
+    "        for item in solar_za:\n",
+    "            f.write('\"%s\" ' % item)        "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pycrh",
+   "language": "python",
+   "name": "pycrh"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/offlineRT/pre_process_data_for_CRH_uncertainty_calc.py b/offlineRT/pre_process_data_for_CRH_uncertainty_calc.py
new file mode 100644
index 0000000000000000000000000000000000000000..b2b750e24c86a2bf7c37213107dcfa4b554231a2
--- /dev/null
+++ b/offlineRT/pre_process_data_for_CRH_uncertainty_calc.py
@@ -0,0 +1,407 @@
+#@ Behrooz Keshtgar, KIT 2024
+
+# This script is for coarse-graining and calculating CRH uncertainty as a function of horizontal scale
+# MYSTIC solver
+
+#### Loading libraries
+import numpy as np
+import matplotlib.pyplot as plt
+import xarray as xr
+
+# Dictionary for loading datasets for the 4 simulations
+simdict = {
+         'lem_domain01' : {'name':'Shallow cumulus'          ,'res':'300', 'radiation':4, 'mphy':4}, # Only cloud radiation
+         'lem_domain02' : {'name':'WCB ascent'               ,'res':'300', 'radiation':4, 'mphy':4}, # Only cloud radiation
+         'lem_domain03' : {'name':'WCB cyclonic outflow'     ,'res':'300', 'radiation':4, 'mphy':4}, # Only cloud radiation
+         'lem_domain04' : {'name':'WCB anticyclonic outflow' ,'res':'300', 'radiation':4, 'mphy':4}  # Only cloud radiation
+          }
+
+#####################################################
+##### Step 1, loading CRH from different calculations
+####################################################
+
+# all libradtrn calculations 
+def load_simulations1(num):
+    ds_list = []
+    sim = list(simdict.keys())[num]
+    print('Working on loading data for', sim)
+    path = '/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/offline_radiation_calculation_output/'+sim+'/'
+    for solver in ['ipa3d','mysti','mcipa']:
+        # loop over sources (thermal/solar)
+        for source in ['thermal','solar']:
+            # loop over sim types
+            for nsim in ['01']: # simulations with Fu for ice param
+                if solver in ['mysti','mcipa']:
+                    ds = xr.open_mfdataset(path+'ds_librad_05T*_'+source+'_'+solver+'_'+nsim+'.nc',concat_dim='time',combine='nested',
+                                           chunks={'height': 50},parallel=True).isel(height=slice(0,140))
+                else:
+                    ds = xr.open_mfdataset(path+'ds_librad2_05T*_'+source+'_'+solver+'_'+nsim+'.nc',concat_dim='time',combine='nested',
+                                           chunks={'height': 50},parallel=True).isel(height=slice(0,140))
+                ds.attrs['sim_name'] = 'ds_librad_'+source+'_'+solver+'_'+nsim+''
+                ds_list.append(ds)
+    return ds_list                
+#-------------------------------------
+ds_lib1=load_simulations1(0)
+ds_lib2=load_simulations1(1)
+ds_lib3=load_simulations1(2)
+ds_lib4=load_simulations1(3)
+
+# libradtrn simulation using other ice parametrization
+def load_simulations2(num):
+    ds_list = []
+    sim = list(simdict.keys())[num]
+    print('Working on loading data for', sim)
+    path = '/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/offline_radiation_calculation_output/'+sim+'/'
+    for solver in ['ipa3d']:
+        # loop over sources (thermal/solar)
+        for source in ['thermal','solar']:
+            # loop over sim types
+            for nsim in ['02']: # simulations with Baum_ghm for ice param    
+                ds = xr.open_mfdataset(path+'ds_librad2_05T*_'+source+'_'+solver+'_'+nsim+'.nc',concat_dim='time',combine='nested',
+                                      chunks={'height': 50},parallel=True).isel(height=slice(0,140))
+                ds.attrs['sim_name'] = 'ds_librad_'+source+'_'+solver+'_'+nsim+''
+                ds_list.append(ds)
+    return ds_list                
+#-------------------------------------
+tmp22=load_simulations2(1)
+tmp33=load_simulations2(2)
+tmp44=load_simulations2(3)
+#-------------------------------------
+## add to the original datasets 
+ds_lib2.append(tmp22[0])
+ds_lib2.append(tmp22[1])
+ds_lib3.append(tmp33[0])
+ds_lib3.append(tmp33[1])
+ds_lib4.append(tmp44[0])
+ds_lib4.append(tmp44[1])
+
+'''
+#-------------------------------------
+# libradtrn simulations with NWP clouds 
+def load_simulations3(num):
+    ds_list = []
+    sim = list(simdict.keys())[num]
+    print('Working on loading data for', sim)
+    path = '/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/offline_radiation_calculation_output/'+sim+'/'
+    for solver in ['ipa3d']:
+        # loop over sources (thermal/solar)
+        for source in ['thermal','solar']:
+            # loop over sim types
+            for nsim in ['01']: # simulations with ice param of Fu (1998)
+                #--------------------------------------------------
+                # delta-eddington two-stream using homogenized clouds 
+                # with 2.5 km horizontal resolution
+                ds1 = xr.open_mfdataset(path+'ds_librad_dl_*_'+source+'_'+solver+'_'+nsim+'.nc',concat_dim='time',combine='nested').isel(height=slice(0,140))
+                ds1.attrs['sim_name'] = 'ds_librad_'+source+'_'+solver+'dl_'+nsim+''
+                ds_list.append(ds1.rename({'lat': 'lat_2.5', 'lon': 'lon_2.5', 'ddt_radlw': 'ddt_radlw_2.5'}))
+                #--------------------------------------------------
+                # delta-eddington two-stream using homogenized clouds with fractions 
+                # at 2.5 km horizontal resolution
+                ds2 = xr.open_mfdataset(path+'ds_librad_cg_*_'+source+'_'+solver+'_'+nsim+'.nc',concat_dim='time',combine='nested').isel(height=slice(0,140))
+                ds2.attrs['sim_name'] = 'ds_librad_'+source+'_'+solver+'cg_'+nsim+''
+                ds_list.append(ds2.rename({'lat': 'lat_2.5', 'lon': 'lon_2.5', 'ddt_radlw': 'ddt_radlw_2.5'}))
+    return ds_list                
+#-------------------------------------
+ds_lib1=load_simulations3(0)
+ds_lib2=load_simulations3(1)
+ds_lib3=load_simulations3(2)
+ds_lib4=load_simulations3(3)
+'''
+
+########################################################
+##### Step 2, coarse-graining CRH different calculations
+########################################################
+
+def coarse_graining(ds_list,name):
+    print(len(ds_list))
+    for i in range(len(ds_list)):  
+
+        print('working on dataset: ', i)
+
+        for res in [2.5,5,10,50,100,500]:
+
+            if res == 2.5:
+                xbin, ybin = 246, 198
+            elif res == 5:
+                xbin, ybin = 123, 99
+            elif res == 10:
+                xbin, ybin = 61, 49
+            elif res == 50:
+                xbin, ybin = 12, 10
+            elif res == 100:
+                xbin, ybin = 6, 5
+            else:
+                xbin, ybin = 1, 1  
+
+            print(f"Resolution: {res}, xbin: {xbin}, ybin: {ybin}")
+
+            tmp = ds_list[i]['ddt_radlw'].groupby_bins("lon", bins=xbin).mean(dim="lon").compute()
+            new_variable = tmp.groupby_bins("lat", bins=ybin).mean(dim="lat").compute()
+
+            # Create new dimensions for each resolution
+            new_lon_dim = f"lon_{res}"
+            new_lat_dim = f"lat_{res}"
+
+            # Extract time and height dimensions from the original dataset
+            time_dim = ds_list[i]['time'].values
+            height_dim = ds_list[i]['height'].values
+
+            # Align dimensions and assign the new variable to the dataset with new dimensions
+            ds_list[i]['ddt_radlw_' + str(res)] = xr.DataArray(new_variable,
+                                                               dims=['time', new_lon_dim, new_lat_dim,'height'],
+                                                               coords={'time': time_dim,
+                                                                       'height': height_dim,
+                                                                       new_lon_dim: np.arange(xbin),
+                                                                       new_lat_dim: np.arange(ybin)})
+        ds_list[i] = ds_list[i].drop_vars(['ddt_dom1','ddt_cs_dom1'])
+        ds_list[i].to_netcdf('/work/bb1135/b381185/icon_output/lem_clouds_diag/'+name+'_'+ds_list[i].attrs['sim_name']+'.nc')
+        
+cg_ds_lib1 = coarse_graining(ds_lib1,'dom_01')
+cg_ds_lib2 = coarse_graining(ds_lib2,'dom_02')
+cg_ds_lib3 = coarse_graining(ds_lib3,'dom_03')
+cg_ds_lib4 = coarse_graining(ds_lib4,'dom_04')
+
+########## for NWP clouds
+
+'''
+def coarse_graining(ds_list,name):
+    print(len(ds_list))
+    for i in range(len(ds_list)):  
+        print('working on dataset: ', i)
+        for res in [5,10,50,100,500]:
+
+            if res == 5:
+                xbin, ybin = 123, 99
+            elif res == 10:
+                xbin, ybin = 61, 49
+            elif res == 50:
+                xbin, ybin = 12, 10
+            elif res == 100:
+                xbin, ybin = 6, 5
+            else:
+                xbin, ybin = 1, 1  
+
+            print(f"Resolution: {res}, xbin: {xbin}, ybin: {ybin}")
+
+            tmp = ds_list[i]['ddt_radlw_2.5'].groupby_bins("lon_2.5", bins=xbin).mean(dim="lon_2.5").compute()
+            new_variable = tmp.groupby_bins("lat_2.5", bins=ybin).mean(dim="lat_2.5").compute()
+
+            # Create new dimensions for each resolution
+            new_lon_dim = f"lon_{res}"
+            new_lat_dim = f"lat_{res}"
+
+            # Extract time and height dimensions from the original dataset
+            time_dim = ds_list[i]['time'].values
+            height_dim = ds_list[i]['height'].values
+
+            # Align dimensions and assign the new variable to the dataset with new dimensions
+            ds_list[i]['ddt_radlw_' + str(res)] = xr.DataArray(new_variable,
+                                                               dims=['time', new_lon_dim, new_lat_dim,'height'],
+                                                               coords={'time': time_dim,
+                                                                       'height': height_dim,
+                                                                       new_lon_dim: np.arange(xbin),
+                                                                       new_lat_dim: np.arange(ybin)})
+        ds_list[i] = ds_list[i].drop_vars(['ddt_dom1','ddt_cs_dom1'])
+        ds_list[i].to_netcdf('/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/offline_radiation_calculation_output/lem_crhun_diag/'+name+'_'+ds_list[i].attrs['sim_name']+'.nc')
+
+cg_ds_lib1 = coarse_graining(ds_lib1,'dom_01')
+cg_ds_lib2 = coarse_graining(ds_lib2,'dom_02')
+cg_ds_lib3 = coarse_graining(ds_lib3,'dom_03')
+cg_ds_lib4 = coarse_graining(ds_lib4,'dom_04')
+
+'''
+
+##################################################
+##### Step 3, calculating CRH uncertainty
+##################################################
+
+# laoding coarse_grained CRH datasets
+def load_datasets1(name):
+    ds_list = []
+    #print('Working on loading data for', name)
+    path = '/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/offline_radiation_calculation_output/lem_crhun_diag/'
+    for source in ['thermal','solar']:
+        for solver in ['ipa3d','ipa3ddl','ipa3dcg','mysti','mcipa']:
+            ds = xr.open_dataset(path+name+'_ds_librad_'+source+'_'+solver+'_01.nc')#,chunks={'time':1 ,'height': 10})
+            ds_list.append(ds)
+    return ds_list                
+#-------------------------------------
+ds_dom01=load_datasets1('dom_01')
+ds_dom02=load_datasets1('dom_02')
+ds_dom03=load_datasets1('dom_03')
+ds_dom04=load_datasets1('dom_04')
+
+# laoding coarse_grained CRH datasets ice-optics 02
+def load_datasets2(name):
+    ds_list = []
+    print('Working on loading data for', name)
+    path = '/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/offline_radiation_calculation_output/lem_crhun_diag/'
+    for source in ['thermal','solar']:
+        for solver in ['ipa3d']:
+            ds = xr.open_dataset(path+name+'_ds_librad_'+source+'_'+solver+'_02.nc')#,chunks={'time':1 ,'height': 10})
+            ds_list.append(ds)
+    return ds_list                
+#-------------------------------------
+tmp02=load_datasets2('dom_02')
+tmp03=load_datasets2('dom_03')
+tmp04=load_datasets2('dom_04')
+
+ds_dom02.append(tmp02[0])
+ds_dom02.append(tmp02[1])
+ds_dom03.append(tmp03[0])
+ds_dom03.append(tmp03[1])
+ds_dom04.append(tmp04[0])
+ds_dom04.append(tmp04[1])
+
+# function to calculate local longwave and shortwave CRH uncertainty
+def calc_pixel_error(ds1,ds2):
+    error_list = []
+    # error at 300 m
+    variable_name = "ddt_radlw"
+    if variable_name in ds1 and variable_name in ds2:
+        # error at 300 m
+        data1 = ds1["ddt_radlw"]
+        data2 = ds2["ddt_radlw"]
+        error = (np.abs(data1 - data2).mean(dim=['lat','lon'])).mean('time').compute()
+        error_list.append(error)
+    # error at other resolutions
+    for res in [2.5,5,10,50,100,500]:
+        data1 = ds1[f"ddt_radlw_{res}"]
+        data2 = ds2[f"ddt_radlw_{res}"]
+        error = (np.abs(data1 - data2).mean(dim=[f"lat_{res}",f"lon_{res}"])).mean('time').compute()
+        error_list.append(error)
+    return error_list
+
+# function to calculate local net CRH uncertainty 
+def calc_pixel_error_net(ds1,ds2,ds3,ds4):
+    error_list = []
+    # error at 300 m
+    variable_name = "ddt_radlw"
+    if variable_name in ds1 and variable_name in ds3:
+        # calc net CRH
+        net1 = ds1["ddt_radlw"] + ds2["ddt_radlw"]
+        net2 = ds3["ddt_radlw"] + ds4["ddt_radlw"]
+        # error at 300 m
+        error = (np.abs(net1 - net2).mean(dim=['lat','lon'])).mean('time').compute()
+        error_list.append(error)
+    # error at other resolutions
+    for res in [2.5,5,10,50,100,500]:
+        # calc net CRH
+        net1 = ds1[f"ddt_radlw_{res}"]+ds2[f"ddt_radlw_{res}"]
+        net2 = ds3[f"ddt_radlw_{res}"]+ds4[f"ddt_radlw_{res}"]
+        # calc error
+        error = (np.abs(net1 - net2).mean(dim=[f"lat_{res}",f"lon_{res}"])).mean('time').compute()
+        error_list.append(error)
+    return error_list
+
+# function to call above functions for each LEM domain
+def calc_errors(ds, indices):
+    lw_error = calc_pixel_error(ds[indices[0]], ds[indices[1]])
+    sw_error = calc_pixel_error(ds[indices[2]], ds[indices[3]])
+    net_error = calc_pixel_error_net(ds[indices[0]], ds[indices[2]], ds[indices[1]], ds[indices[3]])
+    return lw_error, sw_error, net_error
+'''
+Ovrveiw of the indexes
+####Thermal:
+0=ipa3d, 1=ipa3ddl, 2=ipa3dcg, 3=mysti, 4=mcipa
+####Solar:
+5=ipa3d, 6=ipa3ddl, 7=ipa3dcg, 8=mysti, 9=mcipa
+For domains 2, 3, 4:
+10=ipa3d_2 thermal, 11=ipa3d_2 solar
+'''
+def get_indices_for_error_type(error_type):
+    if error_type == '3d':
+        return np.array([3, 4, 8, 9])
+    elif error_type == 'hg':
+        return np.array([0, 1, 5, 6])
+    elif error_type == 'vo':
+        return np.array([0, 2, 5, 7])
+    elif error_type == 'ic':
+        return np.array([0, 10, 5, 11])
+    else:
+        raise ValueError(f'Invalid error type: {error_type}')
+        
+# Calling the function and doing the calculations
+datasets = [ds_dom01]
+error_type = ['3d','hg','vo']
+# looping over LEM domains and calculating errors
+for ds, domain_name in zip(datasets, ['dom01']):
+    for etype in error_type:
+        indices = get_indices_for_error_type(etype)
+        print(f'###### Working on Domain {datasets.index(ds) + 1}, Error Type {etype.upper()}')
+        var_name = f'{domain_name}_{etype}_error'
+        print(f'{var_name} CRH unc')
+        globals()[var_name] = calc_errors(ds, indices)
+            
+#####################################################################################################            
+datasets = [ds_dom02,ds_dom03,ds_dom04]
+error_type = ['3d','hg','vo','ic']
+# looping over LEM domains and calculating errors
+for ds, domain_name in zip(datasets, ['dom02','dom03','dom04']):
+    for etype in error_type:
+        indices = get_indices_for_error_type(etype)
+        print(f'###### Working on Domain {datasets.index(ds) + 1}, Error Type {etype.upper()}')
+        var_name = f'{domain_name}_{etype}_error'
+        print(f'{var_name} CRH unc')
+        globals()[var_name] = calc_errors(ds, indices)      
+        
+ds = xr.Dataset(
+    data_vars={"dom01_3d_lw_error": (("res1", "height"), xr.concat(dom01_3d_error[0], dim="res1").values),
+               "dom01_3d_sw_error": (("res1", "height"), xr.concat(dom01_3d_error[1], dim="res1").values),
+               "dom01_3d_nt_error": (("res1", "height"), xr.concat(dom01_3d_error[2], dim="res1").values),
+               "dom01_hg_lw_error": (("res2", "height"), xr.concat(dom01_hg_error[0], dim="res2").values),
+               "dom01_hg_sw_error": (("res2", "height"), xr.concat(dom01_hg_error[1], dim="res2").values),
+               "dom01_hg_nt_error": (("res2", "height"), xr.concat(dom01_hg_error[2], dim="res2").values),
+               "dom01_vo_lw_error": (("res2", "height"), xr.concat(dom01_vo_error[0], dim="res2").values),
+               "dom01_vo_sw_error": (("res2", "height"), xr.concat(dom01_vo_error[1], dim="res2").values),
+               "dom01_vo_nt_error": (("res2", "height"), xr.concat(dom01_vo_error[2], dim="res2").values),
+               
+               "dom02_3d_lw_error": (("res1", "height"), xr.concat(dom02_3d_error[0], dim="res1").values),
+               "dom02_3d_sw_error": (("res1", "height"), xr.concat(dom02_3d_error[1], dim="res1").values),
+               "dom02_3d_nt_error": (("res1", "height"), xr.concat(dom02_3d_error[2], dim="res1").values),
+               "dom02_hg_lw_error": (("res2", "height"), xr.concat(dom02_hg_error[0], dim="res2").values),
+               "dom02_hg_sw_error": (("res2", "height"), xr.concat(dom02_hg_error[1], dim="res2").values),
+               "dom02_hg_nt_error": (("res2", "height"), xr.concat(dom02_hg_error[2], dim="res2").values),
+               "dom02_vo_lw_error": (("res2", "height"), xr.concat(dom02_vo_error[0], dim="res2").values),
+               "dom02_vo_sw_error": (("res2", "height"), xr.concat(dom02_vo_error[1], dim="res2").values),
+               "dom02_vo_nt_error": (("res2", "height"), xr.concat(dom02_vo_error[2], dim="res2").values),
+               "dom02_ic_lw_error": (("res1", "height"), xr.concat(dom02_ic_error[0], dim="res1").values),
+               "dom02_ic_sw_error": (("res1", "height"), xr.concat(dom02_ic_error[1], dim="res1").values),
+               "dom02_ic_nt_error": (("res1", "height"), xr.concat(dom02_ic_error[2], dim="res1").values),
+               
+               "dom03_3d_lw_error": (("res1", "height"), xr.concat(dom03_3d_error[0], dim="res1").values),
+               "dom03_3d_sw_error": (("res1", "height"), xr.concat(dom03_3d_error[1], dim="res1").values),
+               "dom03_3d_nt_error": (("res1", "height"), xr.concat(dom03_3d_error[2], dim="res1").values),
+               "dom03_hg_lw_error": (("res2", "height"), xr.concat(dom03_hg_error[0], dim="res2").values),
+               "dom03_hg_sw_error": (("res2", "height"), xr.concat(dom03_hg_error[1], dim="res2").values),
+               "dom03_hg_nt_error": (("res2", "height"), xr.concat(dom03_hg_error[2], dim="res2").values),
+               "dom03_vo_lw_error": (("res2", "height"), xr.concat(dom03_vo_error[0], dim="res2").values),
+               "dom03_vo_sw_error": (("res2", "height"), xr.concat(dom03_vo_error[1], dim="res2").values),
+               "dom03_vo_nt_error": (("res2", "height"), xr.concat(dom03_vo_error[2], dim="res2").values),
+               "dom03_ic_lw_error": (("res1", "height"), xr.concat(dom03_ic_error[0], dim="res1").values),
+               "dom03_ic_sw_error": (("res1", "height"), xr.concat(dom03_ic_error[1], dim="res1").values),
+               "dom03_ic_nt_error": (("res1", "height"), xr.concat(dom03_ic_error[2], dim="res1").values),
+               
+               "dom04_3d_lw_error": (("res1", "height"), xr.concat(dom04_3d_error[0], dim="res1").values),
+               "dom04_3d_sw_error": (("res1", "height"), xr.concat(dom04_3d_error[1], dim="res1").values),
+               "dom04_3d_nt_error": (("res1", "height"), xr.concat(dom04_3d_error[2], dim="res1").values),
+               "dom04_hg_lw_error": (("res2", "height"), xr.concat(dom04_hg_error[0], dim="res2").values),
+               "dom04_hg_sw_error": (("res2", "height"), xr.concat(dom04_hg_error[1], dim="res2").values),
+               "dom04_hg_nt_error": (("res2", "height"), xr.concat(dom04_hg_error[2], dim="res2").values),
+               "dom04_vo_lw_error": (("res2", "height"), xr.concat(dom04_vo_error[0], dim="res2").values),
+               "dom04_vo_sw_error": (("res2", "height"), xr.concat(dom04_vo_error[1], dim="res2").values),
+               "dom04_vo_nt_error": (("res2", "height"), xr.concat(dom04_vo_error[2], dim="res2").values),
+               "dom04_ic_lw_error": (("res1", "height"), xr.concat(dom04_ic_error[0], dim="res1").values),
+               "dom04_ic_sw_error": (("res1", "height"), xr.concat(dom04_ic_error[1], dim="res1").values),
+               "dom04_ic_nt_error": (("res1", "height"), xr.concat(dom04_ic_error[2], dim="res1").values),
+               
+               
+              },
+    coords={
+        "height": (["height"], np.arange(140)),
+        "res1": (["res1"], np.array([0.3, 2.5, 5, 10, 50, 100, 500])),
+        "res2": (["res2"], np.array([2.5, 5, 10, 50, 100, 500])),
+    }
+)
+# save the final data
+ds.to_netcdf('/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/offline_radiation_calculation_output/lem_crhun_diag/crh_unc_domain_mean.nc')
diff --git a/offlineRT/pre_process_data_for_archiving.py b/offlineRT/pre_process_data_for_archiving.py
new file mode 100644
index 0000000000000000000000000000000000000000..d9c55958ccb3ac7c0afeeb803337682af22e93f9
--- /dev/null
+++ b/offlineRT/pre_process_data_for_archiving.py
@@ -0,0 +1,283 @@
+#@ Behrooz Keshtgar, KIT 2024
+
+# This script is for cleaning up the data for archiving
+# and publication
+
+#### Loading libraries
+import numpy as np
+import matplotlib.pyplot as plt
+import xarray as xr
+
+#### Dictionary for loading datasets for the 4 simulations
+simdict = {
+         'lem_domain01' : {'name':'Shallow cumulus'          ,'res':'300', 'radiation':4, 'mphy':4}, # Only cloud radiation
+         'lem_domain02' : {'name':'WCB ascent'               ,'res':'300', 'radiation':4, 'mphy':4}, # Only cloud radiation
+         'lem_domain03' : {'name':'WCB cyclonic outflow'     ,'res':'300', 'radiation':4, 'mphy':4}, # Only cloud radiation
+         'lem_domain04' : {'name':'WCB anticyclonic outflow' ,'res':'300', 'radiation':4, 'mphy':4}  # Only cloud radiation
+          }
+
+##############################################################################################
+##### ICON post-processed data
+##############################################################################################
+
+# Loading icon datasets
+def load_simulations(num):
+    d_icon = []
+    sim = list(simdict.keys())[num]
+    print('Working on loading data for', sim)
+    # loading all subdomains
+    path_i = '/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/ICON_postprocessed_data/'+sim+'/'
+    for time in ['20220105T100033Z','20220105T103033Z','20220105T110033Z','20220105T113033Z','20220105T120033Z','20220105T123033Z','20220105T130033Z','20220105T133033Z','20220105T140033Z']:
+        ds_a = []
+        for dom in range(1,37):
+            ds = xr.open_mfdataset(path_i+'icon2_'+str(dom)+'_'+time+'.nc',concat_dim='lon')[['ddt_radlw','ddt_radsw']].isel(height=slice(0,140))
+            ds_a.append(ds) 
+        # concat along x dim
+        ds_j=[]        
+        for j in range(0,36,6):
+            ds = xr.concat([ds_a[j],ds_a[j+1],ds_a[j+2],ds_a[j+3],ds_a[j+4],ds_a[j+5]],dim='lon')
+            ds_j.append(ds)
+        # concat along y dim    
+        ds_temp = xr.concat([ds_j[0],ds_j[1],ds_j[2],ds_j[3],ds_j[4],ds_j[5]],dim='lat') 
+        # change units to K/day 
+        ds_temp['lwcrh'] = ds_temp['ddt_radlw']*86400
+        ds_temp['swcrh'] = ds_temp['ddt_radsw']*86400
+
+        ds_temp.coords['lon'] = np.arange(0,344*6)
+        ds_temp.coords['lat'] = np.arange(0,281*6)
+        ds_temp.coords['time'] = time
+        
+        # also adding rho
+        ds_temp2 = xr.open_dataset(path_i+'icon_rho_'+time+'.nc').isel(height=slice(0,140),lon=slice(1,2064),lat=slice(1,1686))
+        ds_mg = xr.merge([ds_temp,ds_temp2])
+        d_icon.append(ds_mg)
+
+    # merge them together in time
+    ds_icon = xr.concat([d_icon[0],d_icon[1],d_icon[2],d_icon[3],d_icon[4],d_icon[5],d_icon[6]
+                     ,d_icon[7],d_icon[8]],dim='time')
+    ds = ds_icon.chunk(chunks={'time': 1, 'height': 50})
+    return ds
+#---------------------------
+ds_icon1=load_simulations(0)
+ds_icon2=load_simulations(1)
+ds_icon3=load_simulations(2)
+ds_icon4=load_simulations(3)
+
+# model height at full-levels
+z_ifc = xr.open_dataset('/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/ICON_LEM_DOM02_lon40_lat44_300m/icon-atm3d_ML_20220105T120033Z.nc')["z_ifc"].isel(ncells=20000)
+z_fl  = ((z_ifc - z_ifc.diff('height_3')/2).values)*1e-3 # km
+# select between index 0:140
+z_fl2  = z_fl[10:150][::-1]
+z_fl2 = xr.DataArray(z_fl2, dims=('height'), coords={'height': ds_icon3['height']})
+
+# assign full model levels to datasets
+ds_icon1_u = ds_icon1.assign(z_mc=z_fl2).drop_vars(['ddt_radlw','ddt_radsw'])
+ds_icon2_u = ds_icon2.assign(z_mc=z_fl2).drop_vars(['ddt_radlw','ddt_radsw'])
+ds_icon3_u = ds_icon3.assign(z_mc=z_fl2).drop_vars(['ddt_radlw','ddt_radsw'])
+ds_icon4_u = ds_icon4.assign(z_mc=z_fl2).drop_vars(['ddt_radlw','ddt_radsw'])
+
+# encode and save as nc files
+encoding_settings = {var: {'zlib': True, 'complevel': 4} for var in ds_icon3_u.variables}
+ds_icon1_u.to_netcdf('/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/postprocessed_data_for_publication/shallow_cumulus/icon_pp_data.nc', encoding=encoding_settings)
+
+encoding_settings = {var: {'zlib': True, 'complevel': 4} for var in ds_icon3_u.variables}
+ds_icon2_u.to_netcdf('/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/postprocessed_data_for_publication/WCB_ascent/icon_pp_data.nc', encoding=encoding_settings)
+
+encoding_settings = {var: {'zlib': True, 'complevel': 4} for var in ds_icon3_u.variables}
+ds_icon3_u.to_netcdf('/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/postprocessed_data_for_publication/WCB_cyclonic_outflow/icon_pp_data.nc', encoding=encoding_settings)
+
+encoding_settings = {var: {'zlib': True, 'complevel': 4} for var in ds_icon4_u.variables}
+ds_icon4_u.to_netcdf('/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/postprocessed_data_for_publication/WCB_anticyclonic_outflow/icon_pp_data.nc', encoding=encoding_settings)
+
+##############################################################################################
+##### MYSTIC calculations
+##############################################################################################
+def load_simulations1(num):
+    ds_list = []
+    sim = list(simdict.keys())[num]
+    print('Working on loading data for', sim)
+    path = '/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/offline_radiation_calculation_output/'+sim+'/'
+    for solver in ['mysti']:
+        # loop over sources (thermal/solar)
+        for source in ['thermal','solar']:
+            # loop over sim types
+            for nsim in ['01']: # simulations with Fu for ice param
+                ds = xr.open_mfdataset(path+'ds_librad_05T*_'+source+'_'+solver+'_'+nsim+'.nc',concat_dim='time',combine='nested',
+                                       chunks={'height': 50},parallel=True).isel(height=slice(0,140))
+                ds.attrs['sim_name'] = 'ds_librad_'+source+'_'+solver+'_'+nsim+''
+                ds_list.append(ds)
+    return ds_list                
+#-------------------------------------
+ds_lib1=load_simulations1(0)
+ds_lib2=load_simulations1(1)
+ds_lib3=load_simulations1(2)
+ds_lib4=load_simulations1(3)
+
+names = ['shallow_cumulus','WCB_ascent','WCB_cyclonic_outflow','WCB_anticyclonic_outflow']
+for i in range(1,5):
+    sim = names[i-1]
+    print(sim)
+    dataset_name = f'ds_lib{i}'  # Construct the dataset name
+    dataset = globals()[dataset_name]
+    lwcrh = dataset[0]['ddt_dom1'].isel(time=8)
+    swcrh = dataset[1]['ddt_dom1'].isel(time=8)
+    # Create a dataset without loading the data into memory
+    ds = xr.Dataset(
+        data_vars={
+            "lwrh": (lwcrh.dims, lwcrh.data),
+            "swrh": (swcrh.dims, swcrh.data),
+        },
+        coords=lwcrh.coords  # Copy coordinates from lwcrh or swcrh
+    )
+    ds = ds.assign(z_mc=z_fl2)
+    ds.attrs['sim_name'] = 'RH from libradtran calculation for '+sim+' domain, LEM clouds, MYSTIC solver, Fu'
+    # Save to netCDF without loading the entire dataset into memory
+    encoding_settings = {var: {'zlib': True, 'complevel': 4} for var in ds.variables}
+    ds.to_netcdf("/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/postprocessed_data_for_publication/"+sim+"/libradtran_pp_mystic_RH_Fu.nc", encoding=encoding_settings)
+    
+##############################################################################################
+##### MYSTIC-ICA calculations
+##############################################################################################
+def load_simulations1(num):
+    ds_list = []
+    sim = list(simdict.keys())[num]
+    print('Working on loading data for', sim)
+    path = '/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/offline_radiation_calculation_output/'+sim+'/'
+    for solver in ['mcipa']:
+        # loop over sources (thermal/solar)
+        for source in ['thermal','solar']:
+            # loop over sim types
+            for nsim in ['01']: # simulations with Fu for ice param
+                ds = xr.open_mfdataset(path+'ds_librad_05T*_'+source+'_'+solver+'_'+nsim+'.nc',concat_dim='time',combine='nested',
+                                       chunks={'height': 50},parallel=True).isel(height=slice(0,140))
+                ds.attrs['sim_name'] = 'ds_librad_'+source+'_'+solver+'_'+nsim+''
+                ds_list.append(ds)
+    return ds_list                
+#-------------------------------------
+ds_lib1=load_simulations1(0)
+ds_lib2=load_simulations1(1)
+ds_lib3=load_simulations1(2)
+ds_lib4=load_simulations1(3)
+
+names = ['shallow_cumulus','WCB_ascent','WCB_cyclonic_outflow','WCB_anticyclonic_outflow']
+for i in range(1,5):
+    sim = names[i-1]
+    print(sim)
+    dataset_name = f'ds_lib{i}'  # Construct the dataset name
+    dataset = globals()[dataset_name]
+    lwcrh = dataset[0]['ddt_radlw']
+    swcrh = dataset[1]['ddt_radlw']
+    # Create a dataset without loading the data into memory
+    ds = xr.Dataset(
+        data_vars={
+            "lwcrh": (lwcrh.dims, lwcrh.data),
+            "swcrh": (swcrh.dims, swcrh.data),
+        },
+        coords=lwcrh.coords  # Copy coordinates from lwcrh or swcrh
+    )
+    ds = ds.assign(z_mc=z_fl2)
+    ds.attrs['sim_name'] = 'CRH from libradtran calculation for '+sim+' domain, LEM clouds, MYSTIC-ICA solver, Fu'
+    # Save to netCDF without loading the entire dataset into memory
+    encoding_settings = {var: {'zlib': True, 'complevel': 4} for var in ds.variables}
+    ds.to_netcdf("/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/postprocessed_data_for_publication/"+sim+"/libradtran_pp_mystic_ica_Fu.nc", encoding=encoding_settings)
+    
+##############################################################################################
+##### Twostream-Fu calculations
+##############################################################################################
+def load_simulations1(num):
+    ds_list = []
+    sim = list(simdict.keys())[num]
+    print('Working on loading data for', sim)
+    path = '/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/offline_radiation_calculation_output/'+sim+'/'
+    for solver in ['ipa3d']:
+        # loop over sources (thermal/solar)
+        for source in ['thermal','solar']:
+            # loop over sim types
+            for nsim in ['01']: # simulations with Fu for ice param ['02','03','04'] for Baum-ghm, Baum-sc, Baum-rg
+                ds = xr.open_mfdataset(path+'ds_librad2_05T*_'+source+'_'+solver+'_'+nsim+'.nc',concat_dim='time',combine='nested',
+                                       chunks={'height': 50},parallel=True).isel(height=slice(0,140))
+                ds.attrs['sim_name'] = 'ds_librad_'+source+'_'+solver+'_'+nsim+''
+                ds_list.append(ds)
+    return ds_list                
+#-------------------------------------
+ds_lib1=load_simulations1(0)
+ds_lib2=load_simulations1(1)
+ds_lib3=load_simulations1(2)
+ds_lib4=load_simulations1(3)
+
+names = ['shallow_cumulus','WCB_ascent','WCB_cyclonic_outflow','WCB_anticyclonic_outflow']
+for i in range(1,5):
+    sim = names[i-1]
+    print(sim)
+    dataset_name = f'ds_lib{i}'  # Construct the dataset name
+    dataset = globals()[dataset_name]
+    lwcrh = dataset[0]['ddt_radlw']
+    swcrh = dataset[1]['ddt_radlw']
+    # Create a dataset without loading the data into memory
+    ds = xr.Dataset(
+        data_vars={
+            "lwcrh": (lwcrh.dims, lwcrh.data),
+            "swcrh": (swcrh.dims, swcrh.data),
+        },
+        coords=lwcrh.coords  # Copy coordinates from lwcrh or swcrh
+    )
+    ds = ds.assign(z_mc=z_fl2)
+    ds.attrs['sim_name'] = 'CRH from libradtran calculation for '+sim+' domain, LEM clouds, twostream solver, Fu'
+    # Save to netCDF without loading the entire dataset into memory
+    encoding_settings = {var: {'zlib': True, 'complevel': 4} for var in ds.variables}
+    ds.to_netcdf("/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/postprocessed_data_for_publication/"+sim+"/libradtran_pp_twostr_Fu.nc", encoding=encoding_settings)
+    
+##############################################################################################
+##### twostream- Fu for NWP clouds calculations
+##############################################################################################
+def load_simulations2(num):
+    ds_list = []
+    sim = list(simdict.keys())[num]
+    print('Working on loading data for', sim)
+    path = '/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/offline_radiation_calculation_output/'+sim+'/'
+    for solver in ['ipa3d']:
+        # loop over sources (thermal/solar)
+        for source in ['thermal','solar']:
+            # loop over sim types
+            for nsim in ['01']: # simulations with ice param of Fu (1998)
+                #--------------------------------------------------
+                # delta-eddington two-stream using homogenized clouds 
+                # with 2.5 km horizontal resolution
+                #ds1 = xr.open_mfdataset(path+'ds_librad_dl_*_'+source+'_'+solver+'_'+nsim+'.nc',concat_dim='time',combine='nested').isel(height=slice(0,140))
+                #ds1.attrs['sim_name'] = 'ds_librad_'+source+'_'+solver+'dl_'+nsim+''
+                #ds_list.append(ds1)
+                #--------------------------------------------------
+                # delta-eddington two-stream using homogenized clouds with fractions 
+                # at 2.5 km horizontal resolution
+                ds2 = xr.open_mfdataset(path+'ds_librad_cg_*_'+source+'_'+solver+'_'+nsim+'.nc',concat_dim='time',combine='nested').isel(height=slice(0,140))
+                ds2.attrs['sim_name'] = 'ds_librad_'+source+'_'+solver+'cg_'+nsim+''
+                ds_list.append(ds2)
+    return ds_list
+
+#-------------------------------------
+ds_lib1=load_simulations2(0)
+ds_lib2=load_simulations2(1)
+ds_lib3=load_simulations2(2)
+ds_lib4=load_simulations2(3)
+
+names = ['shallow_cumulus','WCB_ascent','WCB_cyclonic_outflow','WCB_anticyclonic_outflow']
+for i in range(1,5):
+    sim = names[i-1]
+    print(sim)
+    dataset_name = f'ds_lib{i}'  # Construct the dataset name
+    dataset = globals()[dataset_name]
+    lwcrh = dataset[0]['ddt_radlw']
+    swcrh = dataset[1]['ddt_radlw']
+    # Create a dataset without loading the data into memory
+    ds = xr.Dataset(
+        data_vars={
+            "lwcrh": (lwcrh.dims, lwcrh.data),
+            "swcrh": (swcrh.dims, swcrh.data),
+        },
+        coords=lwcrh.coords  # Copy coordinates from lwcrh or swcrh
+    )
+    ds = ds.assign(z_mc=z_fl2)
+    ds.attrs['sim_name'] = 'CRH from libradtran calculation for '+sim+' domain, NWP homogenized clouds with cloud fraction, twostream solver, Fu'
+    # Save to netCDF without loading the entire dataset into memory
+    encoding_settings = {var: {'zlib': True, 'complevel': 4} for var in ds.variables}
+    ds.to_netcdf("/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/postprocessed_data_for_publication/"+sim+"/libradtran_pp_nwpfrcld_twostr_Fu.nc", encoding=encoding_settings)
\ No newline at end of file
diff --git a/offlineRT/solar_clear_sky/input_run.sh b/offlineRT/solar_clear_sky/input_run.sh
new file mode 100755
index 0000000000000000000000000000000000000000..77c2f68906a9d81649c0f96702792e3d13cd798e
--- /dev/null
+++ b/offlineRT/solar_clear_sky/input_run.sh
@@ -0,0 +1,121 @@
+#!/usr/bin/env bash
+
+. VARIABLES
+
+
+pathh=$(cd ../../ && pwd)
+INP_FILE_NAME='libsetup.inp'
+LIBRAD="/home/b/b381185/libRadtran"
+
+mkdir output
+cd output
+
+for sol in "${solver[@]}"
+do
+    echo 'solver = ' $sol
+
+for it in "${time_array[@]}"
+do
+    echo 'time = ' $it
+
+i=0
+for dm in "${dom[@]}"
+do
+echo 'dom = ' $dm
+if [ $it == 05T1000 ]
+then
+        echo 'sza = ' "${sza_05T1000[$i]}"
+        sza="${sza_05T1000[$i]}"
+elif [ $it == 05T1030 ]
+then
+        echo 'sza = ' "${sza_05T1030[$i]}"
+        sza="${sza_05T1030[$i]}"
+elif [ $it == 05T1100 ]
+then
+        echo 'sza = ' "${sza_05T1100[$i]}"
+        sza="${sza_05T1100[$i]}"
+elif [ $it == 05T1130 ]
+then
+        echo 'sza = ' "${sza_05T1130[$i]}"
+        sza="${sza_05T1130[$i]}"
+elif [ $it == 05T1200 ]
+then
+        echo 'sza = ' "${sza_05T1200[$i]}"
+        sza="${sza_05T1200[$i]}"
+elif [ $it == 05T1230 ]
+then
+        echo 'sza = ' "${sza_05T1230[$i]}"
+        sza="${sza_05T1230[$i]}"
+elif [ $it == 05T1300 ]
+then
+        echo 'sza = ' "${sza_05T1300[$i]}"
+        sza="${sza_05T1300[$i]}"
+elif [ $it == 05T1330 ]
+then
+        echo 'sza = ' "${sza_05T1330[$i]}"
+        sza="${sza_05T1330[$i]}"
+else
+        echo 'sza = ' "${sza_05T1400[$i]}"
+        sza="${sza_05T1400[$i]}"
+fi
+i=$i+1
+
+OUT_FILE=$sol'_'$it'_'$dm'_cs_hr.out'
+
+############################
+# CREATE INPUT FILE HERE !!!
+cat > $INP_FILE_NAME << EOF
+data_files_path $LIBRAD/data/
+atmosphere_file ${pathh}/atmosphere_mean_${dm}_202201${it}33Z.dat
+#source solar /work/bb1135/b381185/tools/libRadtran/data/data/solar_flux/kato
+
+albedo 0.07
+source solar
+
+sza ${sza}
+#phi ${phi}
+  
+mol_abs_param Fu   
+wavelength_index 1  7   
+output_process sum   
+
+# gas profiles
+#mixing_ratio CO2 348
+#mixing_ratio O2 209460
+#mixing_ratio CH4 1.650 
+#mixing_ratio N2O 0.396
+
+mixing_ratio F11 0.0
+mixing_ratio F12 0.0
+mixing_ratio F22 0.0
+mixing_ratio NO2 0.0
+
+mol_modify O4 0.0 DU
+mol_modify BRO 0.0 DU
+mol_modify OCLO 0.0 DU
+mol_modify HCHO 0.0 DU
+mol_modify SO2 0.0 DU
+mol_modify CO 0.0 DU
+mol_modify N2 0.0 DU
+
+#surface
+albedo_library IGBP
+brdf_rpv_type 17
+
+rte_solver $sol
+
+heating_rate layer_fd 
+
+zout 0.000000 0.020000 0.051914 0.090769 0.135002 0.183759 0.236489 0.292801 0.352401 0.415061 0.480594 0.548850 0.619700 0.693036 0.768763 0.846801 0.927078 1.009531 1.094104 1.180746 1.269413 1.360064 1.452663 1.547177 1.643575 1.741831 1.841918 1.943816 2.047502 2.152959 2.260168 2.369114 2.479784 2.592163 2.706240 2.822004 2.939446 3.058556 3.179328 3.301754 3.425828 3.551544 3.678898 3.807886 3.938505 4.070751 4.204624 4.340121 4.477242 4.615985 4.756352 4.898344 5.041960 5.187202 5.334074 5.482577 5.632714 5.784490 5.937907 6.092970 6.249683 6.408053 6.568084 6.729782 6.893155 7.058208 7.224949 7.393386 7.563527 7.735380 7.908954 8.084258 8.261303 8.440098 8.620654 8.802982 8.987094 9.173002 9.360718 9.550255 9.741626 9.934847 10.129930 10.326890 10.525744 10.726509 10.929198 11.133832 11.340426 11.549001 11.759574 11.972166 12.186796 12.403487 12.622261 12.843139 13.066146 13.291306 13.518643 13.748186 13.979961 14.213994 14.450318 14.688961 14.929955 15.173333 15.419130 15.667381 15.918122 16.171394 16.427235 16.685688 16.946798 17.210609 17.477171 17.746536 18.018753 18.293882 18.571980 18.853111 19.137339 19.424730 19.715363 20.009314 20.306667 20.607512 20.911943 21.220062 21.531981 21.847816 22.167702 22.491774 22.820189 23.153118 23.490747 23.833288 24.180979 24.534081 24.892902 25.257799 25.629181 26.007547 26.393496 26.787775 27.191345 27.605484 28.031990 28.473600 28.934996 29.426264
+
+quiet
+
+EOF
+### END OF INPUT FILE ###########
+
+#run libradtran
+$LIBRAD/bin/uvspec <$INP_FILE_NAME> $OUT_FILE
+
+done
+done
+done
diff --git a/offlineRT/thermal_clear_sky/VARIABLES b/offlineRT/thermal_clear_sky/VARIABLES
new file mode 100644
index 0000000000000000000000000000000000000000..69d8009a75e82705fc2afd011be80f62873ce382
--- /dev/null
+++ b/offlineRT/thermal_clear_sky/VARIABLES
@@ -0,0 +1,11 @@
+export solver=("mystic" "twostr")
+
+export time_array=("05T1000" "05T1030" "05T1100" "05T1130" "05T1200" "05T1230" "05T1300" "05T1330" "05T1400")
+
+export dom=("1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12" "13" "14" "15" "16" "17" "18" "19" "20" "21" "22" "23" "24" "25" "26" "27" "28" "29" "30" "31" "32" "33" "34" "35" "36")
+
+export dom1=("1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12" "13" "14" "15" "16" "17")
+
+export dom2=("18" "19" "20" "21" "22" "23" "24" "25" "26" "27" "28" "29" "30" "31" "32" "33" "34" "35" "36")
+
+export isim_array=("01" "04")
diff --git a/offlineRT/thermal_clear_sky/input_run.sh b/offlineRT/thermal_clear_sky/input_run.sh
new file mode 100755
index 0000000000000000000000000000000000000000..49f07ad6278124d158b1c3492211736c3d4210ae
--- /dev/null
+++ b/offlineRT/thermal_clear_sky/input_run.sh
@@ -0,0 +1,78 @@
+#!/usr/bin/env bash
+
+. VARIABLES
+
+pathh=$(cd ../../ && pwd)
+INP_FILE_NAME='libsetup.inp'
+LIBRAD="/home/b/b381185/libRadtran"
+
+mkdir output
+cd output
+
+for sol in "${solver[@]}"
+do
+    echo 'solver = ' $sol
+
+for it in "${time_array[@]}"
+do
+    echo 'time = ' $it
+
+for dm in "${dom[@]}"
+do
+    echo 'dom = ' $dm
+
+OUT_FILE=$sol'_'$it'_'$dm'_cs_hr.out'
+
+
+############################
+# CREATE INPUT FILE HERE !!!
+cat > $INP_FILE_NAME << EOF
+data_files_path $LIBRAD/data/
+atmosphere_file ${pathh}/atmosphere_mean_${dm}_202201${it}33Z.dat
+
+albedo 0.009000000000000008
+source thermal  
+mol_abs_param Fu   
+wavelength_index 7  18   
+output_process sum   
+
+# gas profiles
+#mixing_ratio CO2 348
+#mixing_ratio O2 209460
+#mixing_ratio CH4 1.650 
+#mixing_ratio N2O 0.396
+
+mixing_ratio F11 0.0
+mixing_ratio F12 0.0
+mixing_ratio F22 0.0
+mixing_ratio NO2 0.0
+
+mol_modify O4 0.0 DU
+mol_modify BRO 0.0 DU
+mol_modify OCLO 0.0 DU
+mol_modify HCHO 0.0 DU
+mol_modify SO2 0.0 DU
+mol_modify CO 0.0 DU
+mol_modify N2 0.0 DU
+
+#surface
+albedo_library IGBP
+brdf_rpv_type 17
+
+rte_solver $sol
+
+heating_rate layer_fd 
+
+zout 0.000000 0.020000 0.051914 0.090769 0.135002 0.183759 0.236489 0.292801 0.352401 0.415061 0.480594 0.548850 0.619700 0.693036 0.768763 0.846801 0.927078 1.009531 1.094104 1.180746 1.269413 1.360064 1.452663 1.547177 1.643575 1.741831 1.841918 1.943816 2.047502 2.152959 2.260168 2.369114 2.479784 2.592163 2.706240 2.822004 2.939446 3.058556 3.179328 3.301754 3.425828 3.551544 3.678898 3.807886 3.938505 4.070751 4.204624 4.340121 4.477242 4.615985 4.756352 4.898344 5.041960 5.187202 5.334074 5.482577 5.632714 5.784490 5.937907 6.092970 6.249683 6.408053 6.568084 6.729782 6.893155 7.058208 7.224949 7.393386 7.563527 7.735380 7.908954 8.084258 8.261303 8.440098 8.620654 8.802982 8.987094 9.173002 9.360718 9.550255 9.741626 9.934847 10.129930 10.326890 10.525744 10.726509 10.929198 11.133832 11.340426 11.549001 11.759574 11.972166 12.186796 12.403487 12.622261 12.843139 13.066146 13.291306 13.518643 13.748186 13.979961 14.213994 14.450318 14.688961 14.929955 15.173333 15.419130 15.667381 15.918122 16.171394 16.427235 16.685688 16.946798 17.210609 17.477171 17.746536 18.018753 18.293882 18.571980 18.853111 19.137339 19.424730 19.715363 20.009314 20.306667 20.607512 20.911943 21.220062 21.531981 21.847816 22.167702 22.491774 22.820189 23.153118 23.490747 23.833288 24.180979 24.534081 24.892902 25.257799 25.629181 26.007547 26.393496 26.787775 27.191345 27.605484 28.031990 28.473600 28.934996 29.426264
+
+quiet
+#verbose
+EOF
+### END OF INPUT FILE ###########
+
+#run libradtran
+$LIBRAD/bin/uvspec <$INP_FILE_NAME> $OUT_FILE
+
+done
+done
+done
diff --git a/plots4paper/figure1.ipynb b/plots4paper/figure1.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..456e9aeca24239eba5349e8ba0ad424558a95847
--- /dev/null
+++ b/plots4paper/figure1.ipynb
@@ -0,0 +1,411 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "fd7e7d31-662b-4c80-9cf1-74565756f22e",
+   "metadata": {},
+   "source": [
+    "# Figure 1\n",
+    "\n",
+    "**Figure 1.** (a) Surface pressure (black contours, hPa), cloud cover, and precipitation at day 4.5 in the baroclinic life cycle simulation with ICON-NWP. Panel (b) shows cloud classes. The rectangles in both panels indicate the location of the domains for the LEM simulations.\n",
+    "\n",
+    "---\n",
+    "@ Behrooz Keshtgar, KIT 2024"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c9d6b860-5669-460a-85e3-9b4cc7fd6ba0",
+   "metadata": {},
+   "source": [
+    "## 1- load python packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "bc71da64-aaec-4035-8689-3f8be0728fbc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import xarray as xr\n",
+    "import pandas as pd\n",
+    "import matplotlib as mpl\n",
+    "import colorlegend\n",
+    "from matplotlib.ticker import (MultipleLocator, AutoMinorLocator)\n",
+    "import matplotlib.colors as mcolors\n",
+    "from matplotlib import cm\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04dd62ca-cf4b-42a2-85ef-2469f19bf6fc",
+   "metadata": {},
+   "source": [
+    "For reference, print package versions to screen:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "41f5bd93-ad99-44d4-ade6-2d7684bf34b6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "xarrary:    0.16.0\n",
+      "numpy:      1.23.5\n",
+      "matplotlib: 3.3.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('xarrary:   ', xr.__version__)\n",
+    "print('numpy:     ', np.__version__)\n",
+    "import matplotlib; print('matplotlib:', matplotlib.__version__); del matplotlib"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9af0ecad-9a06-4b17-9c7f-948c2e9180aa",
+   "metadata": {},
+   "source": [
+    "## 2- Loading datasets "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "db4e1350-0029-4443-acd4-973b225f8fb3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load post-precessed baroclinic life cycle simulation (remapped to 2.5 km grid resolution) \n",
+    "ds = xr.open_mfdataset('/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/postprocessed_data_for_publication/NWP_blc_sim/icon-atm2d_ML_reg_20*.nc')\n",
+    "# deriving precipitation rate\n",
+    "ds['tot_prec_dt'] = ds['tot_prec'].diff(dim='time', label='upper') #mm/hr\n",
+    "# adjusting time coordinates\n",
+    "rng = pd.date_range('2022-01-01', periods=216, freq=\"1 H\")\n",
+    "df = pd.DataFrame({ 'Date': rng })\n",
+    "t =  df.values[:,0]\n",
+    "ds.coords['time'] = t"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "77b8296a-ed31-4688-8a43-312a5d06aefe",
+   "metadata": {},
+   "source": [
+    "## 3- Cloud classification"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f36e1364-bdc7-4a52-8430-a105fabba3b4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# The threshold for the cloud fraction to identify different cloud classes\n",
+    "thres=50\n",
+    "\n",
+    "# function that gets 2-D cloud fraction data at High, Mid and Low level intervals and creates classes for the vertical column\n",
+    "def cloud_class(cc_data,thres):\n",
+    "    \n",
+    "    # CL1: H \n",
+    "    # CL2: M \n",
+    "    # CL3: L \n",
+    "    # CL4: HM \n",
+    "    # CL5: ML \n",
+    "    # CL6: HL \n",
+    "    # CL7: HML \n",
+    "    # CL8: clear-sky\n",
+    "    \n",
+    "    # Create the vertical cloud classes based on the threshold (thres)\n",
+    "    \n",
+    "    cl1 = cc_data.where((cc_data.clch<thres) & (cc_data.clcm<thres) & (cc_data.clcl>thres)) # L\n",
+    "    cl1_num = cl1.where(xr.ufuncs.isnan(cl1.clch),other=1)\n",
+    "    cl1_num = cl1_num.where((xr.ufuncs.isnan(cl1_num.clch))==False,other=0)\n",
+    "    \n",
+    "\n",
+    "    cl2 = cc_data.where((cc_data.clch<thres) & (cc_data.clcm>thres) & (cc_data.clcl<thres)) # M\n",
+    "    cl2_num = cl2.where(xr.ufuncs.isnan(cl2.clch),other=2)\n",
+    "    cl2_num = cl2_num.where((xr.ufuncs.isnan(cl2_num.clch))==False,other=0)\n",
+    "\n",
+    "\n",
+    "    cl3     = cc_data.where((cc_data.clch>thres) & (cc_data.clcm<thres) & (cc_data.clcl<thres)) # H\n",
+    "    cl3_num = cl3.where(xr.ufuncs.isnan(cl3.clch),other=3)\n",
+    "    cl3_num = cl3_num.where((xr.ufuncs.isnan(cl3_num.clch))==False,other=0)\n",
+    "    \n",
+    "    \n",
+    "    cl4 = cc_data.where((cc_data.clch<thres) & (cc_data.clcm>thres) & (cc_data.clcl>thres)) # ML\n",
+    "    cl4_num = cl4.where(xr.ufuncs.isnan(cl4.clch),other=4)\n",
+    "    cl4_num = cl4_num.where((xr.ufuncs.isnan(cl4_num.clch))==False,other=0)\n",
+    "\n",
+    "    \n",
+    "    cl5 = cc_data.where((cc_data.clch>thres) & (cc_data.clcm>thres) & (cc_data.clcl<thres)) # HM\n",
+    "    cl5_num = cl5.where(xr.ufuncs.isnan(cl5.clch),other=5)\n",
+    "    cl5_num = cl5_num.where((xr.ufuncs.isnan(cl5_num.clch))==False,other=0)\n",
+    "    \n",
+    "\n",
+    "    cl6 = cc_data.where((cc_data.clch>thres) & (cc_data.clcm<thres) & (cc_data.clcl>thres)) # HL\n",
+    "    cl6_num = cl6.where(xr.ufuncs.isnan(cl6.clch),other=6)\n",
+    "    cl6_num = cl6_num.where((xr.ufuncs.isnan(cl6_num.clch))==False,other=0)\n",
+    "\n",
+    "    \n",
+    "    cl7 = cc_data.where((cc_data.clch>thres) & (cc_data.clcm>thres) & (cc_data.clcl>thres)) # HML\n",
+    "    cl7_num = cl7.where(xr.ufuncs.isnan(cl7.clch),other=7)\n",
+    "    cl7_num = cl7_num.where((xr.ufuncs.isnan(cl7_num.clch))==False,other=0)\n",
+    "\n",
+    "    \n",
+    "    cl8 = cc_data.where((cc_data.clch<thres) & (cc_data.clcm<thres) & (cc_data.clcl<thres)) # clear-sky\n",
+    "    cl8_num = cl8.where(xr.ufuncs.isnan(cl8.clch),other=8)\n",
+    "    cl8_num = cl8_num.where((xr.ufuncs.isnan(cl8_num.clch))==False,other=0)\n",
+    "\n",
+    "    # we sum all the classes to one array to make a mask for the entire domain based on the cloud classification\n",
+    "    cloud_class_mask   = cl1+cl2+cl3+cl4+cl5+cl6+cl7+cl8 \n",
+    "    cloud_class_number = cl1_num+cl2_num+cl3_num+cl4_num+cl5_num+cl6_num+cl7_num+cl8_num\n",
+    "    \n",
+    "    return cloud_class_number #,cloud_class_mask\n",
+    "\n",
+    "\n",
+    "cloud_class_num = cloud_class(ds[['clcm','clch','clcl']].sel(time='2022-01-05T12:00:00.000000000'),thres)\n",
+    "    \n",
+    "A = cloud_class_num.clcm.squeeze().values\n",
+    "\n",
+    "bounds = np.linspace(np.nanmin(A), int(np.nanmax(A))+1, int(np.nanmax(A))+1)\n",
+    "cbar_lbls = ['Low','Middle','High','Middle-Low','High-Middle','High-Low','High-Middle-Low','Clear-sky']\n",
+    "\n",
+    "# creating a colorbar to distinguish between cloud classes\n",
+    "cmapb = cm.Blues(np.linspace(0.3,0.8,3))\n",
+    "cmapr = cm.Reds(np.linspace(0.2,1,5))\n",
+    "cmapt = np.concatenate((cmapb, cmapr), axis=0)\n",
+    "cmapt[7] = np.array([0,0,0,0])\n",
+    "cmapt[5] = np.array([0.6, 0.6, 0.6, 1.]) #Gray\n",
+    "\n",
+    "cmap2 = mcolors.ListedColormap(cmapt)\n",
+    "norm = mpl.colors.BoundaryNorm(bounds, cmap2.N)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03734a5d-73fd-43e6-9dab-588c68801032",
+   "metadata": {},
+   "source": [
+    "### For data publication"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "94b32aad-7b86-4d4e-aee1-ee85774c2d0c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Variables required for the plot\n",
+    "prec_rate   = ds['tot_prec_dt'].sel(time='2022-01-05T12:00:00.000000000')\n",
+    "pres_sfc    = ds['pres_sfc'].sel(time='2022-01-05T12:00:00.000000000')/100 # Pa -> hPa\n",
+    "cloud_cover = ds['clct'].sel(time='2022-01-05T12:00:00.000000000')\n",
+    "cloud_class = cloud_class_num['clcm'].squeeze()\n",
+    "lon = ds['lon']-38\n",
+    "lat = ds['lat']\n",
+    "\n",
+    "# creating a dataset and save for data publication\n",
+    "ds_out = xr.Dataset(\n",
+    "    data_vars={\n",
+    "        \"prec_rate\"  : (prec_rate.dims, prec_rate.data),\n",
+    "        \"pres_sfc\"   : (pres_sfc.dims, pres_sfc.data),\n",
+    "        \"cloud_cover\": (cloud_cover.dims, cloud_cover.data),\n",
+    "        \"cloud_class\": (cloud_class.dims, cloud_class.data),\n",
+    "    },\n",
+    "    coords={\"lat\": ([\"lat\"], lat), \n",
+    "            \"lon\": ([\"lon\"], lon)})\n",
+    "\n",
+    "ds_out.attrs['description'] = 'precipitation rate, cloud cover, surface pressure and cloud classes at day 4.5 of NWP baroclinic life cycle simulation'\n",
+    "ds_out.to_netcdf('/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/postprocessed_data_for_publication/for_publication/figure1.nc')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9e08f15e-e5fa-497b-838e-d4a885882cc6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "ds_out = xr.open_dataset('/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/postprocessed_data_for_publication/for_publication/figure1.nc')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c99c8764-60a0-4b5c-89b4-120048dc5b9b",
+   "metadata": {},
+   "source": [
+    "## 4- Plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "f09e0528-4773-44eb-989c-d0cae64fc94e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 865.691x402.02 with 5 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lat = ds_out['lat']\n",
+    "lon = ds_out['lon']\n",
+    "\n",
+    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(12, 5))\n",
+    "i = 0\n",
+    "# position of the LES domains\n",
+    "a1 = np.array([-16,-10,-10,-16,-16])\n",
+    "b1 = np.array([37,37,43,43,37])\n",
+    "\n",
+    "a2 = np.array([-11,-5,-5,-11,-11])\n",
+    "b2 = np.array([50,50,56,56,50])\n",
+    "\n",
+    "a3 = np.array([-1,5,5,-1,-1])\n",
+    "b3 = np.array([41,41,47,47,41])\n",
+    "\n",
+    "a4 = np.array([9,15,15,9,9])\n",
+    "b4 = np.array([45,45,51,51,45])\n",
+    "\n",
+    "for ax in axes.flat:\n",
+    "    \n",
+    "    ax.tick_params(labelsize=14)\n",
+    "    ax.set_ylim([25,65])\n",
+    "        \n",
+    "    if i == 0:\n",
+    "        ax.set_title('Day 4.5', fontsize=14)\n",
+    "        \n",
+    "        # cloud cover contourf\n",
+    "        im1=ax.contourf(lon,lat,ds_out['cloud_cover'],cmap=plt.cm.get_cmap('Blues_r'),levels=np.arange(0.0,101,1))\n",
+    "        cbaxes1 = fig.add_axes([0.465, 0.12, 0.02, 0.76]) \n",
+    "        cbar1 = fig.colorbar(im1, ax=ax,orientation='vertical',cax = cbaxes1,ticks=np.arange(0,120,20))\n",
+    "        cbar1.set_label(label='Cloud cover (%)', size='13')\n",
+    "        cbar1.ax.tick_params(labelsize=12)\n",
+    "        \n",
+    "        # surface pressure contours\n",
+    "        cs=ax.contour(lon,lat,ds_out['pres_sfc'],colors='k',linewidths=0.5,levels=np.arange(930,1027,7))\n",
+    "        plt.clabel(cs, inline=5, fontsize=9,fmt='%1.f')\n",
+    "\n",
+    "        # precipitation rate contourf\n",
+    "        im2 = ax.contourf(lon,lat,ds_out['prec_rate'],levels=np.arange(0.1,10.1,0.1),cmap='rainbow',extend='max') \n",
+    "        cbaxes2 = fig.add_axes([0.125, -0.04, 0.36, 0.045]) \n",
+    "        cbar2 = fig.colorbar(im2, ax=ax,orientation='horizontal',cax = cbaxes2,ticks=[0.1,1,2,3,4,5,6,7,8,9,10])\n",
+    "        cbar2.set_label(label='Precipitation rate (mm hr$^{-1}$)', size='13')\n",
+    "        cbar2.ax.tick_params(labelsize=12)\n",
+    "\n",
+    "\n",
+    "        ax.set_ylabel('Latitude', fontsize=14)\n",
+    "        ax.set_xlabel('Longitude', fontsize=14)\n",
+    "        \n",
+    "        ax.plot(a1,b1,color='k',linewidth=2)\n",
+    "        ax.plot(a2,b2,color='k',linewidth=2)\n",
+    "        ax.plot(a3,b3,color='k',linewidth=2)\n",
+    "        ax.plot(a4,b4,color='k',linewidth=2)\n",
+    "        \n",
+    "        ax.text(-16,44, 'Dom 1', fontsize=8,size=9,fontweight='bold',color='r')\n",
+    "        ax.text(-1,48, 'Dom 2', fontsize=8,size=9,fontweight='bold',color='r')\n",
+    "        ax.text(-11,57, 'Dom 3', fontsize=8,size=9,fontweight='bold',color='r')\n",
+    "        ax.text(9,52, 'Dom 4', fontsize=8,size=9,fontweight='bold',color='r')\n",
+    "        \n",
+    "        ax.text(0.0, 1.03, '(a)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 1:\n",
+    "        ax.set_title('Day 4.5', fontsize=14)\n",
+    "        \n",
+    "        # surface pressure contours\n",
+    "        cs=ax.contour(lon,lat,ds_out['pres_sfc'],colors='k',linewidths=0.5,levels=np.arange(930,1027,7))\n",
+    "        plt.clabel(cs, inline=5, fontsize=9,fmt='%1.f')\n",
+    "        \n",
+    "        # cloud classes\n",
+    "        im3 = ax.pcolormesh(lon,lat,ds_out['cloud_class'].values,cmap=cmap2,norm=norm)\n",
+    "        ax.set_yticklabels([])\n",
+    "        \n",
+    "        ax.set_xlabel('Longitude',fontsize=14)\n",
+    "        \n",
+    "        ax.plot(a1,b1,color='k',linewidth=2)\n",
+    "        ax.plot(a2,b2,color='k',linewidth=2)\n",
+    "        ax.plot(a3,b3,color='k',linewidth=2)\n",
+    "        ax.plot(a4,b4,color='k',linewidth=2)\n",
+    "        \n",
+    "        \n",
+    "        ax.text(-16,44, 'Dom 1', fontsize=8,size=9,fontweight='bold',color='k')\n",
+    "        ax.text(-1,48, 'Dom 2', fontsize=8,size=9,fontweight='bold',color='k')\n",
+    "        ax.text(-11,57, 'Dom 3', fontsize=8,size=9,fontweight='bold',color='k')\n",
+    "        ax.text(9,52, 'Dom 4', fontsize=8,size=9,fontweight='bold',color='k')\n",
+    "        \n",
+    "        ax.text(0.0, 1.03, '(b)', transform=ax.transAxes, \n",
+    "            size=14)    \n",
+    "        \n",
+    "    i = i + 1\n",
+    "      \n",
+    "fig.subplots_adjust(wspace=0.4)    \n",
+    "    \n",
+    "cb_ax1 = fig.add_axes([0.918, 0.12, 0.02, 0.76]) # xcenter/ycenter/width/height\n",
+    "cbar1 = fig.colorbar(im3,cax=cb_ax1,orientation='vertical',shrink=0.95)\n",
+    "cbar1.set_ticklabels(cbar_lbls)\n",
+    "minorticks = np.arange(0.5, 10, 1)\n",
+    "cbar1.set_ticks(minorticks)\n",
+    "cbar1.ax.tick_params(labelsize=12) \n",
+    "\n",
+    "plt.savefig('figure1.pdf', format='pdf', bbox_inches='tight', dpi=100, compression=9)\n",
+    "#plt.savefig('figure1.png', bbox_inches = 'tight',dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e589f5b4-0e9d-4e60-bd64-340acbc44334",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pycrh",
+   "language": "python",
+   "name": "pycrh"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "115f93b6-8a4f-4f98-a0dc-2c79cceb1a7a",
+   "metadata": {},
+   "source": [
+    "# Figure 10\n",
+    "\n",
+    "**Figure 10.** Mean uncertainties in CRH diagnosed as the absolute difference of domain and time mean CRH between different radiative transfer calculations. Uncertainties are decomposed into shortwave, longwave, and net. Uncertainties are computed as mass-weighted averages over 2 km altitude intervals. For the uncertainty due to the ice-optical parameterization, the difference between the ice schemes of Fu and Baum with the general habit mixture is used. The contribution of each factor is given by the horizontal length of its colored bar. Note the different x-axes in the panels.\n",
+    "\n",
+    "---\n",
+    "@ Behrooz Keshtgar, KIT 2024"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c69547d-1bf2-4b27-957a-95e15f48812c",
+   "metadata": {},
+   "source": [
+    "## 1- load python packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "039407e9-ae55-437b-8d36-b67a5aa76fa2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import xarray as xr\n",
+    "import pandas as pd\n",
+    "import colorlegend\n",
+    "from matplotlib.ticker import (MultipleLocator, AutoMinorLocator)\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bf9aea5-6e6e-46c9-925d-ce53a9042b21",
+   "metadata": {},
+   "source": [
+    "For reference, print package versions to screen:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "090549dc-8a05-4a15-a43a-2bef20f073d4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "xarrary:    0.16.0\n",
+      "numpy:      1.23.5\n",
+      "matplotlib: 3.3.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('xarrary:   ', xr.__version__)\n",
+    "print('numpy:     ', np.__version__)\n",
+    "import matplotlib; print('matplotlib:', matplotlib.__version__); del matplotlib"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4d0c18c1-504e-4de3-b1dd-ea51d3e86d7b",
+   "metadata": {},
+   "source": [
+    "**Since datasets are large, I use DASK to speed up my analysis**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f1b5de08-7b30-46a2-b571-0ee9c4b81aa4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table style=\"border: 2px solid white;\">\n",
+       "<tr>\n",
+       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
+       "<h3 style=\"text-align: left;\">Client</h3>\n",
+       "<ul style=\"text-align: left; list-style: none; margin: 0; padding: 0;\">\n",
+       "  <li><b>Scheduler: </b>tcp://127.0.0.1:44355</li>\n",
+       "  <li><b>Dashboard: </b><a href='/user/b381185/levante-spawner-preset//proxy/8787/status' target='_blank'>/user/b381185/levante-spawner-preset//proxy/8787/status</a></li>\n",
+       "</ul>\n",
+       "</td>\n",
+       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
+       "<h3 style=\"text-align: left;\">Cluster</h3>\n",
+       "<ul style=\"text-align: left; list-style:none; margin: 0; padding: 0;\">\n",
+       "  <li><b>Workers: </b>16</li>\n",
+       "  <li><b>Cores: </b>256</li>\n",
+       "  <li><b>Memory: </b>252.72 GB</li>\n",
+       "</ul>\n",
+       "</td>\n",
+       "</tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Client: 'tcp://127.0.0.1:44355' processes=16 threads=256, memory=252.72 GB>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import dask\n",
+    "from dask.distributed import Client, progress, wait\n",
+    "dask.config.config.get('distributed').get('dashboard').update({'link':'{JUPYTERHUB_SERVICE_PREFIX}/proxy/{port}/status'})\n",
+    "client = Client()\n",
+    "client"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b54d56a2-732a-41c8-b030-41e1bbf7542b",
+   "metadata": {},
+   "source": [
+    "## 2- Loading datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "25f15e36-be39-4756-ac4d-c8c1e79cabc5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Dictionary for loading datasets for the 4 LEM domains\n",
+    "domdict = {\n",
+    "         'shallow_cumulus'          : {'res':'300m'}, \n",
+    "         'WCB_ascent'               : {'res':'300m'}, \n",
+    "         'WCB_cyclonic_outflow'     : {'res':'300m'}, \n",
+    "         'WCB_anticyclonic_outflow' : {'res':'300m'}\n",
+    "          }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "939ec90c-4d26-4103-b148-40c1b292a43c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Working on loading data for shallow_cumulus\n",
+      "Working on loading data for WCB_ascent\n",
+      "Working on loading data for WCB_cyclonic_outflow\n",
+      "Working on loading data for WCB_anticyclonic_outflow\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Loading icon-pp datasets\n",
+    "def load_data():\n",
+    "    list_icon = []\n",
+    "    for dom in list(domdict.keys()):\n",
+    "        path = '/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/postprocessed_data_for_publication/'\n",
+    "        print('Working on loading data for', dom)\n",
+    "        ds = xr.open_dataset(path+dom+'/icon_pp_data.nc').chunk(chunks={'time': 1, 'height': 10})\n",
+    "        list_icon.append(ds)\n",
+    "    return list_icon\n",
+    "#-------------------------------------------------------------------------------------------------\n",
+    "list_icon=load_data()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "42c1d05c-5b0b-41e9-9094-b21b62a41292",
+   "metadata": {},
+   "source": [
+    "### Domain and time average of density"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "359476e1-ee13-491c-823b-dfc1b8dda28b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# takin domain average\n",
+    "# icon datasets\n",
+    "for dom in range(len(list_icon)):\n",
+    "    for var in ['rho']:\n",
+    "        list_icon[dom][var+'_mean'] = list_icon[dom][var].isel(lon=slice(10,list_icon[dom].lon.size-10),lat=slice(5,list_icon[dom].lat.size-5)).mean(dim=['lat','lon','time']).compute()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7ffb98f3-d613-4c3b-a4ae-4eaaae048a76",
+   "metadata": {},
+   "source": [
+    "### CRH profiles from previous analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "17197b49-0a32-4209-8337-ec881307a0cb",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "fig6 = xr.open_dataset('/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/postprocessed_data_for_publication/for_publication/figure6.nc')\n",
+    "fig8 = xr.open_dataset('/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/postprocessed_data_for_publication/for_publication/figure8.nc')\n",
+    "fig9 = xr.open_dataset('/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/postprocessed_data_for_publication/for_publication/figure9.nc')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd6f1e1a-0f7c-40ff-b23c-9d548ae5caa9",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### For data publication"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "edbe3660-190e-46e6-9732-cae4c1a3d2d5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# creating a dataset and save for data publication\n",
+    "ds_out = xr.Dataset(\n",
+    "    data_vars={\n",
+    "        \"rho_mean_icon_dom01\"        : (list_icon[0]['rho_mean'].dims, list_icon[0]['rho_mean'].data),\n",
+    "        \"rho_mean_icon_dom02\"        : (list_icon[1]['rho_mean'].dims, list_icon[1]['rho_mean'].data),\n",
+    "        \"rho_mean_icon_dom03\"        : (list_icon[2]['rho_mean'].dims, list_icon[2]['rho_mean'].data),\n",
+    "        \"rho_mean_icon_dom04\"        : (list_icon[3]['rho_mean'].dims, list_icon[3]['rho_mean'].data),\n",
+    "  \n",
+    "    },\n",
+    "    coords=list_icon[0]['rho_mean'].coords)\n",
+    "\n",
+    "ds_out = xr.merge([fig6,fig8,fig9,ds_out])\n",
+    "\n",
+    "ds_out.attrs['description'] = 'Vertical profiles of CRH and density from different offline radiation calculations for each LEM domain'\n",
+    "ds_out.to_netcdf('/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/postprocessed_data_for_publication/for_publication/figure10.nc')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f4bba5f4-58f2-4c84-88c2-fbb794bcf029",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "ds_out = xr.open_dataset('/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/postprocessed_data_for_publication/for_publication/figure10.nc')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "872417af-0e22-4887-8302-df6e590da9bb",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## 3- Calculating weighted vertical mean of absolute mean differences"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "ec256ad6-c58c-477a-b930-e6732c6b2b32",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def absolute_mean_difference(ds, dom, rho):\n",
+    "    \"\"\"\n",
+    "    Calculate AMD and weighted vertical mean in vertical intervals of 2 km.\n",
+    "    Input is a dataset containing time and spatial mean from different radiation calculations.\n",
+    "    \"\"\"\n",
+    "    # thickness\n",
+    "    dp = np.zeros((140))\n",
+    "    dp[1:-1] = (rho[2:] - rho[:-2]) \n",
+    "    dp[0] = (rho[1] - rho[0]) \n",
+    "    dp[-1] = (rho[-1] - rho[-2])\n",
+    "    dp = dp[::-1]\n",
+    "    \n",
+    "    amd_list = []\n",
+    "    # Define functions for repeated calculations\n",
+    "    def calculate_amd_vm(data, dp):\n",
+    "        return np.array([(np.sum(data[i:j] * dp[i:j]) / np.sum(dp[i:j])) for i, j in zip([0, 28, 45, 59, 71, 82], [28, 45, 59, 71, 82, 92])])\n",
+    "\n",
+    "    def calculate_and_append_amd_vm(prefix1, prefix2, ds):\n",
+    "        if prefix1 == 'smean_mystic':\n",
+    "            lw_amd = np.abs((ds[f'lwcrh_{prefix1}_{dom}'].mean('time') - ds[f'lwcrh_{prefix2}_{dom}'].mean('time')).values)\n",
+    "            sw_amd = np.abs((ds[f'swcrh_{prefix1}_{dom}'].mean('time') - ds[f'swcrh_{prefix2}_{dom}'].mean('time')).values)\n",
+    "            \n",
+    "            net1 = ds[f'lwcrh_{prefix1}_{dom}'].mean('time') + ds[f'swcrh_{prefix1}_{dom}'].mean('time')\n",
+    "            net2 = ds[f'lwcrh_{prefix2}_{dom}'].mean('time') + ds[f'swcrh_{prefix2}_{dom}'].mean('time')\n",
+    "            nt_amd = np.abs((net1 - net2).values)\n",
+    "            \n",
+    "        else:\n",
+    "            lw_amd = np.abs((ds[f'lwcrh_{prefix1}_{dom}'] - ds[f'lwcrh_{prefix2}_{dom}']).values)\n",
+    "            sw_amd = np.abs((ds[f'swcrh_{prefix1}_{dom}'] - ds[f'swcrh_{prefix2}_{dom}']).values)\n",
+    "            \n",
+    "            net1 = ds[f'lwcrh_{prefix1}_{dom}'] + ds[f'swcrh_{prefix1}_{dom}']\n",
+    "            net2 = ds[f'lwcrh_{prefix2}_{dom}'] + ds[f'swcrh_{prefix2}_{dom}']\n",
+    "            nt_amd = np.abs((net1 - net2).values)\n",
+    "        \n",
+    "        lw_amd_vm = calculate_amd_vm(lw_amd, dp)\n",
+    "        sw_amd_vm = calculate_amd_vm(sw_amd, dp)\n",
+    "        nt_amd_vm = calculate_amd_vm(nt_amd, dp)\n",
+    "    \n",
+    "        amd_list.append(lw_amd_vm)\n",
+    "        amd_list.append(sw_amd_vm)\n",
+    "        amd_list.append(nt_amd_vm)\n",
+    "\n",
+    "    # 3D cloud radiative effects\n",
+    "    calculate_and_append_amd_vm('smean_mystic', 'smean_mystic_ica', ds)\n",
+    "\n",
+    "    # Impact of cloud horizontal heterogeneity\n",
+    "    calculate_and_append_amd_vm('mean_nwp', 'mean_lem', ds)\n",
+    "\n",
+    "    # Impact of cloud horizontal heterogeneity and cloud vertical overlap\n",
+    "    calculate_and_append_amd_vm('mean_nwpfr', 'mean_lem', ds)\n",
+    "\n",
+    "    # Impact of ice-optical param\n",
+    "    if dom == 'dom01':\n",
+    "        amd_list.extend([np.zeros(6), np.zeros(6), np.zeros(6)])\n",
+    "    else:\n",
+    "        calculate_and_append_amd_vm('mean_fu', 'mean_Baum_ghm', ds)\n",
+    "\n",
+    "    return amd_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "67c238ac-ea4a-4507-b188-ad7fc9ec2e0f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "amd_dom01 = absolute_mean_difference(ds_out,'dom01',ds_out['rho_mean_icon_dom01'].values)\n",
+    "amd_dom02 = absolute_mean_difference(ds_out,'dom02',ds_out['rho_mean_icon_dom02'].values)\n",
+    "amd_dom03 = absolute_mean_difference(ds_out,'dom03',ds_out['rho_mean_icon_dom03'].values)\n",
+    "amd_dom04 = absolute_mean_difference(ds_out,'dom04',ds_out['rho_mean_icon_dom04'].values)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f5a483de-2599-46a4-923f-932309dd5663",
+   "metadata": {},
+   "source": [
+    "## 4- Plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "6d17ceae-bb43-4314-bd21-437fd89d6ea5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 12 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# height intervals\n",
+    "h1 = np.arange(0,6,1)\n",
+    "\n",
+    "fig, axes = plt.subplots(nrows=3, ncols=4, figsize=(20, 10)) \n",
+    "i = 0\n",
+    "\n",
+    "for ax in axes.flat:\n",
+    "    ax.tick_params(labelsize=14)\n",
+    "    ax.spines['top'].set_color('none')\n",
+    "    ax.spines['right'].set_color('none')\n",
+    "    \n",
+    "    ############################################################\n",
+    "    ####### Longwave ###########################################\n",
+    "    ############################################################\n",
+    "    if i == 0:\n",
+    "\n",
+    "        ax.barh(h1, amd_dom01[0], color='#000075', label='3D effetcs')\n",
+    "        ax.barh(h1, amd_dom01[3], color='#4363d8', left=amd_dom01[0], label='Horizontal heterogeneity')\n",
+    "        ax.barh(h1, amd_dom01[6], color='#469990', left=amd_dom01[0]+amd_dom01[3], label='Horizontal heterogeneity and vertical overlap')\n",
+    "        ax.barh(h1, amd_dom01[9], color='#e6194B', left=amd_dom01[0]+amd_dom01[3]+amd_dom01[6], label='Ice-optical parameterization')\n",
+    "        #lg=colorlegend.color_legend(ax,loc=1,fsize=11)\n",
+    "        #ax.legend(frameon=False)\n",
+    "        ax.set_title('Shallow cumulus', fontsize=15,pad=35)\n",
+    "        #ax.set_xlabel('CRH uncertainty (K day$^{-1}$)', fontsize=14)\n",
+    "        ax.set_ylabel('Height intervals (km)', fontsize=14)\n",
+    "\n",
+    "        ax.set_ylim([-0.5,5.5])\n",
+    "        ax.spines['left'].set_bounds(-0.5,5.5)\n",
+    "        ax.set_yticks([0,1,2,3,4,5])\n",
+    "        ax.set_yticklabels(['0-2','2-4','4-6','6-8','8-10','10-12'])\n",
+    "        \n",
+    "        ax.set_xlim([0.,1.5])\n",
+    "        ax.spines['bottom'].set_bounds(0,1.5)\n",
+    "        ax.set_xticks(np.linspace(0,1.5,6))\n",
+    "        minor_locator = AutoMinorLocator(2)\n",
+    "        ax.xaxis.set_minor_locator(minor_locator)\n",
+    "        \n",
+    "        ax.text(0.0, 1.03, '(a)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "        ax.text(0.08, 5.2, '3D effects', color = '#000075', size=12, va=\"top\")\n",
+    "        ax.text(0.08, 4.5, 'Cloud horizontal heterogeneity,\\nwithout overlap assumption', color='#4363d8', size=12, va=\"top\")\n",
+    "        ax.text(0.08, 3.2, 'Cloud horizontal heterogeneity,\\nwith overlap assumption', color='#469990', size=12, va=\"top\")\n",
+    "        ax.text(0.08, 1.9, 'Ice-optical parameterization', color='#e6194B', size=12, va=\"top\")\n",
+    "        \n",
+    "    if i == 1:\n",
+    "        \n",
+    "        ax.barh(h1, amd_dom02[0], color='#000075', label='3D effetcs')\n",
+    "        ax.barh(h1, amd_dom02[3], color='#4363d8', left=amd_dom02[0], label='Horizontal heterogeneity')\n",
+    "        ax.barh(h1, amd_dom02[6], color='#469990', left=amd_dom02[0]+amd_dom02[3], label='Horizontal heterogeneity and vertical overlap')\n",
+    "        ax.barh(h1, amd_dom02[9], color='#e6194B', left=amd_dom02[0]+amd_dom02[3]+amd_dom02[6], label='Ice-optical parameterization')\n",
+    "        ax.set_title('WCB ascent', fontsize=15,pad=35)\n",
+    "\n",
+    "        ax.set_ylim([-0.5,5.5])\n",
+    "        ax.spines['left'].set_bounds(-0.5,5.5)\n",
+    "        ax.set_yticks([0,1,2,3,4,5])\n",
+    "        ax.set_yticklabels(['0-2','2-4','4-6','6-8','8-10','10-12'])\n",
+    "        ax.set_yticklabels([])\n",
+    "        \n",
+    "        ax.set_xlim([0.,0.6])\n",
+    "        ax.spines['bottom'].set_bounds(0,0.6)\n",
+    "        ax.set_xticks( np.linspace(0,0.6,7))\n",
+    "        minor_locator = AutoMinorLocator(2)\n",
+    "        ax.xaxis.set_minor_locator(minor_locator)\n",
+    "        \n",
+    "        ax.text(0.0, 1.03, '(d)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 2:\n",
+    "        \n",
+    "        ax.barh(h1, amd_dom03[0], color='#000075', label='3D effetcs')\n",
+    "        ax.barh(h1, amd_dom03[3], color='#4363d8', left=amd_dom03[0], label='Horizontal heterogeneity')\n",
+    "        ax.barh(h1, amd_dom03[6], color='#469990', left=amd_dom03[0]+amd_dom03[3], label='Horizontal heterogeneity and vertical overlap')\n",
+    "        ax.barh(h1, amd_dom03[9], color='#e6194B', left=amd_dom03[0]+amd_dom03[3]+amd_dom03[6], label='Ice-optical parameterization')\n",
+    "        ax.set_title('WCB cyclonic outflow', fontsize=15,pad=35)\n",
+    "\n",
+    "        ax.set_ylim([-0.5,5.5])\n",
+    "        ax.spines['left'].set_bounds(-0.5,5.5)\n",
+    "        ax.set_yticks([0,1,2,3,4,5])\n",
+    "        ax.set_yticklabels(['0-2','2-4','4-6','6-8','8-10','10-12'])\n",
+    "        ax.set_yticklabels([])\n",
+    "        \n",
+    "        ax.set_xlim([0.,0.6])\n",
+    "        ax.spines['bottom'].set_bounds(0,0.6)\n",
+    "        ax.set_xticks( np.linspace(0,0.6,7))\n",
+    "        minor_locator = AutoMinorLocator(2)\n",
+    "        ax.xaxis.set_minor_locator(minor_locator)\n",
+    "        \n",
+    "        ax.text(0.0, 1.03, '(g)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 3:\n",
+    "        \n",
+    "        ax.barh(h1, amd_dom04[0], color='#000075', label='3D effetcs')\n",
+    "        ax.barh(h1, amd_dom04[3], color='#4363d8', left=amd_dom04[0], label='Horizontal heterogeneity')\n",
+    "        ax.barh(h1, amd_dom04[6], color='#469990', left=amd_dom04[0]+amd_dom04[3], label='Horizontal heterogeneity and vertical overlap')\n",
+    "        ax.barh(h1, amd_dom04[9], color='#e6194B', left=amd_dom04[0]+amd_dom04[3]+amd_dom04[6], label='Ice-optical parameterization')\n",
+    "        ax.set_title('WCB anticyclonic outflow', fontsize=15,pad=35)\n",
+    "\n",
+    "        ax.set_ylim([-0.5,5.5])\n",
+    "        ax.spines['left'].set_bounds(-0.5,5.5)\n",
+    "        ax.set_yticks([0,1,2,3,4,5])\n",
+    "        ax.set_yticklabels(['0-2','2-4','4-6','6-8','8-10','10-12'])\n",
+    "        ax.set_yticklabels([])\n",
+    "        \n",
+    "        ax.set_xlim([0.,0.6])\n",
+    "        ax.spines['bottom'].set_bounds(0,0.6)\n",
+    "        ax.set_xticks( np.linspace(0,0.6,7))\n",
+    "        minor_locator = AutoMinorLocator(2)\n",
+    "        ax.xaxis.set_minor_locator(minor_locator)\n",
+    "        \n",
+    "        ax.text(0.0, 1.03, '(j)', transform=ax.transAxes, \n",
+    "            size=14)   \n",
+    "    \n",
+    "    ################################################################## \n",
+    "    ## Shortwave\n",
+    "    ##################################################################\n",
+    "    \n",
+    "    if i == 4:\n",
+    "\n",
+    "        ax.barh(h1, amd_dom01[1], color='#000075', label='3D effetcs')\n",
+    "        ax.barh(h1, amd_dom01[4], color='#4363d8', left=amd_dom01[1], label='Horizontal heterogeneity')\n",
+    "        ax.barh(h1, amd_dom01[7], color='#469990', left=amd_dom01[1]+amd_dom01[4], label='Horizontal heterogeneity and vertical overlap')\n",
+    "        ax.barh(h1, amd_dom01[10], color='#e6194B', left=amd_dom01[1]+amd_dom01[4]+amd_dom01[7], label='Ice-optical parameterization')\n",
+    "        \n",
+    "        ax.set_ylabel('Height intervals (km)', fontsize=14)\n",
+    "        ax.set_ylim([-0.5,5.5])\n",
+    "        ax.spines['left'].set_bounds(-0.5,5.5)\n",
+    "        ax.set_yticks([0,1,2,3,4,5])\n",
+    "        ax.set_yticklabels(['0-2','2-4','4-6','6-8','8-10','10-12'])\n",
+    "        ax.set_xlim([0.0,0.6])\n",
+    "        ax.spines['bottom'].set_bounds(0,0.6)\n",
+    "        ax.set_xticks(np.linspace(0,0.6,7))\n",
+    "        minor_locator = AutoMinorLocator(2)\n",
+    "        ax.xaxis.set_minor_locator(minor_locator)\n",
+    "        ax.text(0.0, 1.03, '(b)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 5:\n",
+    "\n",
+    "        ax.barh(h1, amd_dom02[1], color='#000075', label='3D effetcs')\n",
+    "        ax.barh(h1, amd_dom02[4], color='#4363d8', left=amd_dom02[1], label='Horizontal heterogeneity')\n",
+    "        ax.barh(h1, amd_dom02[7], color='#469990', left=amd_dom02[1]+amd_dom02[4], label='Horizontal heterogeneity and vertical overlap')\n",
+    "        ax.barh(h1, amd_dom02[10], color='#e6194B', left=amd_dom02[1]+amd_dom02[4]+amd_dom02[7], label='Ice-optical parameterization')\n",
+    "        \n",
+    "        ax.set_ylim([-0.5,5.5])\n",
+    "        ax.spines['left'].set_bounds(-0.5,5.5)\n",
+    "        ax.set_yticks([0,1,2,3,4,5])\n",
+    "        ax.set_yticklabels(['0-2','2-4','4-6','6-8','8-10','10-12'])\n",
+    "        ax.set_yticklabels([])\n",
+    "        \n",
+    "        ax.set_xlim([0.,0.6])\n",
+    "        ax.spines['bottom'].set_bounds(0,0.6)\n",
+    "        ax.set_xticks( np.linspace(0,0.6,7))\n",
+    "        minor_locator = AutoMinorLocator(2)\n",
+    "        ax.xaxis.set_minor_locator(minor_locator)\n",
+    "        ax.text(0.0, 1.03, '(e)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "        \n",
+    "    if i == 6:\n",
+    "\n",
+    "        ax.barh(h1, amd_dom03[1], color='#000075', label='3D effetcs')\n",
+    "        ax.barh(h1, amd_dom03[4], color='#4363d8', left=amd_dom03[1], label='Horizontal heterogeneity')\n",
+    "        ax.barh(h1, amd_dom03[7], color='#469990', left=amd_dom03[1]+amd_dom03[4], label='Horizontal heterogeneity and vertical overlap')\n",
+    "        ax.barh(h1, amd_dom03[10], color='#e6194B', left=amd_dom03[1]+amd_dom03[4]+amd_dom03[7], label='Ice-optical parameterization')\n",
+    "        \n",
+    "        ax.set_ylim([-0.5,5.5])\n",
+    "        ax.spines['left'].set_bounds(-0.5,5.5)\n",
+    "        ax.set_yticks([0,1,2,3,4,5])\n",
+    "        ax.set_yticklabels(['0-2','2-4','4-6','6-8','8-10','10-12'])\n",
+    "        ax.set_yticklabels([])\n",
+    "        \n",
+    "        ax.set_xlim([0.,0.6])\n",
+    "        ax.spines['bottom'].set_bounds(0,0.6)\n",
+    "        ax.set_xticks( np.linspace(0,0.6,7))\n",
+    "        minor_locator = AutoMinorLocator(2)\n",
+    "        ax.xaxis.set_minor_locator(minor_locator)\n",
+    "        ax.text(0.0, 1.03, '(h)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 7:\n",
+    "\n",
+    "        ax.barh(h1, amd_dom04[1], color='#000075', label='3D effetcs')\n",
+    "        ax.barh(h1, amd_dom04[4], color='#4363d8', left=amd_dom04[1], label='Horizontal heterogeneity')\n",
+    "        ax.barh(h1, amd_dom04[7], color='#469990', left=amd_dom04[1]+amd_dom04[4], label='Horizontal heterogeneity and vertical overlap')\n",
+    "        ax.barh(h1, amd_dom04[10], color='#e6194B', left=amd_dom04[1]+amd_dom04[4]+amd_dom04[7], label='Ice-optical parameterization')\n",
+    "        \n",
+    "        ax.set_ylim([-0.5,5.5])\n",
+    "        ax.spines['left'].set_bounds(-0.5,5.5)\n",
+    "        ax.set_yticks([0,1,2,3,4,5])\n",
+    "        ax.set_yticklabels(['0-2','2-4','4-6','6-8','8-10','10-12'])\n",
+    "        ax.set_yticklabels([])\n",
+    "        \n",
+    "        ax.set_xlim([0.,0.6])\n",
+    "        ax.spines['bottom'].set_bounds(0,0.6)\n",
+    "        ax.set_xticks( np.linspace(0,0.6,7))\n",
+    "        minor_locator = AutoMinorLocator(2)\n",
+    "        ax.xaxis.set_minor_locator(minor_locator)\n",
+    "        ax.text(0.0, 1.03, '(k)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    ############################################################\n",
+    "    ####### Net ################################################\n",
+    "    ############################################################\n",
+    "    if i == 8:\n",
+    "        \n",
+    "        ax.barh(h1, amd_dom01[2], color='#000075', label='3D effetcs')\n",
+    "        ax.barh(h1, amd_dom01[5], color='#4363d8', left=amd_dom01[2], label='Horizontal heterogeneity')\n",
+    "        ax.barh(h1, amd_dom01[8], color='#469990', left=amd_dom01[2]+amd_dom01[5], label='Horizontal heterogeneity and vertical overlap')\n",
+    "        ax.barh(h1, amd_dom01[11], color='#e6194B', left=amd_dom01[2]+amd_dom01[5]+amd_dom01[8], label='Ice-optical parameterization')\n",
+    "    \n",
+    "        ax.set_ylabel('Height intervals (km)', fontsize=14)\n",
+    "        ax.set_ylim([-0.5,5.5])\n",
+    "        ax.spines['left'].set_bounds(-0.5,5.5)\n",
+    "        ax.set_yticks([0,1,2,3,4,5])\n",
+    "        ax.set_yticklabels(['0-2','2-4','4-6','6-8','8-10','10-12'])\n",
+    "        \n",
+    "        ax.set_xlabel('CRH uncertainty (K day$^{-1}$)', fontsize=14)\n",
+    "        ax.set_xlim([0.0,1])\n",
+    "        ax.spines['bottom'].set_bounds(0,1)\n",
+    "        ax.set_xticks(np.linspace(0,1,6))\n",
+    "        minor_locator = AutoMinorLocator(2)\n",
+    "        ax.xaxis.set_minor_locator(minor_locator)\n",
+    "        \n",
+    "        ax.text(0.0, 1.03, '(c)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 9:\n",
+    "        \n",
+    "        ax.barh(h1, amd_dom02[2], color='#000075', label='3D effetcs')\n",
+    "        ax.barh(h1, amd_dom02[5], color='#4363d8', left=amd_dom02[2], label='Horizontal heterogeneity')\n",
+    "        ax.barh(h1, amd_dom02[8], color='#469990', left=amd_dom02[2]+amd_dom02[5], label='Horizontal heterogeneity and vertical overlap')\n",
+    "        ax.barh(h1, amd_dom02[11], color='#e6194B', left=amd_dom02[2]+amd_dom02[5]+amd_dom02[8], label='Ice-optical parameterization')\n",
+    "        \n",
+    "        ax.set_ylim([-0.5,5.5])\n",
+    "        ax.spines['left'].set_bounds(-0.5,5.5)\n",
+    "        ax.set_yticks([0,1,2,3,4,5])\n",
+    "        ax.set_yticklabels(['0-2','2-4','4-6','6-8','8-10','10-12'])\n",
+    "        ax.set_yticklabels([])\n",
+    "        \n",
+    "        ax.set_xlabel('CRH uncertainty (K day$^{-1}$)', fontsize=14)\n",
+    "        ax.set_xlim([0.,0.6])\n",
+    "        ax.spines['bottom'].set_bounds(0,0.6)\n",
+    "        ax.set_xticks( np.linspace(0,0.6,7))\n",
+    "        minor_locator = AutoMinorLocator(2)\n",
+    "        ax.xaxis.set_minor_locator(minor_locator)\n",
+    "        \n",
+    "        ax.text(0.0, 1.03, '(f)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 10:\n",
+    "        \n",
+    "        ax.barh(h1, amd_dom03[2], color='#000075', label='3D effetcs')\n",
+    "        ax.barh(h1, amd_dom03[5], color='#4363d8', left=amd_dom03[2], label='Horizontal heterogeneity')\n",
+    "        ax.barh(h1, amd_dom03[8], color='#469990', left=amd_dom03[2]+amd_dom03[5], label='Horizontal heterogeneity and vertical overlap')\n",
+    "        ax.barh(h1, amd_dom03[11], color='#e6194B', left=amd_dom03[2]+amd_dom03[5]+amd_dom03[8], label='Ice-optical parameterization')\n",
+    "    \n",
+    "        \n",
+    "        ax.set_ylim([-0.5,5.5])\n",
+    "        ax.spines['left'].set_bounds(-0.5,5.5)\n",
+    "        ax.set_yticks([0,1,2,3,4,5])\n",
+    "        ax.set_yticklabels(['0-2','2-4','4-6','6-8','8-10','10-12'])\n",
+    "        ax.set_yticklabels([])\n",
+    "        \n",
+    "        ax.set_xlabel('CRH uncertainty (K day$^{-1}$)', fontsize=14)\n",
+    "        ax.set_xlim([0.,0.6])\n",
+    "        ax.spines['bottom'].set_bounds(0,0.6)\n",
+    "        ax.set_xticks( np.linspace(0,0.6,7))\n",
+    "        minor_locator = AutoMinorLocator(2)\n",
+    "        ax.xaxis.set_minor_locator(minor_locator)\n",
+    "        \n",
+    "        ax.text(0.0, 1.03, '(i)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 11:\n",
+    "        \n",
+    "        ax.barh(h1, amd_dom04[2], color='#000075', label='3D effetcs')\n",
+    "        ax.barh(h1, amd_dom04[5], color='#4363d8', left=amd_dom04[2], label='Horizontal heterogeneity')\n",
+    "        ax.barh(h1, amd_dom04[8], color='#469990', left=amd_dom04[2]+amd_dom04[5], label='Horizontal heterogeneity and vertical overlap')\n",
+    "        ax.barh(h1, amd_dom04[11], color='#e6194B', left=amd_dom04[2]+amd_dom04[5]+amd_dom04[8], label='Ice-optical parameterization')\n",
+    "        \n",
+    "        ax.set_ylim([-0.5,5.5])\n",
+    "        ax.spines['left'].set_bounds(-0.5,5.5)\n",
+    "        ax.set_yticks([0,1,2,3,4,5])\n",
+    "        ax.set_yticklabels(['0-2','2-4','4-6','6-8','8-10','10-12'])\n",
+    "        ax.set_yticklabels([])\n",
+    "        \n",
+    "        ax.set_xlabel('CRH uncertainty (K day$^{-1}$)', fontsize=14)\n",
+    "        ax.set_xlim([0.,0.6])\n",
+    "        ax.spines['bottom'].set_bounds(0,0.6)\n",
+    "        ax.set_xticks( np.linspace(0,0.6,7))\n",
+    "        minor_locator = AutoMinorLocator(2)\n",
+    "        ax.xaxis.set_minor_locator(minor_locator)\n",
+    "        \n",
+    "        ax.text(0.0, 1.03, '(l)', transform=ax.transAxes, \n",
+    "            size=14)    \n",
+    "     \n",
+    "    i = i + 1     \n",
+    "\n",
+    "fig.subplots_adjust(wspace=0.12,hspace=0.55)\n",
+    "\n",
+    "plt.text(.5, 0.91, 'Longwave', transform=fig.transFigure, horizontalalignment='center',fontsize=15)\n",
+    "plt.text(.5, 0.63, 'Shortwave', transform=fig.transFigure, horizontalalignment='center',fontsize=15)\n",
+    "plt.text(.5, 0.34, 'Net', transform=fig.transFigure, horizontalalignment='center',fontsize=15)\n",
+    "\n",
+    "plt.savefig('figure10.pdf', bbox_inches = 'tight')\n",
+    "plt.savefig('figure10.png', bbox_inches = 'tight',dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "6e84b629-3d7d-4cd5-b760-ee38bd17d646",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x216 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# height intervals\n",
+    "h1 = np.arange(0,6,1)\n",
+    "\n",
+    "fig, axes = plt.subplots(nrows=1, ncols=4, figsize=(20, 3)) \n",
+    "i = 0\n",
+    "\n",
+    "for ax in axes.flat:\n",
+    "    ax.tick_params(labelsize=14)\n",
+    "    ax.spines['top'].set_color('none')\n",
+    "    ax.spines['right'].set_color('none')\n",
+    "        \n",
+    "    ############################################################\n",
+    "    ####### Net###### ##########################################\n",
+    "    ############################################################\n",
+    "    \n",
+    "    if i == 0:\n",
+    "\n",
+    "        ax.barh(h1, d1_lunc_ho_nt, color='#4363d8', label='Horizontal heterogeneity')\n",
+    "        ax.barh(h1, d1_lunc_hl_nt, color='#469990', left=d1_lunc_ho_nt, label='Horizontal heterogeneity and vertical overlap')\n",
+    "        #ax.barh(h1, d1_lunc_ic_nt, color='#e6194B', left=d1_lunc_3d_nt+d1_lunc_ho_nt+d1_lunc_hl_nt, label='Ice optical properties')\n",
+    "        \n",
+    "        ax.barh(h1, d1_unc_ho_nt, edgecolor='k', label='Horizontal heterogeneity',fill = False, hatch=\"//\",linewidth=1,height=0.75)\n",
+    "        ax.barh(h1, d1_unc_hl_nt, edgecolor='k', left=d1_lunc_ho_nt, label='Horizontal heterogeneity and vertical overlap',fill = False, hatch=\"//\",linewidth=1,height=0.75)\n",
+    "        \n",
+    "        #lg=colorlegend.color_legend(ax,loc=1,fsize=11)\n",
+    "        #ax.set_title('Shallow cumulus', fontsize=15,pad=15)\n",
+    "        ax.set_xlabel('Net CRH uncertainty (K day$^{-1}$)', fontsize=14)\n",
+    "        ax.set_ylabel('Height intervals (km)', fontsize=14)\n",
+    "        ax.set_title('Shallow cumulus', fontsize=15)\n",
+    "\n",
+    "        ax.set_ylim([-0.5,5.5])\n",
+    "        ax.spines['left'].set_bounds(-0.5,5.5)\n",
+    "        ax.set_yticks([0,1,2,3,4,5])\n",
+    "        ax.set_yticklabels(['0-2','2-4','4-6','6-8','8-10','10-12'])\n",
+    "        \n",
+    "        ax.set_xlim([0.,10])\n",
+    "        ax.spines['bottom'].set_bounds(0,10)\n",
+    "        ax.set_xticks(np.linspace(0,10,6))\n",
+    "        minor_locator = AutoMinorLocator(2)\n",
+    "        ax.xaxis.set_minor_locator(minor_locator)\n",
+    "        \n",
+    "        ax.text(0.0, 1.03, '(a)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "\n",
+    "        ax.text(0.4, 4.5, 'Cloud horizontal heterogeneity,\\nwithout overlap assumption', color='#4363d8', size=12, va=\"top\")\n",
+    "        ax.text(0.4, 3.2, 'Cloud horizontal heterogeneity,\\nwith overlap assumption', color='#469990', size=12, va=\"top\")\n",
+    "        ax.text(0.4, 1.9, 'Ice-optical parameterization', color='#e6194B', size=12, va=\"top\")\n",
+    "        \n",
+    "\n",
+    "        #ax.text(0.3, 4.5, 'Unresoved cloud horizontal heterogeneity\\nand vertical overlap', color='#4363d8', size=12, va=\"top\")\n",
+    "        #ax.text(0.3, 3.2, 'Unresoved cloud horizontal heterogeneity', color='#469990', size=12, va=\"top\")\n",
+    "        #ax.text(0.3, 2.5, 'Ice-optical parameterization', color='#e6194B', size=12, va=\"top\")\n",
+    "    \n",
+    "    if i == 1:\n",
+    "\n",
+    "        ax.barh(h1, d2_lunc_ho_nt, color='#4363d8', label='Horizontal heterogeneity')\n",
+    "        ax.barh(h1, d2_lunc_hl_nt, color='#469990', left=d2_lunc_ho_nt, label='Horizontal heterogeneity and vertical overlap')\n",
+    "        ax.barh(h1, d2_lunc_ic_nt, color='#e6194B', left=d2_lunc_ho_nt+d2_lunc_hl_nt, label='Ice optical properties')\n",
+    "        \n",
+    "        ax.barh(h1, d2_unc_ho_nt, edgecolor='k', label='Horizontal heterogeneity',fill = False, hatch=\"//\",linewidth=1,height=0.75)\n",
+    "        ax.barh(h1, d2_unc_hl_nt, edgecolor='k', left=d2_lunc_ho_nt, label='Horizontal heterogeneity and vertical overlap',fill = False, hatch=\"//\",linewidth=1,height=0.75)\n",
+    "        ax.barh(h1, d2_unc_ic_nt, edgecolor='k', left=d2_lunc_ho_nt+d2_lunc_hl_nt, label='Ice optical properties',fill = False, hatch=\"//\",linewidth=1,height=0.75)\n",
+    "        \n",
+    "        #lg=colorlegend.color_legend(ax,loc=1,fsize=11)\n",
+    "        #ax.set_title('WCB ascent', fontsize=15,pad=15)\n",
+    "        ax.set_xlabel('Net CRH uncertainty (K day$^{-1}$)', fontsize=14)\n",
+    "        #ax.set_ylabel('Height intervals (km)', fontsize=14)\n",
+    "        ax.set_title('WCB ascent', fontsize=15)\n",
+    "\n",
+    "        ax.set_ylim([-0.5,5.5])\n",
+    "        ax.spines['left'].set_bounds(-0.5,5.5)\n",
+    "        ax.set_yticks([0,1,2,3,4,5])\n",
+    "        ax.set_yticklabels(['0-2','2-4','4-6','6-8','8-10','10-12'])\n",
+    "        ax.set_yticklabels([])\n",
+    "        \n",
+    "        ax.set_xlim([0.,5])\n",
+    "        ax.spines['bottom'].set_bounds(0,5)\n",
+    "        ax.set_xticks(np.linspace(0,5,6))\n",
+    "        minor_locator = AutoMinorLocator(2)\n",
+    "        ax.xaxis.set_minor_locator(minor_locator)\n",
+    "        \n",
+    "        ax.text(0.0, 1.03, '(b)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "        \n",
+    "    if i == 2:\n",
+    "\n",
+    "\n",
+    "        ax.barh(h1, d3_lunc_ho_nt, color='#4363d8', label='Horizontal heterogeneity')\n",
+    "        ax.barh(h1, d3_lunc_hl_nt, color='#469990', left=d3_lunc_ho_nt, label='Horizontal heterogeneity and vertical overlap')\n",
+    "        ax.barh(h1, d3_lunc_ic_nt, color='#e6194B', left=d3_lunc_ho_nt+d3_lunc_hl_nt, label='Ice optical properties')\n",
+    "        \n",
+    "        ax.barh(h1, d3_unc_ho_nt, edgecolor='k', label='Horizontal heterogeneity',fill = False, hatch=\"//\",linewidth=1,height=0.75)\n",
+    "        ax.barh(h1, d3_unc_hl_nt, edgecolor='k', left=d3_lunc_ho_nt, label='Horizontal heterogeneity and vertical overlap',fill = False, hatch=\"//\",linewidth=1,height=0.75)\n",
+    "        ax.barh(h1, d3_unc_ic_nt, edgecolor='k', left=d3_lunc_ho_nt+d3_lunc_hl_nt, label='Ice optical properties',fill = False, hatch=\"//\",linewidth=1,height=0.75)\n",
+    "        \n",
+    "        #lg=colorlegend.color_legend(ax,loc=1,fsize=11)\n",
+    "        #ax.set_title('WCB cyclonic outflow', fontsize=15,pad=15)\n",
+    "        ax.set_xlabel('Net CRH uncertainty (K day$^{-1}$)', fontsize=14)\n",
+    "        #ax.set_ylabel('Height intervals (km)', fontsize=14)\n",
+    "        ax.set_title('WCB cyclonic outflow', fontsize=15)\n",
+    "\n",
+    "        ax.set_ylim([-0.5,5.5])\n",
+    "        ax.spines['left'].set_bounds(-0.5,5.5)\n",
+    "        ax.set_yticks([0,1,2,3,4,5])\n",
+    "        ax.set_yticklabels(['0-2','2-4','4-6','6-8','8-10','10-12'])\n",
+    "        ax.set_yticklabels([])\n",
+    "        \n",
+    "        \n",
+    "        ax.text(0.0, 1.03, '(c)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "        ax.set_xlim([0.,4])\n",
+    "        ax.spines['bottom'].set_bounds(0,4)\n",
+    "        ax.set_xticks(np.linspace(0,4,6))\n",
+    "        minor_locator = AutoMinorLocator(2)\n",
+    "        ax.xaxis.set_minor_locator(minor_locator) \n",
+    "       \n",
+    "    if i == 3:\n",
+    "        \n",
+    "        ax.barh(h1, d4_lunc_ho_nt, color='#4363d8', label='Horizontal heterogeneity')\n",
+    "        ax.barh(h1, d4_lunc_hl_nt, color='#469990', left=d4_lunc_ho_nt, label='Horizontal heterogeneity and vertical overlap')\n",
+    "        ax.barh(h1, d4_lunc_ic_nt, color='#e6194B', left=d4_lunc_ho_nt+d4_lunc_hl_nt, label='Ice optical properties')\n",
+    "        \n",
+    "        ax.barh(h1, d4_unc_ho_nt, edgecolor='k', label='Horizontal heterogeneity',fill = False, hatch=\"//\",linewidth=1,height=0.75)\n",
+    "        ax.barh(h1, d4_unc_hl_nt, edgecolor='k', left=d4_lunc_ho_nt, label='Horizontal heterogeneity and vertical overlap',fill = False, hatch=\"//\",linewidth=1,height=0.75)\n",
+    "        ax.barh(h1, d4_unc_ic_nt, edgecolor='k', left=d4_lunc_ho_nt+d4_lunc_hl_nt, label='Ice optical properties',fill = False, hatch=\"//\",linewidth=1,height=0.75)\n",
+    "        \n",
+    "        #lg=colorlegend.color_legend(ax,loc=1,fsize=11)\n",
+    "        #ax.set_title('WCB anticyclonic outflow', fontsize=15,pad=15)\n",
+    "        ax.set_xlabel('Net CRH uncertainty (K day$^{-1}$)', fontsize=14)\n",
+    "        #ax.set_ylabel('Height intervals (km)', fontsize=14)\n",
+    "        ax.set_title('WCB anticyclonic outflow', fontsize=15)\n",
+    "\n",
+    "        ax.set_ylim([-0.5,5.5])\n",
+    "        ax.spines['left'].set_bounds(-0.5,5.5)\n",
+    "        ax.set_yticks([0,1,2,3,4,5])\n",
+    "        ax.set_yticklabels(['0-2','2-4','4-6','6-8','8-10','10-12'])\n",
+    "        ax.set_yticklabels([])\n",
+    "        \n",
+    "        ax.set_xlim([0.,2])\n",
+    "        ax.spines['bottom'].set_bounds(0,2)\n",
+    "        ax.set_xticks(np.linspace(0,2,6))\n",
+    "        minor_locator = AutoMinorLocator(2)\n",
+    "        ax.xaxis.set_minor_locator(minor_locator)\n",
+    "        \n",
+    "\n",
+    "        ax.text(0.0, 1.03, '(d)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    i = i + 1     \n",
+    "\n",
+    "fig.subplots_adjust(wspace=0.12,hspace=0.55)\n",
+    "\n",
+    "#plt.text(.5, 0.91, 'Longwave', transform=fig.transFigure, horizontalalignment='center',fontsize=15)\n",
+    "#plt.text(.5, 0.63, 'Shortwave', transform=fig.transFigure, horizontalalignment='center',fontsize=15)\n",
+    "#plt.text(.5, 0.34, 'Total', transform=fig.transFigure, horizontalalignment='center',fontsize=15)\n",
+    "\n",
+    "#plt.text((crh_unc_1[0]+crh_unc_2[0])/2,0,\"{:.1f}\".format(per2[0]))\n",
+    "#plt.text((crh_unc_1[0]+crh_unc_2[0]+crh_unc_3[0])/2,0,\"{:.1f}\".format(per3[0]))\n",
+    "\n",
+    "plt.savefig('figure11.pdf', bbox_inches = 'tight')\n",
+    "plt.savefig('figure11.png', bbox_inches = 'tight',dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "5e8e380c-350c-4309-8648-67994593d501",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 12 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# height intervals\n",
+    "h1 = np.arange(0,6,1)\n",
+    "\n",
+    "fig, axes = plt.subplots(nrows=3, ncols=4, figsize=(20, 10)) \n",
+    "i = 0\n",
+    "\n",
+    "for ax in axes.flat:\n",
+    "    ax.tick_params(labelsize=14)\n",
+    "    ax.spines['top'].set_color('none')\n",
+    "    ax.spines['right'].set_color('none')\n",
+    "    \n",
+    "    ############################################################\n",
+    "    ####### Longwave ###########################################\n",
+    "    ############################################################\n",
+    "    if i == 0:\n",
+    "\n",
+    "        ax.barh(h1, d1_unc_3d_lw, color='#000075', label='3D effetcs')\n",
+    "        ax.barh(h1, d1_unc_ho_lw, color='#4363d8', left=d1_unc_3d_lw, label='Horizontal heterogeneity')\n",
+    "        ax.barh(h1, d1_unc_hl_lw, color='#469990', left=d1_unc_3d_lw+d1_unc_ho_lw, label='Horizontal heterogeneity and vertical overlap')\n",
+    "        ax.barh(h1, d1_unc_ic_lw, color='#e6194B', left=d1_unc_3d_lw+d1_unc_ho_lw+d1_unc_hl_lw, label='Ice-optical parameterization')\n",
+    "        #lg=colorlegend.color_legend(ax,loc=1,fsize=11)\n",
+    "        #ax.legend(frameon=False)\n",
+    "        ax.set_title('Shallow cumulus', fontsize=15,pad=35)\n",
+    "        #ax.set_xlabel('CRH uncertainty (K day$^{-1}$)', fontsize=14)\n",
+    "        ax.set_ylabel('Height intervals (km)', fontsize=14)\n",
+    "\n",
+    "        ax.set_ylim([-0.5,5.5])\n",
+    "        ax.spines['left'].set_bounds(-0.5,5.5)\n",
+    "        ax.set_yticks([0,1,2,3,4,5])\n",
+    "        ax.set_yticklabels(['0-2','2-4','4-6','6-8','8-10','10-12'])\n",
+    "        \n",
+    "        #ax.set_xlim([0.,1.5])\n",
+    "        #ax.spines['bottom'].set_bounds(0,1.5)\n",
+    "        #ax.set_xticks(np.linspace(0,1.5,6))\n",
+    "        minor_locator = AutoMinorLocator(2)\n",
+    "        ax.xaxis.set_minor_locator(minor_locator)\n",
+    "        \n",
+    "        ax.text(0.0, 1.03, '(a)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "        \n",
+    "        #ax.text(0.05, 5.2, '3D effects', color = '#000075', size=12, va=\"top\")\n",
+    "        #ax.text(0.05, 4.5, 'Unresoved cloud horizontal heterogeneity\\nand vertical overlap', color='#4363d8', size=12, va=\"top\")\n",
+    "        #ax.text(0.05, 3.2, 'Unresoved cloud horizontal heterogeneity', color='#469990', size=12, va=\"top\")\n",
+    "        #ax.text(0.05, 2.5, 'Ice-optical parameterization', color='#e6194B', size=12, va=\"top\")\n",
+    "        \n",
+    "        ax.text(0.05, 5.2, '3D effects', color = '#000075', size=12, va=\"top\")\n",
+    "        ax.text(0.05, 4.5, 'Cloud horizontal heterogeneity,\\nwithout overlap assumption', color='#4363d8', size=12, va=\"top\")\n",
+    "        ax.text(0.05, 3.2, 'Cloud horizontal heterogeneity,\\nwith overlap assumption', color='#469990', size=12, va=\"top\")\n",
+    "        ax.text(0.05, 1.9, 'Ice-optical parameterization', color='#e6194B', size=12, va=\"top\")\n",
+    "        \n",
+    "    if i == 1:\n",
+    "\n",
+    "        ax.barh(h1, d2_unc_3d_lw, color='#000075', label='3D effetcs')\n",
+    "        ax.barh(h1, d2_unc_ho_lw, color='#4363d8', left=d2_unc_3d_lw, label='Horizontal heterogeneity')\n",
+    "        ax.barh(h1, d2_unc_hl_lw, color='#469990', left=d2_unc_3d_lw+d2_unc_ho_lw, label='Horizontal heterogeneity and vertical overlap')\n",
+    "        ax.barh(h1, d2_unc_ic_lw, color='#e6194B', left=d2_unc_3d_lw+d2_unc_ho_lw+d2_unc_hl_lw, label='Ice optical properties')\n",
+    "        #lg=colorlegend.color_legend(ax,loc=1,fsize=11)\n",
+    "        ax.set_title('WCB ascent', fontsize=15,pad=35)\n",
+    "        #ax.set_xlabel('CRH uncertainty (K day$^{-1}$)', fontsize=14)\n",
+    "        #ax.set_ylabel('Height intervals (km)', fontsize=14)\n",
+    "\n",
+    "        ax.set_ylim([-0.5,5.5])\n",
+    "        ax.spines['left'].set_bounds(-0.5,5.5)\n",
+    "        ax.set_yticks([0,1,2,3,4,5])\n",
+    "        ax.set_yticklabels(['0-2','2-4','4-6','6-8','8-10','10-12'])\n",
+    "        ax.set_yticklabels([])\n",
+    "        \n",
+    "        #ax.set_xlim([0.,0.6])\n",
+    "        #ax.spines['bottom'].set_bounds(0,0.6)\n",
+    "        #ax.set_xticks( np.linspace(0,0.6,7))\n",
+    "        minor_locator = AutoMinorLocator(2)\n",
+    "        ax.xaxis.set_minor_locator(minor_locator)\n",
+    "        \n",
+    "        ax.text(0.0, 1.03, '(d)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 2:\n",
+    "\n",
+    "        ax.barh(h1, d3_unc_3d_lw, color='#000075', label='3D effetcs')\n",
+    "        ax.barh(h1, d3_unc_ho_lw, color='#4363d8', left=d3_unc_3d_lw, label='Horizontal heterogeneity')\n",
+    "        ax.barh(h1, d3_unc_hl_lw, color='#469990', left=d3_unc_3d_lw+d3_unc_ho_lw, label='Horizontal heterogeneity and vertical overlap')\n",
+    "        ax.barh(h1, d3_unc_ic_lw, color='#e6194B', left=d3_unc_3d_lw+d3_unc_ho_lw+d3_unc_hl_lw, label='Ice optical properties')\n",
+    "        #lg=colorlegend.color_legend(ax,loc=1,fsize=11)\n",
+    "        ax.set_title('WCB cyclonic outflow', fontsize=15,pad=35)\n",
+    "        #ax.set_xlabel('CRH uncertainty (K day$^{-1}$)', fontsize=14)\n",
+    "        #ax.set_ylabel('Height intervals (km)', fontsize=14)\n",
+    "\n",
+    "        ax.set_ylim([-0.5,5.5])\n",
+    "        ax.spines['left'].set_bounds(-0.5,5.5)\n",
+    "        ax.set_yticks([0,1,2,3,4,5])\n",
+    "        ax.set_yticklabels(['0-2','2-4','4-6','6-8','8-10','10-12'])\n",
+    "        ax.set_yticklabels([])\n",
+    "        \n",
+    "        #ax.set_xlim([0.,0.6])\n",
+    "        #ax.spines['bottom'].set_bounds(0,0.6)\n",
+    "        #ax.set_xticks( np.linspace(0,0.6,7))\n",
+    "        minor_locator = AutoMinorLocator(2)\n",
+    "        ax.xaxis.set_minor_locator(minor_locator)\n",
+    "        \n",
+    "        ax.text(0.0, 1.03, '(g)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    \n",
+    "    if i == 3:\n",
+    "\n",
+    "        ax.barh(h1, d4_unc_3d_lw, color='#000075', label='3D effetcs')\n",
+    "        ax.barh(h1, d4_unc_ho_lw, color='#4363d8', left=d4_unc_3d_lw, label='Horizontal heterogeneity')\n",
+    "        ax.barh(h1, d4_unc_hl_lw, color='#469990', left=d4_unc_3d_lw+d4_unc_ho_lw, label='Horizontal heterogeneity and vertical overlap')\n",
+    "        ax.barh(h1, d4_unc_ic_lw, color='#e6194B', left=d4_unc_3d_lw+d4_unc_ho_lw+d4_unc_hl_lw, label='Ice optical properties')\n",
+    "        #lg=colorlegend.color_legend(ax,loc=1,fsize=11)\n",
+    "        ax.set_title('WCB anticyclonic outflow', fontsize=15,pad=35)\n",
+    "        #ax.set_xlabel('CRH uncertainty (K day$^{-1}$)', fontsize=14)\n",
+    "        #ax.set_ylabel('Height intervals (km)', fontsize=14)\n",
+    "\n",
+    "        ax.set_ylim([-0.5,5.5])\n",
+    "        ax.spines['left'].set_bounds(-0.5,5.5)\n",
+    "        ax.set_yticks([0,1,2,3,4,5])\n",
+    "        ax.set_yticklabels(['0-2','2-4','4-6','6-8','8-10','10-12'])\n",
+    "        ax.set_yticklabels([])\n",
+    "        \n",
+    "        #ax.set_xlim([0.,0.6])\n",
+    "        #ax.spines['bottom'].set_bounds(0,0.6)\n",
+    "        #ax.set_xticks( np.linspace(0,0.6,7))\n",
+    "        minor_locator = AutoMinorLocator(2)\n",
+    "        ax.xaxis.set_minor_locator(minor_locator)\n",
+    "        \n",
+    "        ax.text(0.0, 1.03, '(j)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    ############################################################\n",
+    "    ####### Shortwave ##########################################\n",
+    "    ############################################################\n",
+    "    \n",
+    "    if i == 4:\n",
+    "\n",
+    "        ax.barh(h1, d1_unc_3d_sw, color='#000075', label='3D effetcs')\n",
+    "        ax.barh(h1, d1_unc_ho_sw, color='#4363d8', left=d1_unc_3d_sw, label='Horizontal heterogeneity')\n",
+    "        ax.barh(h1, d1_unc_hl_sw, color='#469990', left=d1_unc_3d_sw+d1_unc_ho_sw, label='Horizontal heterogeneity and vertical overlap')\n",
+    "        #ax.barh(h1, d1_unc_ic_sw, color='#e6194B', left=d1_unc_3d_sw+d1_unc_ho_sw+d1_unc_hl_sw, label='Ice optical properties')\n",
+    "        #lg=colorlegend.color_legend(ax,loc=1,fsize=11)\n",
+    "        #ax.set_title('Shallow cumulus', fontsize=15,pad=15)\n",
+    "        #ax.set_xlabel('CRH uncertainty (K day$^{-1}$)', fontsize=14)\n",
+    "        ax.set_ylabel('Height intervals (km)', fontsize=14)\n",
+    "\n",
+    "        ax.set_ylim([-0.5,5.5])\n",
+    "        ax.spines['left'].set_bounds(-0.5,5.5)\n",
+    "        ax.set_yticks([0,1,2,3,4,5])\n",
+    "        ax.set_yticklabels(['0-2','2-4','4-6','6-8','8-10','10-12'])\n",
+    "        \n",
+    "        #ax.set_xlim([0.0,0.6])\n",
+    "        #ax.spines['bottom'].set_bounds(0,0.6)\n",
+    "        #ax.set_xticks(np.linspace(0,0.6,7))\n",
+    "        minor_locator = AutoMinorLocator(2)\n",
+    "        ax.xaxis.set_minor_locator(minor_locator)\n",
+    "        \n",
+    "        ax.text(0.0, 1.03, '(b)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 5:\n",
+    "\n",
+    "        ax.barh(h1, d2_unc_3d_sw, color='#000075', label='3D effetcs')\n",
+    "        ax.barh(h1, d2_unc_ho_sw, color='#4363d8', left=d2_unc_3d_sw, label='Horizontal heterogeneity')\n",
+    "        ax.barh(h1, d2_unc_hl_sw, color='#469990', left=d2_unc_3d_sw+d2_unc_ho_sw, label='Horizontal heterogeneity and vertical overlap')\n",
+    "        ax.barh(h1, d2_unc_ic_sw, color='#e6194B', left=d2_unc_3d_sw+d2_unc_ho_sw+d2_unc_hl_sw, label='Ice optical properties')\n",
+    "        #lg=colorlegend.color_legend(ax,loc=1,fsize=11)\n",
+    "        #ax.set_title('WCB ascent', fontsize=15,pad=15)\n",
+    "        #ax.set_xlabel('CRH uncertainty (K day$^{-1}$)', fontsize=14)\n",
+    "        #ax.set_ylabel('Height intervals (km)', fontsize=14)\n",
+    "\n",
+    "        ax.set_ylim([-0.5,5.5])\n",
+    "        ax.spines['left'].set_bounds(-0.5,5.5)\n",
+    "        ax.set_yticks([0,1,2,3,4,5])\n",
+    "        ax.set_yticklabels(['0-2','2-4','4-6','6-8','8-10','10-12'])\n",
+    "        ax.set_yticklabels([])\n",
+    "        \n",
+    "        #ax.set_xlim([0.0,0.6])\n",
+    "        #ax.spines['bottom'].set_bounds(0,0.6)\n",
+    "        #ax.set_xticks(np.linspace(0,0.6,7))\n",
+    "        minor_locator = AutoMinorLocator(2)\n",
+    "        ax.xaxis.set_minor_locator(minor_locator)\n",
+    "        \n",
+    "        ax.text(0.0, 1.03, '(e)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 6:\n",
+    "\n",
+    "        ax.barh(h1, d3_unc_3d_sw, color='#000075', label='3D effetcs')\n",
+    "        ax.barh(h1, d3_unc_ho_sw, color='#4363d8', left=d3_unc_3d_sw, label='Horizontal heterogeneity')\n",
+    "        ax.barh(h1, d3_unc_hl_sw, color='#469990', left=d3_unc_3d_sw+d3_unc_ho_sw, label='Horizontal heterogeneity and vertical overlap')\n",
+    "        ax.barh(h1, d3_unc_ic_sw, color='#e6194B', left=d3_unc_3d_sw+d3_unc_ho_sw+d3_unc_hl_sw, label='Ice optical properties')\n",
+    "        #lg=colorlegend.color_legend(ax,loc=1,fsize=11)\n",
+    "        #ax.set_title('WCB cyclonic outflow', fontsize=15,pad=15)\n",
+    "        #ax.set_xlabel('CRH uncertainty (K day$^{-1}$)', fontsize=14)\n",
+    "        #ax.set_ylabel('Height intervals (km)', fontsize=14)\n",
+    "\n",
+    "        ax.set_ylim([-0.5,5.5])\n",
+    "        ax.spines['left'].set_bounds(-0.5,5.5)\n",
+    "        ax.set_yticks([0,1,2,3,4,5])\n",
+    "        ax.set_yticklabels(['0-2','2-4','4-6','6-8','8-10','10-12'])\n",
+    "        ax.set_yticklabels([])\n",
+    "        \n",
+    "        #ax.set_xlim([0.0,0.6])\n",
+    "        #ax.spines['bottom'].set_bounds(0,0.6)\n",
+    "        #ax.set_xticks(np.linspace(0,0.6,7))\n",
+    "        minor_locator = AutoMinorLocator(2)\n",
+    "        ax.xaxis.set_minor_locator(minor_locator)\n",
+    "        \n",
+    "        ax.text(0.0, 1.03, '(h)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    \n",
+    "    if i == 7:\n",
+    "\n",
+    "        ax.barh(h1, d4_unc_3d_sw, color='#000075', label='3D effetcs')\n",
+    "        ax.barh(h1, d4_unc_ho_sw, color='#4363d8', left=d4_unc_3d_sw, label='Horizontal heterogeneity')\n",
+    "        ax.barh(h1, d4_unc_hl_sw, color='#469990', left=d4_unc_3d_sw+d4_unc_ho_sw, label='Horizontal heterogeneity and vertical overlap')\n",
+    "        ax.barh(h1, d4_unc_ic_sw, color='#e6194B', left=d4_unc_3d_sw+d4_unc_ho_sw+d4_unc_hl_sw, label='Ice-optical pa')\n",
+    "        #lg=colorlegend.color_legend(ax,loc=1,fsize=11)\n",
+    "        #ax.set_title('WCB anticyclonic outflow', fontsize=15,pad=15)\n",
+    "        #ax.set_xlabel('CRH uncertainty (K day$^{-1}$)', fontsize=14)\n",
+    "        #ax.set_ylabel('Height intervals (km)', fontsize=14)\n",
+    "\n",
+    "        ax.set_ylim([-0.5,5.5])\n",
+    "        ax.spines['left'].set_bounds(-0.5,5.5)\n",
+    "        ax.set_yticks([0,1,2,3,4,5])\n",
+    "        ax.set_yticklabels(['0-2','2-4','4-6','6-8','8-10','10-12'])\n",
+    "        ax.set_yticklabels([])\n",
+    "        \n",
+    "        #ax.set_xlim([0.0,0.6])\n",
+    "        #ax.spines['bottom'].set_bounds(0,0.6)\n",
+    "        #ax.set_xticks(np.linspace(0,0.6,7))\n",
+    "        minor_locator = AutoMinorLocator(2)\n",
+    "        ax.xaxis.set_minor_locator(minor_locator)\n",
+    "        \n",
+    "        ax.text(0.0, 1.03, '(k)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    ############################################################\n",
+    "    ####### Net###### ##########################################\n",
+    "    ############################################################\n",
+    "    \n",
+    "    if i == 8:\n",
+    "\n",
+    "        ax.barh(h1, d1_unc_3d_nt, color='#000075', label='3D effetcs')\n",
+    "        ax.barh(h1, d1_unc_ho_nt, color='#4363d8', left=d1_unc_3d_nt, label='Horizontal heterogeneity')\n",
+    "        ax.barh(h1, d1_unc_hl_nt, color='#469990', left=d1_unc_3d_nt+d1_unc_ho_nt, label='Horizontal heterogeneity and vertical overlap')\n",
+    "        #ax.barh(h1, d1_unc_ic_nt, color='#e6194B', left=d1_unc_3d_nt+d1_unc_ho_nt+d1_unc_hl_nt, label='Ice optical properties')\n",
+    "        #lg=colorlegend.color_legend(ax,loc=1,fsize=11)\n",
+    "        #ax.set_title('Shallow cumulus', fontsize=15,pad=15)\n",
+    "        ax.set_xlabel('CRH uncertainty (K day$^{-1}$)', fontsize=14)\n",
+    "        ax.set_ylabel('Height intervals (km)', fontsize=14)\n",
+    "\n",
+    "        ax.set_ylim([-0.5,5.5])\n",
+    "        ax.spines['left'].set_bounds(-0.5,5.5)\n",
+    "        ax.set_yticks([0,1,2,3,4,5])\n",
+    "        ax.set_yticklabels(['0-2','2-4','4-6','6-8','8-10','10-12'])\n",
+    "        \n",
+    "        #ax.set_xlim([0.,2])\n",
+    "        #ax.spines['bottom'].set_bounds(0,2)\n",
+    "        #ax.set_xticks(np.linspace(0,2,6))\n",
+    "        minor_locator = AutoMinorLocator(2)\n",
+    "        ax.xaxis.set_minor_locator(minor_locator)\n",
+    "        \n",
+    "        ax.text(0.0, 1.03, '(c)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "        #table = ax.table(cellText=d1_df.values, colLabels=None, loc='center',bbox=[0, -1.5, 1, 1],rowLabels=d1_df.index,colColours=ccolors)\n",
+    "        #table.auto_set_font_size(False)\n",
+    "        #table.set_fontsize(13)\n",
+    "        #table.scale(1, 1)\n",
+    "        #cellDict = table.get_celld()\n",
+    "        #cellDict[(0,0)].set_height(0.01);cellDict[(0,1)].set_height(0.01);cellDict[(0,2)].set_height(0.01);cellDict[(0,3)].set_height(0.01)\n",
+    "        \n",
+    "    if i == 9:\n",
+    "\n",
+    "        ax.barh(h1, d2_unc_3d_nt, color='#000075', label='3D effetcs')\n",
+    "        ax.barh(h1, d2_unc_ho_nt, color='#4363d8', left=d2_unc_3d_nt, label='Horizontal heterogeneity')\n",
+    "        ax.barh(h1, d2_unc_hl_nt, color='#469990', left=d2_unc_3d_nt+d2_unc_ho_nt, label='Horizontal heterogeneity and vertical overlap')\n",
+    "        ax.barh(h1, d2_unc_ic_nt, color='#e6194B', left=d2_unc_3d_nt+d2_unc_ho_nt+d2_unc_hl_nt, label='Ice optical properties')\n",
+    "        #lg=colorlegend.color_legend(ax,loc=1,fsize=11)\n",
+    "        #ax.set_title('WCB ascent', fontsize=15,pad=15)\n",
+    "        ax.set_xlabel('CRH uncertainty (K day$^{-1}$)', fontsize=14)\n",
+    "        #ax.set_ylabel('Height intervals (km)', fontsize=14)\n",
+    "\n",
+    "        ax.set_ylim([-0.5,5.5])\n",
+    "        ax.spines['left'].set_bounds(-0.5,5.5)\n",
+    "        ax.set_yticks([0,1,2,3,4,5])\n",
+    "        ax.set_yticklabels(['0-2','2-4','4-6','6-8','8-10','10-12'])\n",
+    "        ax.set_yticklabels([])\n",
+    "        \n",
+    "        #ax.set_xlim([0.0,1])\n",
+    "        #ax.spines['bottom'].set_bounds(0,1)\n",
+    "        #ax.set_xticks(np.linspace(0,1,6))\n",
+    "        minor_locator = AutoMinorLocator(2)\n",
+    "        ax.xaxis.set_minor_locator(minor_locator)\n",
+    "        \n",
+    "        ax.text(0.0, 1.03, '(f)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "        #table = ax.table(cellText=d2_df.values, colLabels=None, loc='center',bbox=[0, -1.5, 1, 1],colColours=ccolors)\n",
+    "        #table.auto_set_font_size(False)\n",
+    "        #table.set_fontsize(13)\n",
+    "        #table.scale(1, 1)\n",
+    "        #cellDict = table.get_celld()\n",
+    "        #cellDict[(0,0)].set_height(0.01);cellDict[(0,1)].set_height(0.01);cellDict[(0,2)].set_height(0.01);cellDict[(0,3)].set_height(0.01)\n",
+    "        \n",
+    "    if i == 10:\n",
+    "\n",
+    "        ax.barh(h1, d3_unc_3d_nt, color='#000075', label='3D effetcs')\n",
+    "        ax.barh(h1, d3_unc_ho_nt, color='#4363d8', left=d3_unc_3d_nt, label='Horizontal heterogeneity')\n",
+    "        ax.barh(h1, d3_unc_hl_nt, color='#469990', left=d3_unc_3d_nt+d3_unc_ho_nt, label='Horizontal heterogeneity and vertical overlap')\n",
+    "        ax.barh(h1, d3_unc_ic_nt, color='#e6194B', left=d3_unc_3d_nt+d3_unc_ho_nt+d3_unc_hl_nt, label='Ice optical properties')\n",
+    "        #lg=colorlegend.color_legend(ax,loc=1,fsize=11)\n",
+    "        #ax.set_title('WCB cyclonic outflow', fontsize=15,pad=15)\n",
+    "        ax.set_xlabel('CRH uncertainty (K day$^{-1}$)', fontsize=14)\n",
+    "        #ax.set_ylabel('Height intervals (km)', fontsize=14)\n",
+    "\n",
+    "        ax.set_ylim([-0.5,5.5])\n",
+    "        ax.spines['left'].set_bounds(-0.5,5.5)\n",
+    "        ax.set_yticks([0,1,2,3,4,5])\n",
+    "        ax.set_yticklabels(['0-2','2-4','4-6','6-8','8-10','10-12'])\n",
+    "        ax.set_yticklabels([])\n",
+    "        \n",
+    "        #ax.set_xlim([0.0,1])\n",
+    "        #ax.spines['bottom'].set_bounds(0,1)\n",
+    "        #ax.set_xticks(np.linspace(0,1,6))\n",
+    "        minor_locator = AutoMinorLocator(2)\n",
+    "        ax.xaxis.set_minor_locator(minor_locator)\n",
+    "        \n",
+    "        ax.text(0.0, 1.03, '(i)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "        #table = ax.table(cellText=d3_df.values, colLabels=None, loc='center',bbox=[0, -1.5, 1, 1],colColours=ccolors)\n",
+    "        #table.auto_set_font_size(False)\n",
+    "        #table.set_fontsize(13)\n",
+    "        #table.scale(1, 1)\n",
+    "        #cellDict = table.get_celld()\n",
+    "        #cellDict[(0,0)].set_height(0.01);cellDict[(0,1)].set_height(0.01);cellDict[(0,2)].set_height(0.01);cellDict[(0,3)].set_height(0.01)\n",
+    "    \n",
+    "    if i == 11:\n",
+    "\n",
+    "        ax.barh(h1, d4_unc_3d_nt, color='#000075', label='3D effetcs')\n",
+    "        ax.barh(h1, d4_unc_ho_nt, color='#4363d8', left=d4_unc_3d_nt, label='Horizontal heterogeneity')\n",
+    "        ax.barh(h1, d4_unc_hl_nt, color='#469990', left=d4_unc_3d_nt+d4_unc_ho_nt, label='Horizontal heterogeneity and vertical overlap')\n",
+    "        ax.barh(h1, d4_unc_ic_nt, color='#e6194B', left=d4_unc_3d_nt+d4_unc_ho_nt+d4_unc_hl_nt, label='Ice optical properties')\n",
+    "        #lg=colorlegend.color_legend(ax,loc=1,fsize=11)\n",
+    "        #ax.set_title('WCB anticyclonic outflow', fontsize=15,pad=15)\n",
+    "        ax.set_xlabel('CRH uncertainty (K day$^{-1}$)', fontsize=14)\n",
+    "        #ax.set_ylabel('Height intervals (km)', fontsize=14)\n",
+    "\n",
+    "        ax.set_ylim([-0.5,5.5])\n",
+    "        ax.spines['left'].set_bounds(-0.5,5.5)\n",
+    "        ax.set_yticks([0,1,2,3,4,5])\n",
+    "        ax.set_yticklabels(['0-2','2-4','4-6','6-8','8-10','10-12'])\n",
+    "        ax.set_yticklabels([])\n",
+    "        \n",
+    "        #ax.set_xlim([0.0,1])\n",
+    "        #ax.spines['bottom'].set_bounds(0,1)\n",
+    "        #ax.set_xticks(np.linspace(0,1,6))\n",
+    "        minor_locator = AutoMinorLocator(2)\n",
+    "        ax.xaxis.set_minor_locator(minor_locator)\n",
+    "\n",
+    "        ax.text(0.0, 1.03, '(l)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "        #table = ax.table(cellText=d4_df.values, colLabels=None, loc='center',bbox=[0, -1.5, 1, 1],colColours=ccolors)\n",
+    "        #table.auto_set_font_size(False)\n",
+    "        #table.set_fontsize(13)\n",
+    "        #table.scale(1, 1)\n",
+    "        #cellDict = table.get_celld()\n",
+    "        #cellDict[(0,0)].set_height(0.01);cellDict[(0,1)].set_height(0.01);cellDict[(0,2)].set_height(0.01);cellDict[(0,3)].set_height(0.01)\n",
+    "\n",
+    "        \n",
+    "    i = i + 1     \n",
+    "\n",
+    "fig.subplots_adjust(wspace=0.12,hspace=0.55)\n",
+    "\n",
+    "plt.text(.5, 0.91, 'Longwave', transform=fig.transFigure, horizontalalignment='center',fontsize=15)\n",
+    "plt.text(.5, 0.63, 'Shortwave', transform=fig.transFigure, horizontalalignment='center',fontsize=15)\n",
+    "plt.text(.5, 0.34, 'Net', transform=fig.transFigure, horizontalalignment='center',fontsize=15)\n",
+    "\n",
+    "#plt.text((crh_unc_1[0]+crh_unc_2[0])/2,0,\"{:.1f}\".format(per2[0]))\n",
+    "#plt.text((crh_unc_1[0]+crh_unc_2[0]+crh_unc_3[0])/2,0,\"{:.1f}\".format(per3[0]))\n",
+    "\n",
+    "#plt.savefig('figure10.pdf', bbox_inches = 'tight')\n",
+    "plt.savefig('figure11_2.png', bbox_inches = 'tight',dpi=100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "3049f12b-2f60-49d8-9703-cb13196cae5f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "client.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7e35dd8e-294a-4d97-98d0-79c3ade4045f",
+   "metadata": {},
+   "source": [
+    "## Latitude-longitude plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "id": "8ea9fef3-f5ff-43b9-9e6a-53c9c8f7c64d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x1656 with 13 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(nrows=4, ncols=3, figsize=(15, 23))\n",
+    "i = 0\n",
+    "\n",
+    "## coordinates for plotting\n",
+    "lat = np.linspace(41,47,198)\n",
+    "lon = np.linspace(-1,5,246)\n",
+    "\n",
+    "for ax in axes.flat:\n",
+    "    ax.tick_params(labelsize=14)\n",
+    "    \n",
+    "    if i == 0:\n",
+    "     \n",
+    "        im = ax.pcolor(lon,lat,ds_lib2[1]['ddt_radlw_cg'][4,:,:,70].T.values,vmin=-30,vmax=30,cmap='seismic')\n",
+    "        ax.set_xticklabels([])\n",
+    "        ax.set_ylabel('Latitude', fontsize=14)\n",
+    "        ax.set_title(' Shortwave CRH', fontsize=14)\n",
+    "        ax.text(0.0, 1.03, '(a) ', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 1:    \n",
+    "\n",
+    "        ax.pcolor(lon,lat,ds_lib2[0]['ddt_radlw_cg'][4,:,:,70].T.values,vmin=-30,vmax=30,cmap='seismic')\n",
+    "        ax.set_xticklabels([])\n",
+    "        ax.set_yticklabels([])\n",
+    "        ax.set_title(' Longwave CRH', fontsize=14)\n",
+    "        ax.text(0.0, 1.03, '(b) ', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 2:\n",
+    "        \n",
+    "        ax.pcolor(lon,lat,(ds_lib2[1]['ddt_radlw_cg']+ds_lib2[0]['ddt_radlw_cg'])[4,:,:,70].T.values,vmin=-30,vmax=30,cmap='seismic')\n",
+    "        ax.set_xticklabels([])\n",
+    "        ax.set_yticklabels([])\n",
+    "        ax.set_title(' Total CRH', fontsize=14)\n",
+    "        ax.text(0.0, 1.03, '(c) ', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 3:\n",
+    "        \n",
+    "        ax.pcolor(lon,lat,ds_lib2[8]['ddt_radlw'][4,:,:,70].T.values,vmin=-30,vmax=30,cmap='seismic')\n",
+    "        ax.set_xticklabels([])\n",
+    "        ax.set_ylabel('Latitude', fontsize=14)\n",
+    "        ax.text(0.0, 1.03, '(d) ', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 4:\n",
+    "        \n",
+    "        ax.pcolor(lon,lat,ds_lib2[6]['ddt_radlw'][4,:,:,70].T.values,vmin=-30,vmax=30,cmap='seismic')\n",
+    "        ax.set_xticklabels([])\n",
+    "        ax.set_yticklabels([])\n",
+    "        ax.text(0.0, 1.03, '(e) ', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 5:\n",
+    "        \n",
+    "        ax.pcolor(lon,lat,(ds_lib2[6]['ddt_radlw']+ds_lib2[8]['ddt_radlw'])[4,:,:,70].T.values,vmin=-30,vmax=30,cmap='seismic')\n",
+    "        ax.set_xticklabels([])\n",
+    "        ax.set_yticklabels([])\n",
+    "        ax.text(0.0, 1.03, '(f) ', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 6:\n",
+    "        \n",
+    "        diffsw = ds_lib2[1]['ddt_radlw_cg'][4,:,:,70].T.values - ds_lib2[8]['ddt_radlw'][4,:,:,70].T.values\n",
+    "        ax.pcolor(lon,lat,diffsw,vmin=-30,vmax=30,cmap='seismic')\n",
+    "        ax.set_xticklabels([])\n",
+    "        ax.set_ylabel('Latitude', fontsize=14)\n",
+    "        ax.text(0.0, 1.03, '(g) ', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 7:\n",
+    "        \n",
+    "        difflw = ds_lib2[0]['ddt_radlw_cg'][4,:,:,70].T.values - ds_lib2[6]['ddt_radlw'][4,:,:,70].T.values\n",
+    "        ax.pcolor(lon,lat,difflw,vmin=-30,vmax=30,cmap='seismic')\n",
+    "        ax.set_xticklabels([])\n",
+    "        ax.set_yticklabels([])\n",
+    "        ax.text(0.0, 1.03, '(h) ', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 8:\n",
+    "        \n",
+    "        diffsw = ds_lib2[1]['ddt_radlw_cg'][4,:,:,70].T.values - ds_lib2[8]['ddt_radlw'][4,:,:,70].T.values\n",
+    "        difflw = ds_lib2[0]['ddt_radlw_cg'][4,:,:,70].T.values - ds_lib2[6]['ddt_radlw'][4,:,:,70].T.values\n",
+    "        diffnt = difflw + diffsw\n",
+    "        ax.pcolor(lon,lat,diffnt,vmin=-30,vmax=30,cmap='seismic')\n",
+    "        ax.set_xticklabels([])\n",
+    "        ax.set_yticklabels([])\n",
+    "        ax.text(0.0, 1.03, '(i) ', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 9:\n",
+    "        \n",
+    "        diffsw = ds_lib2[1]['ddt_radlw_cg'][4,:,:,70].T.values - ds_lib2[9]['ddt_radlw'][4,:,:,70].T.values\n",
+    "        ax.pcolor(lon,lat,diffsw,vmin=-30,vmax=30,cmap='seismic')\n",
+    "        ax.set_ylabel('Latitude', fontsize=14)\n",
+    "        ax.set_xlabel('Longitude', fontsize=14)\n",
+    "        ax.text(0.0, 1.03, '(j) ', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "\n",
+    "    if i == 10:\n",
+    "        \n",
+    "        difflw = ds_lib2[0]['ddt_radlw_cg'][4,:,:,70].T.values - ds_lib2[7]['ddt_radlw'][4,:,:,70].T.values\n",
+    "        ax.pcolor(lon,lat,difflw,vmin=-30,vmax=30,cmap='seismic')\n",
+    "        ax.set_yticklabels([])\n",
+    "        ax.set_xlabel('Longitude', fontsize=14)\n",
+    "        ax.text(0.0, 1.03, '(k) ', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "\n",
+    "    if i == 11:\n",
+    "        \n",
+    "        diffsw = ds_lib2[1]['ddt_radlw_cg'][4,:,:,70].T.values - ds_lib2[9]['ddt_radlw'][4,:,:,70].T.values\n",
+    "        difflw = ds_lib2[0]['ddt_radlw_cg'][4,:,:,70].T.values - ds_lib2[7]['ddt_radlw'][4,:,:,70].T.values\n",
+    "        diffnt = difflw + diffsw\n",
+    "        ax.pcolor(lon,lat,diffnt,vmin=-30,vmax=30,cmap='seismic')\n",
+    "        ax.set_yticklabels([])\n",
+    "        ax.set_xlabel('Longitude', fontsize=14)\n",
+    "        ax.text(0.0, 1.03, '(l) ', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    i = i + 1\n",
+    "    \n",
+    "fig.subplots_adjust(wspace=0.1,hspace=0.2)\n",
+    "\n",
+    "cb_ax = fig.add_axes([0.92, 0.56, 0.015, 0.32]) # xcenter/ycenter/width/height\n",
+    "cbar = fig.colorbar(im,cax=cb_ax,orientation='vertical',shrink=0.95,extend='both')\n",
+    "cbar.set_label(label='K day$^{-1}$', size='13',labelpad=1)\n",
+    "cbar.ax.tick_params(labelsize=13)\n",
+    "\n",
+    "\n",
+    "plt.text(.5, 0.902, 'Coarse-grained LEM CRH', transform=fig.transFigure, horizontalalignment='center',fontsize=15)\n",
+    "plt.text(.5, 0.702, 'NWP CRH, grid-box clouds', transform=fig.transFigure, horizontalalignment='center',fontsize=15)\n",
+    "plt.text(.5, 0.502, 'Coarse-grained LEM CRH - NWP CRH, grid-box homogeneous clouds', transform=fig.transFigure, horizontalalignment='center',fontsize=15)\n",
+    "plt.text(.5, 0.302, 'Coarse-grained LEM CRH - NWP CRH, homogeneous clouds with fractional cloud cover ', transform=fig.transFigure, horizontalalignment='center',fontsize=15)\n",
+    "\n",
+    "#plt.savefig('figure11.pdf', bbox_inches = 'tight')\n",
+    "plt.savefig('sfigure1.png', bbox_inches = 'tight',dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "id": "3f4b915f-cc93-43f8-aa08-76a89b0b2443",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x1656 with 13 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(nrows=4, ncols=3, figsize=(15, 23))\n",
+    "i = 0\n",
+    "\n",
+    "## coordinates for plotting\n",
+    "lat = np.linspace(41,47,198)\n",
+    "lon = np.linspace(-1,5,246)\n",
+    "\n",
+    "for ax in axes.flat:\n",
+    "    ax.tick_params(labelsize=14)\n",
+    "    ax.set_xlim([0,1])\n",
+    "    ax.set_ylim([41.5,42.5])\n",
+    "    \n",
+    "    if i == 0:\n",
+    "     \n",
+    "        im = ax.pcolor(lon,lat,ds_lib2[1]['ddt_radlw_cg'][4,:,:,70].T.values,vmin=-30,vmax=30,cmap='seismic')\n",
+    "        ax.set_xticklabels([])\n",
+    "        ax.set_ylabel('Latitude', fontsize=14)\n",
+    "        ax.set_title(' Shortwave CRH', fontsize=14)\n",
+    "        ax.text(0.0, 1.03, '(a) ', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 1:    \n",
+    "\n",
+    "        ax.pcolor(lon,lat,ds_lib2[0]['ddt_radlw_cg'][4,:,:,70].T.values,vmin=-30,vmax=30,cmap='seismic')\n",
+    "        ax.set_xticklabels([])\n",
+    "        ax.set_yticklabels([])\n",
+    "        ax.set_title(' Longwave CRH', fontsize=14)\n",
+    "        ax.text(0.0, 1.03, '(b) ', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 2:\n",
+    "        \n",
+    "        ax.pcolor(lon,lat,(ds_lib2[1]['ddt_radlw_cg']+ds_lib2[0]['ddt_radlw_cg'])[4,:,:,70].T.values,vmin=-30,vmax=30,cmap='seismic')\n",
+    "        ax.set_xticklabels([])\n",
+    "        ax.set_yticklabels([])\n",
+    "        ax.set_title(' Total CRH', fontsize=14)\n",
+    "        ax.text(0.0, 1.03, '(c) ', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 3:\n",
+    "        \n",
+    "        ax.pcolor(lon,lat,ds_lib2[8]['ddt_radlw'][4,:,:,70].T.values,vmin=-30,vmax=30,cmap='seismic')\n",
+    "        ax.set_xticklabels([])\n",
+    "        ax.set_ylabel('Latitude', fontsize=14)\n",
+    "        ax.text(0.0, 1.03, '(d) ', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 4:\n",
+    "        \n",
+    "        ax.pcolor(lon,lat,ds_lib2[6]['ddt_radlw'][4,:,:,70].T.values,vmin=-30,vmax=30,cmap='seismic')\n",
+    "        ax.set_xticklabels([])\n",
+    "        ax.set_yticklabels([])\n",
+    "        ax.text(0.0, 1.03, '(e) ', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 5:\n",
+    "        \n",
+    "        ax.pcolor(lon,lat,(ds_lib2[6]['ddt_radlw']+ds_lib2[8]['ddt_radlw'])[4,:,:,70].T.values,vmin=-30,vmax=30,cmap='seismic')\n",
+    "        ax.set_xticklabels([])\n",
+    "        ax.set_yticklabels([])\n",
+    "        ax.text(0.0, 1.03, '(f) ', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 6:\n",
+    "        \n",
+    "        diffsw = ds_lib2[1]['ddt_radlw_cg'][4,:,:,70].T.values - ds_lib2[8]['ddt_radlw'][4,:,:,70].T.values\n",
+    "        ax.pcolor(lon,lat,diffsw,vmin=-30,vmax=30,cmap='seismic')\n",
+    "        ax.set_xticklabels([])\n",
+    "        ax.set_ylabel('Latitude', fontsize=14)\n",
+    "        ax.text(0.0, 1.03, '(g) ', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 7:\n",
+    "        \n",
+    "        difflw = ds_lib2[0]['ddt_radlw_cg'][4,:,:,70].T.values - ds_lib2[6]['ddt_radlw'][4,:,:,70].T.values\n",
+    "        ax.pcolor(lon,lat,difflw,vmin=-30,vmax=30,cmap='seismic')\n",
+    "        ax.set_xticklabels([])\n",
+    "        ax.set_yticklabels([])\n",
+    "        ax.text(0.0, 1.03, '(h) ', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 8:\n",
+    "        \n",
+    "        diffsw = ds_lib2[1]['ddt_radlw_cg'][4,:,:,70].T.values - ds_lib2[8]['ddt_radlw'][4,:,:,70].T.values\n",
+    "        difflw = ds_lib2[0]['ddt_radlw_cg'][4,:,:,70].T.values - ds_lib2[6]['ddt_radlw'][4,:,:,70].T.values\n",
+    "        diffnt = difflw + diffsw\n",
+    "        ax.pcolor(lon,lat,diffnt,vmin=-30,vmax=30,cmap='seismic')\n",
+    "        ax.set_xticklabels([])\n",
+    "        ax.set_yticklabels([])\n",
+    "        ax.text(0.0, 1.03, '(i) ', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 9:\n",
+    "        \n",
+    "        diffsw = ds_lib2[1]['ddt_radlw_cg'][4,:,:,70].T.values - ds_lib2[9]['ddt_radlw'][4,:,:,70].T.values\n",
+    "        ax.pcolor(lon,lat,diffsw,vmin=-30,vmax=30,cmap='seismic')\n",
+    "        ax.set_ylabel('Latitude', fontsize=14)\n",
+    "        ax.set_xlabel('Longitude', fontsize=14)\n",
+    "        ax.text(0.0, 1.03, '(j) ', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "\n",
+    "    if i == 10:\n",
+    "        \n",
+    "        difflw = ds_lib2[0]['ddt_radlw_cg'][4,:,:,70].T.values - ds_lib2[7]['ddt_radlw'][4,:,:,70].T.values\n",
+    "        ax.pcolor(lon,lat,difflw,vmin=-30,vmax=30,cmap='seismic')\n",
+    "        ax.set_yticklabels([])\n",
+    "        ax.set_xlabel('Longitude', fontsize=14)\n",
+    "        ax.text(0.0, 1.03, '(k) ', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "\n",
+    "    if i == 11:\n",
+    "        \n",
+    "        diffsw = ds_lib2[1]['ddt_radlw_cg'][4,:,:,70].T.values - ds_lib2[9]['ddt_radlw'][4,:,:,70].T.values\n",
+    "        difflw = ds_lib2[0]['ddt_radlw_cg'][4,:,:,70].T.values - ds_lib2[7]['ddt_radlw'][4,:,:,70].T.values\n",
+    "        diffnt = difflw + diffsw\n",
+    "        ax.pcolor(lon,lat,diffnt,vmin=-30,vmax=30,cmap='seismic')\n",
+    "        ax.set_yticklabels([])\n",
+    "        ax.set_xlabel('Longitude', fontsize=14)\n",
+    "        ax.text(0.0, 1.03, '(l) ', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    i = i + 1\n",
+    "    \n",
+    "fig.subplots_adjust(wspace=0.1,hspace=0.2)\n",
+    "\n",
+    "cb_ax = fig.add_axes([0.92, 0.56, 0.015, 0.32]) # xcenter/ycenter/width/height\n",
+    "cbar = fig.colorbar(im,cax=cb_ax,orientation='vertical',shrink=0.95,extend='both')\n",
+    "cbar.set_label(label='K day$^{-1}$', size='13',labelpad=1)\n",
+    "cbar.ax.tick_params(labelsize=13)\n",
+    "\n",
+    "\n",
+    "plt.text(.5, 0.902, 'Coarse-grained LEM CRH', transform=fig.transFigure, horizontalalignment='center',fontsize=15)\n",
+    "plt.text(.5, 0.702, 'NWP CRH, grid-box clouds', transform=fig.transFigure, horizontalalignment='center',fontsize=15)\n",
+    "plt.text(.5, 0.502, 'Coarse-grained LEM CRH - NWP CRH, grid-box homogeneous clouds', transform=fig.transFigure, horizontalalignment='center',fontsize=15)\n",
+    "plt.text(.5, 0.302, 'Coarse-grained LEM CRH - NWP CRH, homogeneous clouds with fractional cloud cover ', transform=fig.transFigure, horizontalalignment='center',fontsize=15)\n",
+    "\n",
+    "plt.savefig('sfigure2.png', bbox_inches = 'tight',dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bf9d53b5-d026-47cf-a4ed-a9afe1cb6d5f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pycrh",
+   "language": "python",
+   "name": "pycrh"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/plots4paper/figure10.pdf b/plots4paper/figure10.pdf
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diff --git a/plots4paper/figure11.ipynb b/plots4paper/figure11.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..36a34aa79d3f35d32ed6615913dcb674cca7ae20
--- /dev/null
+++ b/plots4paper/figure11.ipynb
@@ -0,0 +1,878 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "115f93b6-8a4f-4f98-a0dc-2c79cceb1a7a",
+   "metadata": {},
+   "source": [
+    "# Figure 11\n",
+    "\n",
+    "Same as Fig.10 but for local uncertainties of net CRH. For comparison, the mean values of net CRH uncertainty from Fig.10 are\n",
+    "superimposed as gray hatched bars. The mean uncertainties smaller than 0.05 k are not shown as they would not be visible in the plot. Note\n",
+    "the different x-axes in the panels\n",
+    "\n",
+    "---\n",
+    "@ Behrooz Keshtgar, KIT 2024"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c69547d-1bf2-4b27-957a-95e15f48812c",
+   "metadata": {},
+   "source": [
+    "## 1- load python packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "039407e9-ae55-437b-8d36-b67a5aa76fa2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import xarray as xr\n",
+    "import pandas as pd\n",
+    "import colorlegend\n",
+    "from matplotlib.ticker import (MultipleLocator, AutoMinorLocator)\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bf9aea5-6e6e-46c9-925d-ce53a9042b21",
+   "metadata": {},
+   "source": [
+    "For reference, print package versions to screen:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "090549dc-8a05-4a15-a43a-2bef20f073d4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "xarrary:    0.16.0\n",
+      "numpy:      1.23.5\n",
+      "matplotlib: 3.3.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('xarrary:   ', xr.__version__)\n",
+    "print('numpy:     ', np.__version__)\n",
+    "import matplotlib; print('matplotlib:', matplotlib.__version__); del matplotlib"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3048f403-40bd-49af-9ab4-96793473069a",
+   "metadata": {},
+   "source": [
+    "## 2- Loading datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "17197b49-0a32-4209-8337-ec881307a0cb",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Density profiles from previous analysis, figure 10\n",
+    "ds_rho = xr.open_dataset('/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/postprocessed_data_for_publication/for_publication/figure10.nc')[['rho_mean_icon_dom01', 'rho_mean_icon_dom02', 'rho_mean_icon_dom03', 'rho_mean_icon_dom04']]\n",
+    "# post_processed MAD analysis at different resolution and for all LEM domains\n",
+    "ds_mad = xr.open_dataset('/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/offline_radiation_calculation_output/lem_crhun_diag/crh_unc_domain_mean.nc')\n",
+    "# AMD analysis for all LEM domains, figure 10\n",
+    "ds_amd = xr.open_dataset('/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/postprocessed_data_for_publication/for_publication/figure10.nc')\n",
+    "\n",
+    "### For data publication\n",
+    "\n",
+    "# creating a dataset and save for data publication\n",
+    "ds_out = xr.merge([ds_rho,ds_mad])\n",
+    "ds_out.attrs['description'] = 'Vertical profiles of MAD of CRH from different offline radiation calculation at different resolution for each LEM domain'\n",
+    "#ds_out.to_netcdf('/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/postprocessed_data_for_publication/for_publication/figure11.nc')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "c8763751-2796-4396-8a83-f9e23adb0266",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "ds_out = xr.open_dataset('/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/postprocessed_data_for_publication/for_publication/figure11.nc')\n",
+    "# AMD analysis for all LEM domains, figure 10\n",
+    "ds_amd = xr.open_dataset('/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/postprocessed_data_for_publication/for_publication/figure10.nc')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "872417af-0e22-4887-8302-df6e590da9bb",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## 3- Calculating weighted vertical mean of mean absolute differences (MAD)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e8a7f11d-26e0-45d2-8c31-232282b3fa49",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def weight_vertical_mean(ds, dom):\n",
+    "    \"\"\"\n",
+    "    Calculate weighted vertical mean of MAD analysis in vertical intervals of 2 km.\n",
+    "    Input is a dataset containing MAD from different radiation calculations.\n",
+    "    \"\"\"  \n",
+    "    rho = ds[f'rho_mean_icon_{dom}'].values\n",
+    "    dp = np.zeros((140))\n",
+    "    dp[1:-1] = (rho[2:] - rho[:-2]) \n",
+    "    dp[0] = (rho[1] - rho[0]) \n",
+    "    dp[-1] = (rho[-1] - rho[-2])\n",
+    "    dp = dp[::-1]\n",
+    "    \n",
+    "    mad_list = []\n",
+    "    # Define functions for repeated calculations\n",
+    "    def calculate_mad_vm(data, dp):\n",
+    "        return np.array([(np.sum(data[i:j] * dp[i:j]) / np.sum(dp[i:j])) for i, j in zip([0, 28, 45, 59, 71, 82], [28, 45, 59, 71, 82, 92])])\n",
+    "\n",
+    "    for source in ['3d','hg','vo','ic']:\n",
+    "        # Impact of ice-optical param\n",
+    "        if dom == 'dom01' and source == 'ic':\n",
+    "            mad_list.extend([np.zeros(6), np.zeros(6), np.zeros(6)])\n",
+    "        else:\n",
+    "            # longwave\n",
+    "            mad_list.append(calculate_mad_vm(ds[f'{dom}_{source}_lw_error'][0].values,dp))\n",
+    "            # shortwave\n",
+    "            mad_list.append(calculate_mad_vm(ds[f'{dom}_{source}_sw_error'][0].values,dp))\n",
+    "            # net\n",
+    "            mad_list.append(calculate_mad_vm(ds[f'{dom}_{source}_nt_error'][0].values,dp))\n",
+    "        \n",
+    "        \n",
+    "    return mad_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "67c238ac-ea4a-4507-b188-ad7fc9ec2e0f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "mad_dom01 = weight_vertical_mean(ds_out,'dom01')\n",
+    "mad_dom02 = weight_vertical_mean(ds_out,'dom02')\n",
+    "mad_dom03 = weight_vertical_mean(ds_out,'dom03')\n",
+    "mad_dom04 = weight_vertical_mean(ds_out,'dom04')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "146c230e-37a1-4e2b-adc7-c350eff1c676",
+   "metadata": {},
+   "source": [
+    "### 3-1 Calculating weighted vertical mean of absolute mean differences (AMD)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "61184ec6-9004-4cbd-babf-54f5a1d1eee6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def absolute_mean_difference(ds, dom, rho):\n",
+    "    \"\"\"\n",
+    "    Calculate AMD and weighted vertical mean in vertical intervals of 2 km.\n",
+    "    Input is a dataset containing time and spatial mean from different radiation calculations.\n",
+    "    \"\"\"\n",
+    "    # thickness\n",
+    "    dp = np.zeros((140))\n",
+    "    dp[1:-1] = (rho[2:] - rho[:-2]) \n",
+    "    dp[0] = (rho[1] - rho[0]) \n",
+    "    dp[-1] = (rho[-1] - rho[-2])\n",
+    "    dp = dp[::-1]\n",
+    "    \n",
+    "    amd_list = []\n",
+    "    # Define functions for repeated calculations\n",
+    "    def calculate_amd_vm(data, dp):\n",
+    "        return np.array([(np.sum(data[i:j] * dp[i:j]) / np.sum(dp[i:j])) for i, j in zip([0, 28, 45, 59, 71, 82], [28, 45, 59, 71, 82, 92])])\n",
+    "\n",
+    "    def calculate_and_append_amd_vm(prefix1, prefix2, ds):\n",
+    "        if prefix1 == 'smean_mystic':\n",
+    "            lw_amd = np.abs((ds[f'lwcrh_{prefix1}_{dom}'].mean('time') - ds[f'lwcrh_{prefix2}_{dom}'].mean('time')).values)\n",
+    "            sw_amd = np.abs((ds[f'swcrh_{prefix1}_{dom}'].mean('time') - ds[f'swcrh_{prefix2}_{dom}'].mean('time')).values)\n",
+    "            \n",
+    "            net1 = ds[f'lwcrh_{prefix1}_{dom}'].mean('time') + ds[f'swcrh_{prefix1}_{dom}'].mean('time')\n",
+    "            net2 = ds[f'lwcrh_{prefix2}_{dom}'].mean('time') + ds[f'swcrh_{prefix2}_{dom}'].mean('time')\n",
+    "            nt_amd = np.abs((net1 - net2).values)\n",
+    "            \n",
+    "        else:\n",
+    "            lw_amd = np.abs((ds[f'lwcrh_{prefix1}_{dom}'] - ds[f'lwcrh_{prefix2}_{dom}']).values)\n",
+    "            sw_amd = np.abs((ds[f'swcrh_{prefix1}_{dom}'] - ds[f'swcrh_{prefix2}_{dom}']).values)\n",
+    "            \n",
+    "            net1 = ds[f'lwcrh_{prefix1}_{dom}'] + ds[f'swcrh_{prefix1}_{dom}']\n",
+    "            net2 = ds[f'lwcrh_{prefix2}_{dom}'] + ds[f'swcrh_{prefix2}_{dom}']\n",
+    "            nt_amd = np.abs((net1 - net2).values)\n",
+    "        \n",
+    "        lw_amd_vm = calculate_amd_vm(lw_amd, dp)\n",
+    "        sw_amd_vm = calculate_amd_vm(sw_amd, dp)\n",
+    "        nt_amd_vm = calculate_amd_vm(nt_amd, dp)\n",
+    "    \n",
+    "        amd_list.append(lw_amd_vm)\n",
+    "        amd_list.append(sw_amd_vm)\n",
+    "        amd_list.append(nt_amd_vm)\n",
+    "\n",
+    "    # 3D cloud radiative effects\n",
+    "    calculate_and_append_amd_vm('smean_mystic', 'smean_mystic_ica', ds)\n",
+    "\n",
+    "    # Impact of cloud horizontal heterogeneity\n",
+    "    calculate_and_append_amd_vm('mean_nwp', 'mean_lem', ds)\n",
+    "\n",
+    "    # Impact of cloud horizontal heterogeneity and cloud vertical overlap\n",
+    "    calculate_and_append_amd_vm('mean_nwpfr', 'mean_lem', ds)\n",
+    "\n",
+    "    # Impact of ice-optical param\n",
+    "    if dom == 'dom01':\n",
+    "        amd_list.extend([np.zeros(6), np.zeros(6), np.zeros(6)])\n",
+    "    else:\n",
+    "        calculate_and_append_amd_vm('mean_fu', 'mean_Baum_ghm', ds)\n",
+    "\n",
+    "    return amd_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "f7e6fe33-27ee-42e2-900f-a29b01cabf82",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "amd_dom01 = absolute_mean_difference(ds_amd,'dom01',ds_amd['rho_mean_icon_dom01'].values)\n",
+    "amd_dom02 = absolute_mean_difference(ds_amd,'dom02',ds_amd['rho_mean_icon_dom02'].values)\n",
+    "amd_dom03 = absolute_mean_difference(ds_amd,'dom03',ds_amd['rho_mean_icon_dom03'].values)\n",
+    "amd_dom04 = absolute_mean_difference(ds_amd,'dom04',ds_amd['rho_mean_icon_dom04'].values)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f5a483de-2599-46a4-923f-932309dd5663",
+   "metadata": {},
+   "source": [
+    "## 4- Plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "a3846cc7-917c-48d3-8e05-0db4b2cd1a0d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x216 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# height intervals\n",
+    "h1 = np.arange(0,6,1)\n",
+    "threshold = 0.05 # the threshold value to mask data\n",
+    "\n",
+    "fig, axes = plt.subplots(nrows=1, ncols=4, figsize=(20, 3)) \n",
+    "i = 0\n",
+    "\n",
+    "for ax in axes.flat:\n",
+    "    ax.tick_params(labelsize=14)\n",
+    "    ax.spines['top'].set_color('none')\n",
+    "    ax.spines['right'].set_color('none')\n",
+    "    \n",
+    "        \n",
+    "    ############################################################\n",
+    "    ####### Net ################################################\n",
+    "    ############################################################\n",
+    "    if i == 0:\n",
+    "        \n",
+    "        ax.barh(h1, mad_dom01[2], color='#000075', label='3D effetcs')\n",
+    "        ax.barh(h1, mad_dom01[5], color='#4363d8', left=mad_dom01[2], label='Horizontal heterogeneity')\n",
+    "        ax.barh(h1, mad_dom01[8], color='#469990', left=mad_dom01[2]+mad_dom01[5], label='Horizontal heterogeneity and vertical overlap')\n",
+    "        ax.barh(h1, mad_dom01[11], color='#e6194B', left=mad_dom01[2]+mad_dom01[5]+mad_dom01[8], label='Ice-optical parameterization')\n",
+    "        \n",
+    "        # Apply a mask\n",
+    "        masked_amd_dom01_2 = np.where(amd_dom01[2] < threshold, np.nan, amd_dom01[2])\n",
+    "        ax.barh(h1, masked_amd_dom01_2, edgecolor='lightgray', label='3D effects', fill=False, hatch=\"//\", linewidth=1,\n",
+    "                height=0.725)\n",
+    "        \n",
+    "        masked_amd_dom01_5 = np.where(amd_dom01[5] < threshold, np.nan, amd_dom01[5])\n",
+    "        ax.barh(h1, masked_amd_dom01_5, edgecolor='lightgray', left=mad_dom01[2], label='Horizontal heterogeneity',\n",
+    "                fill=False, hatch=\"//\", linewidth=1, height=0.725)\n",
+    "\n",
+    "        masked_amd_dom01_8 = np.where(amd_dom01[8] < threshold, np.nan, amd_dom01[8])\n",
+    "        ax.barh(h1, masked_amd_dom01_8, edgecolor='lightgray', left=mad_dom01[2] + mad_dom01[5],\n",
+    "                label='Horizontal heterogeneity', fill=False, hatch=\"//\", linewidth=1, height=0.725)\n",
+    "\n",
+    "        #ax.barh(h1, amd_dom01[2], edgecolor='lightgray', label='3D effetcs',fill = False, hatch=\"//\",linewidth=1,height=0.725)\n",
+    "        #ax.barh(h1, amd_dom01[5], edgecolor='lightgray', left=mad_dom01[2], label='Horizontal heterogeneity',fill = False, hatch=\"//\",linewidth=1,height=0.725)\n",
+    "        #ax.barh(h1, amd_dom01[8], edgecolor='lightgray', left=mad_dom01[2]+mad_dom01[5], label='Horizontal heterogeneity',fill = False, hatch=\"//\",linewidth=1,height=0.725)\n",
+    "    \n",
+    "    \n",
+    "        ax.set_title('Shallow cumulus', fontsize=15)\n",
+    "        ax.set_ylabel('Height intervals (km)', fontsize=14)\n",
+    "        ax.set_ylim([-0.5,5.5])\n",
+    "        ax.spines['left'].set_bounds(-0.5,5.5)\n",
+    "        ax.set_yticks([0,1,2,3,4,5])\n",
+    "        ax.set_yticklabels(['0-2','2-4','4-6','6-8','8-10','10-12'])\n",
+    "        \n",
+    "        ax.set_xlim([0.,6.5])\n",
+    "        ax.spines['bottom'].set_bounds(0,6.5)\n",
+    "        ax.set_xticks(np.linspace(0,6.5,6))\n",
+    "        minor_locator = AutoMinorLocator(2)\n",
+    "        ax.xaxis.set_minor_locator(minor_locator)\n",
+    "        \n",
+    "        ax.set_xlabel('Net CRH uncertainty (K day$^{-1}$)', fontsize=14)\n",
+    "        \n",
+    "        ax.text(0.0, 1.03, '(a)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "        ax.text(0.5, 5.2, '3D effects', color = '#000075', size=12, va=\"top\")\n",
+    "        ax.text(0.5, 4.5, 'Cloud horizontal heterogeneity,\\nwithout overlap assumption', color='#4363d8', size=12, va=\"top\")\n",
+    "        ax.text(0.5, 3.2, 'Cloud horizontal heterogeneity,\\nwith overlap assumption', color='#469990', size=12, va=\"top\")\n",
+    "        ax.text(0.5, 1.9, 'Ice-optical parameterization', color='#e6194B', size=12, va=\"top\")\n",
+    "        \n",
+    "    if i == 1:\n",
+    "        \n",
+    "        ax.barh(h1, mad_dom02[2], color='#000075', label='3D effetcs')\n",
+    "        ax.barh(h1, mad_dom02[5], color='#4363d8', left=mad_dom02[2], label='Horizontal heterogeneity')\n",
+    "        ax.barh(h1, mad_dom02[8], color='#469990', left=mad_dom02[2]+mad_dom02[5], label='Horizontal heterogeneity and vertical overlap')\n",
+    "        ax.barh(h1, mad_dom02[11], color='#e6194B', left=mad_dom02[2]+mad_dom02[5]+mad_dom02[8], label='Ice-optical parameterization')\n",
+    "        \n",
+    "        # Apply a mask\n",
+    "        masked_amd_dom02_2 = np.where(amd_dom02[2] < threshold, np.nan, amd_dom02[2])\n",
+    "        ax.barh(h1, masked_amd_dom02_2, edgecolor='lightgray', label='3D effects', fill=False, hatch=\"//\", linewidth=1,\n",
+    "                height=0.725)\n",
+    "        \n",
+    "        masked_amd_dom02_5 = np.where(amd_dom02[5] < threshold, np.nan, amd_dom02[5])\n",
+    "        ax.barh(h1, masked_amd_dom02_5, edgecolor='lightgray', left=mad_dom02[2], label='Horizontal heterogeneity',\n",
+    "                fill=False, hatch=\"//\", linewidth=1, height=0.725)\n",
+    "\n",
+    "        masked_amd_dom02_8 = np.where(amd_dom02[8] < threshold, np.nan, amd_dom02[8])\n",
+    "        ax.barh(h1, masked_amd_dom02_8, edgecolor='lightgray', left=mad_dom02[2] + mad_dom02[5],\n",
+    "                label='Horizontal heterogeneity', fill=False, hatch=\"//\", linewidth=1, height=0.725)\n",
+    "        \n",
+    "        masked_amd_dom02_11 = np.where(amd_dom02[11] < threshold, np.nan, amd_dom02[11])\n",
+    "        ax.barh(h1, masked_amd_dom02_11, edgecolor='lightgray', left=mad_dom02[2] + mad_dom02[5] + mad_dom02[8],\n",
+    "                label='Ice-optical parameterization', fill=False, hatch=\"//\", linewidth=1, height=0.725)\n",
+    "        \n",
+    "        #ax.barh(h1, amd_dom02[2], edgecolor='lightgray', label='3D effetcs',fill = False, hatch=\"//\",linewidth=1,height=0.725)\n",
+    "        #ax.barh(h1, amd_dom02[5], edgecolor='lightgray', left=mad_dom02[2], label='Horizontal heterogeneity',fill = False, hatch=\"//\",linewidth=1,height=0.725)\n",
+    "        #ax.barh(h1, amd_dom02[8], edgecolor='lightgray', left=mad_dom02[2]+mad_dom02[5], label='Horizontal heterogeneity',fill = False, hatch=\"//\",linewidth=1,height=0.725)\n",
+    "        #ax.barh(h1, amd_dom02[11], edgecolor='lightgray', left=mad_dom02[2]+mad_dom02[5]+mad_dom02[8], label='Ice-optical parameterization',fill = False, hatch=\"//\",linewidth=1,height=0.725)\n",
+    "        \n",
+    "        ax.set_ylim([-0.5,5.5])\n",
+    "        ax.spines['left'].set_bounds(-0.5,5.5)\n",
+    "        ax.set_yticks([0,1,2,3,4,5])\n",
+    "        ax.set_yticklabels(['0-2','2-4','4-6','6-8','8-10','10-12'])\n",
+    "        ax.set_yticklabels([])\n",
+    "        \n",
+    "        ax.set_xlim([0.,3.5])\n",
+    "        ax.spines['bottom'].set_bounds(0,3.5)\n",
+    "        ax.set_xticks(np.linspace(0,3.5,6))\n",
+    "        minor_locator = AutoMinorLocator(2)\n",
+    "        ax.xaxis.set_minor_locator(minor_locator)\n",
+    "        \n",
+    "        ax.set_title('WCB ascent', fontsize=15)\n",
+    "        ax.set_xlabel('Net CRH uncertainty (K day$^{-1}$)', fontsize=14)\n",
+    "        \n",
+    "        ax.text(0.0, 1.03, '(b)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 2:\n",
+    "        \n",
+    "        ax.barh(h1, mad_dom03[2], color='#000075', label='3D effetcs')\n",
+    "        ax.barh(h1, mad_dom03[5], color='#4363d8', left=mad_dom03[2], label='Horizontal heterogeneity')\n",
+    "        ax.barh(h1, mad_dom03[8], color='#469990', left=mad_dom03[2]+mad_dom03[5], label='Horizontal heterogeneity and vertical overlap')\n",
+    "        ax.barh(h1, mad_dom03[11], color='#e6194B', left=mad_dom03[2]+mad_dom03[5]+mad_dom03[8], label='Ice-optical parameterization')\n",
+    "        \n",
+    "        # Apply a mask\n",
+    "        masked_amd_dom03_2 = np.where(amd_dom03[2] < threshold, np.nan, amd_dom03[2])\n",
+    "        ax.barh(h1, masked_amd_dom03_2, edgecolor='lightgray', label='3D effects', fill=False, hatch=\"//\", linewidth=1,\n",
+    "                height=0.725)\n",
+    "        \n",
+    "        masked_amd_dom03_5 = np.where(amd_dom03[5] < threshold, np.nan, amd_dom03[5])\n",
+    "        ax.barh(h1, masked_amd_dom03_5, edgecolor='lightgray', left=mad_dom03[2], label='Horizontal heterogeneity',\n",
+    "                fill=False, hatch=\"//\", linewidth=1, height=0.725)\n",
+    "\n",
+    "        masked_amd_dom03_8 = np.where(amd_dom03[8] < threshold, np.nan, amd_dom03[8])\n",
+    "        ax.barh(h1, masked_amd_dom03_8, edgecolor='lightgray', left=mad_dom03[2] + mad_dom03[5],\n",
+    "                label='Horizontal heterogeneity', fill=False, hatch=\"//\", linewidth=1, height=0.725)\n",
+    "        \n",
+    "        masked_amd_dom03_11 = np.where(amd_dom03[11] < threshold, np.nan, amd_dom03[11])\n",
+    "        ax.barh(h1, masked_amd_dom03_11, edgecolor='lightgray', left=mad_dom03[2] + mad_dom03[5] + mad_dom03[8],\n",
+    "                label='Ice-optical parameterization', fill=False, hatch=\"//\", linewidth=1, height=0.725)\n",
+    "        \n",
+    "        #ax.barh(h1, amd_dom03[2], edgecolor='lightgray', label='3D effetcs',fill = False, hatch=\"//\",linewidth=1,height=0.725)\n",
+    "        #ax.barh(h1, amd_dom03[5], edgecolor='lightgray', left=mad_dom03[2], label='Horizontal heterogeneity',fill = False, hatch=\"//\",linewidth=1,height=0.725)\n",
+    "        #ax.barh(h1, amd_dom03[8], edgecolor='lightgray', left=mad_dom03[2]+mad_dom03[5], label='Horizontal heterogeneity',fill = False, hatch=\"//\",linewidth=1,height=0.725)\n",
+    "        #ax.barh(h1, amd_dom03[11], edgecolor='lightgray', left=mad_dom03[2]+mad_dom03[5]+mad_dom03[8], label='Ice-optical parameterization',fill = False, hatch=\"//\",linewidth=1,height=0.725)\n",
+    "    \n",
+    "        ax.set_ylim([-0.5,5.5])\n",
+    "        ax.spines['left'].set_bounds(-0.5,5.5)\n",
+    "        ax.set_yticks([0,1,2,3,4,5])\n",
+    "        ax.set_yticklabels(['0-2','2-4','4-6','6-8','8-10','10-12'])\n",
+    "        ax.set_yticklabels([])\n",
+    "        \n",
+    "        ax.set_xlim([0.,2.5])\n",
+    "        ax.spines['bottom'].set_bounds(0,2.5)\n",
+    "        ax.set_xticks(np.linspace(0,2.5,6))\n",
+    "        minor_locator = AutoMinorLocator(2)\n",
+    "        ax.xaxis.set_minor_locator(minor_locator)\n",
+    "        \n",
+    "        ax.set_title('WCB cyclonic outflow', fontsize=15)\n",
+    "        ax.set_xlabel('Net CRH uncertainty (K day$^{-1}$)', fontsize=14)\n",
+    "        \n",
+    "        ax.text(0.0, 1.03, '(c)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 3:\n",
+    "        \n",
+    "        ax.barh(h1, mad_dom04[2], color='#000075', label='3D effetcs')\n",
+    "        ax.barh(h1, mad_dom04[5], color='#4363d8', left=mad_dom04[2], label='Horizontal heterogeneity')\n",
+    "        ax.barh(h1, mad_dom04[8], color='#469990', left=mad_dom04[2]+mad_dom04[5], label='Horizontal heterogeneity and vertical overlap')\n",
+    "        ax.barh(h1, mad_dom04[11], color='#e6194B', left=mad_dom04[2]+mad_dom04[5]+mad_dom04[8], label='Ice-optical parameterization')\n",
+    "        \n",
+    "        # Apply a mask\n",
+    "        masked_amd_dom04_2 = np.where(amd_dom04[2] < threshold, np.nan, amd_dom04[2])\n",
+    "        ax.barh(h1, masked_amd_dom04_2, edgecolor='lightgray', label='3D effects', fill=False, hatch=\"//\", linewidth=1,\n",
+    "                height=0.725)\n",
+    "        \n",
+    "        masked_amd_dom04_5 = np.where(amd_dom04[5] < threshold, np.nan, amd_dom04[5])\n",
+    "        ax.barh(h1, masked_amd_dom04_5, edgecolor='lightgray', left=mad_dom04[2], label='Horizontal heterogeneity',\n",
+    "                fill=False, hatch=\"//\", linewidth=1, height=0.725)\n",
+    "\n",
+    "        masked_amd_dom04_8 = np.where(amd_dom04[8] < threshold, np.nan, amd_dom04[8])\n",
+    "        ax.barh(h1, masked_amd_dom04_8, edgecolor='lightgray', left=mad_dom04[2] + mad_dom04[5],\n",
+    "                label='Horizontal heterogeneity', fill=False, hatch=\"//\", linewidth=1, height=0.725)\n",
+    "        \n",
+    "        masked_amd_dom04_11 = np.where(amd_dom04[11] < threshold, np.nan, amd_dom04[11])\n",
+    "        ax.barh(h1, masked_amd_dom04_11, edgecolor='lightgray', left=mad_dom04[2] + mad_dom04[5] + mad_dom04[8],\n",
+    "                label='Ice-optical parameterization', fill=False, hatch=\"//\", linewidth=1, height=0.725)\n",
+    "        \n",
+    "        #ax.barh(h1, amd_dom04[2], edgecolor='lightgray', label='3D effetcs',fill = False, hatch=\"//\",linewidth=1,height=0.725)\n",
+    "        #ax.barh(h1, amd_dom04[5], edgecolor='lightgray', left=mad_dom04[2], label='Horizontal heterogeneity',fill = False, hatch=\"//\",linewidth=1,height=0.725)\n",
+    "        #ax.barh(h1, amd_dom04[8], edgecolor='lightgray', left=mad_dom04[2]+mad_dom04[5], label='Horizontal heterogeneity',fill = False, hatch=\"//\",linewidth=1,height=0.725)\n",
+    "        #ax.barh(h1, amd_dom04[11], edgecolor='lightgray', left=mad_dom04[2]+mad_dom04[5]+mad_dom04[8], label='Ice-optical parameterization',fill = False, hatch=\"//\",linewidth=1,height=0.725)\n",
+    "        \n",
+    "        ax.set_ylim([-0.5,5.5])\n",
+    "        ax.spines['left'].set_bounds(-0.5,5.5)\n",
+    "        ax.set_yticks([0,1,2,3,4,5])\n",
+    "        ax.set_yticklabels(['0-2','2-4','4-6','6-8','8-10','10-12'])\n",
+    "        ax.set_yticklabels([])\n",
+    "        \n",
+    "        ax.set_xlim([0.,2])\n",
+    "        ax.spines['bottom'].set_bounds(0,2)\n",
+    "        ax.set_xticks(np.linspace(0,2,6))\n",
+    "        minor_locator = AutoMinorLocator(2)\n",
+    "        ax.xaxis.set_minor_locator(minor_locator)\n",
+    "        \n",
+    "        ax.set_title('WCB anticyclonic outflow', fontsize=15)\n",
+    "        ax.set_xlabel('Net CRH uncertainty (K day$^{-1}$)', fontsize=14)\n",
+    "        \n",
+    "        ax.text(0.0, 1.03, '(d)', transform=ax.transAxes, \n",
+    "            size=14)    \n",
+    "     \n",
+    "    i = i + 1     \n",
+    "\n",
+    "fig.subplots_adjust(wspace=0.12,hspace=0.55)\n",
+    "\n",
+    "plt.savefig('figure11.pdf', bbox_inches = 'tight')\n",
+    "plt.savefig('figure11.png', bbox_inches = 'tight',dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7e35dd8e-294a-4d97-98d0-79c3ade4045f",
+   "metadata": {},
+   "source": [
+    "## Latitude-longitude plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "id": "8ea9fef3-f5ff-43b9-9e6a-53c9c8f7c64d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x1656 with 13 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(nrows=4, ncols=3, figsize=(15, 23))\n",
+    "i = 0\n",
+    "\n",
+    "## coordinates for plotting\n",
+    "lat = np.linspace(41,47,198)\n",
+    "lon = np.linspace(-1,5,246)\n",
+    "\n",
+    "for ax in axes.flat:\n",
+    "    ax.tick_params(labelsize=14)\n",
+    "    \n",
+    "    if i == 0:\n",
+    "     \n",
+    "        im = ax.pcolor(lon,lat,ds_lib2[1]['ddt_radlw_cg'][4,:,:,70].T.values,vmin=-30,vmax=30,cmap='seismic')\n",
+    "        ax.set_xticklabels([])\n",
+    "        ax.set_ylabel('Latitude', fontsize=14)\n",
+    "        ax.set_title(' Shortwave CRH', fontsize=14)\n",
+    "        ax.text(0.0, 1.03, '(a) ', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 1:    \n",
+    "\n",
+    "        ax.pcolor(lon,lat,ds_lib2[0]['ddt_radlw_cg'][4,:,:,70].T.values,vmin=-30,vmax=30,cmap='seismic')\n",
+    "        ax.set_xticklabels([])\n",
+    "        ax.set_yticklabels([])\n",
+    "        ax.set_title(' Longwave CRH', fontsize=14)\n",
+    "        ax.text(0.0, 1.03, '(b) ', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 2:\n",
+    "        \n",
+    "        ax.pcolor(lon,lat,(ds_lib2[1]['ddt_radlw_cg']+ds_lib2[0]['ddt_radlw_cg'])[4,:,:,70].T.values,vmin=-30,vmax=30,cmap='seismic')\n",
+    "        ax.set_xticklabels([])\n",
+    "        ax.set_yticklabels([])\n",
+    "        ax.set_title(' Total CRH', fontsize=14)\n",
+    "        ax.text(0.0, 1.03, '(c) ', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 3:\n",
+    "        \n",
+    "        ax.pcolor(lon,lat,ds_lib2[8]['ddt_radlw'][4,:,:,70].T.values,vmin=-30,vmax=30,cmap='seismic')\n",
+    "        ax.set_xticklabels([])\n",
+    "        ax.set_ylabel('Latitude', fontsize=14)\n",
+    "        ax.text(0.0, 1.03, '(d) ', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 4:\n",
+    "        \n",
+    "        ax.pcolor(lon,lat,ds_lib2[6]['ddt_radlw'][4,:,:,70].T.values,vmin=-30,vmax=30,cmap='seismic')\n",
+    "        ax.set_xticklabels([])\n",
+    "        ax.set_yticklabels([])\n",
+    "        ax.text(0.0, 1.03, '(e) ', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 5:\n",
+    "        \n",
+    "        ax.pcolor(lon,lat,(ds_lib2[6]['ddt_radlw']+ds_lib2[8]['ddt_radlw'])[4,:,:,70].T.values,vmin=-30,vmax=30,cmap='seismic')\n",
+    "        ax.set_xticklabels([])\n",
+    "        ax.set_yticklabels([])\n",
+    "        ax.text(0.0, 1.03, '(f) ', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 6:\n",
+    "        \n",
+    "        diffsw = ds_lib2[1]['ddt_radlw_cg'][4,:,:,70].T.values - ds_lib2[8]['ddt_radlw'][4,:,:,70].T.values\n",
+    "        ax.pcolor(lon,lat,diffsw,vmin=-30,vmax=30,cmap='seismic')\n",
+    "        ax.set_xticklabels([])\n",
+    "        ax.set_ylabel('Latitude', fontsize=14)\n",
+    "        ax.text(0.0, 1.03, '(g) ', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 7:\n",
+    "        \n",
+    "        difflw = ds_lib2[0]['ddt_radlw_cg'][4,:,:,70].T.values - ds_lib2[6]['ddt_radlw'][4,:,:,70].T.values\n",
+    "        ax.pcolor(lon,lat,difflw,vmin=-30,vmax=30,cmap='seismic')\n",
+    "        ax.set_xticklabels([])\n",
+    "        ax.set_yticklabels([])\n",
+    "        ax.text(0.0, 1.03, '(h) ', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 8:\n",
+    "        \n",
+    "        diffsw = ds_lib2[1]['ddt_radlw_cg'][4,:,:,70].T.values - ds_lib2[8]['ddt_radlw'][4,:,:,70].T.values\n",
+    "        difflw = ds_lib2[0]['ddt_radlw_cg'][4,:,:,70].T.values - ds_lib2[6]['ddt_radlw'][4,:,:,70].T.values\n",
+    "        diffnt = difflw + diffsw\n",
+    "        ax.pcolor(lon,lat,diffnt,vmin=-30,vmax=30,cmap='seismic')\n",
+    "        ax.set_xticklabels([])\n",
+    "        ax.set_yticklabels([])\n",
+    "        ax.text(0.0, 1.03, '(i) ', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 9:\n",
+    "        \n",
+    "        diffsw = ds_lib2[1]['ddt_radlw_cg'][4,:,:,70].T.values - ds_lib2[9]['ddt_radlw'][4,:,:,70].T.values\n",
+    "        ax.pcolor(lon,lat,diffsw,vmin=-30,vmax=30,cmap='seismic')\n",
+    "        ax.set_ylabel('Latitude', fontsize=14)\n",
+    "        ax.set_xlabel('Longitude', fontsize=14)\n",
+    "        ax.text(0.0, 1.03, '(j) ', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "\n",
+    "    if i == 10:\n",
+    "        \n",
+    "        difflw = ds_lib2[0]['ddt_radlw_cg'][4,:,:,70].T.values - ds_lib2[7]['ddt_radlw'][4,:,:,70].T.values\n",
+    "        ax.pcolor(lon,lat,difflw,vmin=-30,vmax=30,cmap='seismic')\n",
+    "        ax.set_yticklabels([])\n",
+    "        ax.set_xlabel('Longitude', fontsize=14)\n",
+    "        ax.text(0.0, 1.03, '(k) ', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "\n",
+    "    if i == 11:\n",
+    "        \n",
+    "        diffsw = ds_lib2[1]['ddt_radlw_cg'][4,:,:,70].T.values - ds_lib2[9]['ddt_radlw'][4,:,:,70].T.values\n",
+    "        difflw = ds_lib2[0]['ddt_radlw_cg'][4,:,:,70].T.values - ds_lib2[7]['ddt_radlw'][4,:,:,70].T.values\n",
+    "        diffnt = difflw + diffsw\n",
+    "        ax.pcolor(lon,lat,diffnt,vmin=-30,vmax=30,cmap='seismic')\n",
+    "        ax.set_yticklabels([])\n",
+    "        ax.set_xlabel('Longitude', fontsize=14)\n",
+    "        ax.text(0.0, 1.03, '(l) ', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    i = i + 1\n",
+    "    \n",
+    "fig.subplots_adjust(wspace=0.1,hspace=0.2)\n",
+    "\n",
+    "cb_ax = fig.add_axes([0.92, 0.56, 0.015, 0.32]) # xcenter/ycenter/width/height\n",
+    "cbar = fig.colorbar(im,cax=cb_ax,orientation='vertical',shrink=0.95,extend='both')\n",
+    "cbar.set_label(label='K day$^{-1}$', size='13',labelpad=1)\n",
+    "cbar.ax.tick_params(labelsize=13)\n",
+    "\n",
+    "\n",
+    "plt.text(.5, 0.902, 'Coarse-grained LEM CRH', transform=fig.transFigure, horizontalalignment='center',fontsize=15)\n",
+    "plt.text(.5, 0.702, 'NWP CRH, grid-box clouds', transform=fig.transFigure, horizontalalignment='center',fontsize=15)\n",
+    "plt.text(.5, 0.502, 'Coarse-grained LEM CRH - NWP CRH, grid-box homogeneous clouds', transform=fig.transFigure, horizontalalignment='center',fontsize=15)\n",
+    "plt.text(.5, 0.302, 'Coarse-grained LEM CRH - NWP CRH, homogeneous clouds with fractional cloud cover ', transform=fig.transFigure, horizontalalignment='center',fontsize=15)\n",
+    "\n",
+    "#plt.savefig('figure11.pdf', bbox_inches = 'tight')\n",
+    "plt.savefig('sfigure1.png', bbox_inches = 'tight',dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "id": "3f4b915f-cc93-43f8-aa08-76a89b0b2443",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x1656 with 13 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(nrows=4, ncols=3, figsize=(15, 23))\n",
+    "i = 0\n",
+    "\n",
+    "## coordinates for plotting\n",
+    "lat = np.linspace(41,47,198)\n",
+    "lon = np.linspace(-1,5,246)\n",
+    "\n",
+    "for ax in axes.flat:\n",
+    "    ax.tick_params(labelsize=14)\n",
+    "    ax.set_xlim([0,1])\n",
+    "    ax.set_ylim([41.5,42.5])\n",
+    "    \n",
+    "    if i == 0:\n",
+    "     \n",
+    "        im = ax.pcolor(lon,lat,ds_lib2[1]['ddt_radlw_cg'][4,:,:,70].T.values,vmin=-30,vmax=30,cmap='seismic')\n",
+    "        ax.set_xticklabels([])\n",
+    "        ax.set_ylabel('Latitude', fontsize=14)\n",
+    "        ax.set_title(' Shortwave CRH', fontsize=14)\n",
+    "        ax.text(0.0, 1.03, '(a) ', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 1:    \n",
+    "\n",
+    "        ax.pcolor(lon,lat,ds_lib2[0]['ddt_radlw_cg'][4,:,:,70].T.values,vmin=-30,vmax=30,cmap='seismic')\n",
+    "        ax.set_xticklabels([])\n",
+    "        ax.set_yticklabels([])\n",
+    "        ax.set_title(' Longwave CRH', fontsize=14)\n",
+    "        ax.text(0.0, 1.03, '(b) ', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 2:\n",
+    "        \n",
+    "        ax.pcolor(lon,lat,(ds_lib2[1]['ddt_radlw_cg']+ds_lib2[0]['ddt_radlw_cg'])[4,:,:,70].T.values,vmin=-30,vmax=30,cmap='seismic')\n",
+    "        ax.set_xticklabels([])\n",
+    "        ax.set_yticklabels([])\n",
+    "        ax.set_title(' Total CRH', fontsize=14)\n",
+    "        ax.text(0.0, 1.03, '(c) ', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 3:\n",
+    "        \n",
+    "        ax.pcolor(lon,lat,ds_lib2[8]['ddt_radlw'][4,:,:,70].T.values,vmin=-30,vmax=30,cmap='seismic')\n",
+    "        ax.set_xticklabels([])\n",
+    "        ax.set_ylabel('Latitude', fontsize=14)\n",
+    "        ax.text(0.0, 1.03, '(d) ', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 4:\n",
+    "        \n",
+    "        ax.pcolor(lon,lat,ds_lib2[6]['ddt_radlw'][4,:,:,70].T.values,vmin=-30,vmax=30,cmap='seismic')\n",
+    "        ax.set_xticklabels([])\n",
+    "        ax.set_yticklabels([])\n",
+    "        ax.text(0.0, 1.03, '(e) ', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 5:\n",
+    "        \n",
+    "        ax.pcolor(lon,lat,(ds_lib2[6]['ddt_radlw']+ds_lib2[8]['ddt_radlw'])[4,:,:,70].T.values,vmin=-30,vmax=30,cmap='seismic')\n",
+    "        ax.set_xticklabels([])\n",
+    "        ax.set_yticklabels([])\n",
+    "        ax.text(0.0, 1.03, '(f) ', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 6:\n",
+    "        \n",
+    "        diffsw = ds_lib2[1]['ddt_radlw_cg'][4,:,:,70].T.values - ds_lib2[8]['ddt_radlw'][4,:,:,70].T.values\n",
+    "        ax.pcolor(lon,lat,diffsw,vmin=-30,vmax=30,cmap='seismic')\n",
+    "        ax.set_xticklabels([])\n",
+    "        ax.set_ylabel('Latitude', fontsize=14)\n",
+    "        ax.text(0.0, 1.03, '(g) ', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 7:\n",
+    "        \n",
+    "        difflw = ds_lib2[0]['ddt_radlw_cg'][4,:,:,70].T.values - ds_lib2[6]['ddt_radlw'][4,:,:,70].T.values\n",
+    "        ax.pcolor(lon,lat,difflw,vmin=-30,vmax=30,cmap='seismic')\n",
+    "        ax.set_xticklabels([])\n",
+    "        ax.set_yticklabels([])\n",
+    "        ax.text(0.0, 1.03, '(h) ', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 8:\n",
+    "        \n",
+    "        diffsw = ds_lib2[1]['ddt_radlw_cg'][4,:,:,70].T.values - ds_lib2[8]['ddt_radlw'][4,:,:,70].T.values\n",
+    "        difflw = ds_lib2[0]['ddt_radlw_cg'][4,:,:,70].T.values - ds_lib2[6]['ddt_radlw'][4,:,:,70].T.values\n",
+    "        diffnt = difflw + diffsw\n",
+    "        ax.pcolor(lon,lat,diffnt,vmin=-30,vmax=30,cmap='seismic')\n",
+    "        ax.set_xticklabels([])\n",
+    "        ax.set_yticklabels([])\n",
+    "        ax.text(0.0, 1.03, '(i) ', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 9:\n",
+    "        \n",
+    "        diffsw = ds_lib2[1]['ddt_radlw_cg'][4,:,:,70].T.values - ds_lib2[9]['ddt_radlw'][4,:,:,70].T.values\n",
+    "        ax.pcolor(lon,lat,diffsw,vmin=-30,vmax=30,cmap='seismic')\n",
+    "        ax.set_ylabel('Latitude', fontsize=14)\n",
+    "        ax.set_xlabel('Longitude', fontsize=14)\n",
+    "        ax.text(0.0, 1.03, '(j) ', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "\n",
+    "    if i == 10:\n",
+    "        \n",
+    "        difflw = ds_lib2[0]['ddt_radlw_cg'][4,:,:,70].T.values - ds_lib2[7]['ddt_radlw'][4,:,:,70].T.values\n",
+    "        ax.pcolor(lon,lat,difflw,vmin=-30,vmax=30,cmap='seismic')\n",
+    "        ax.set_yticklabels([])\n",
+    "        ax.set_xlabel('Longitude', fontsize=14)\n",
+    "        ax.text(0.0, 1.03, '(k) ', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "\n",
+    "    if i == 11:\n",
+    "        \n",
+    "        diffsw = ds_lib2[1]['ddt_radlw_cg'][4,:,:,70].T.values - ds_lib2[9]['ddt_radlw'][4,:,:,70].T.values\n",
+    "        difflw = ds_lib2[0]['ddt_radlw_cg'][4,:,:,70].T.values - ds_lib2[7]['ddt_radlw'][4,:,:,70].T.values\n",
+    "        diffnt = difflw + diffsw\n",
+    "        ax.pcolor(lon,lat,diffnt,vmin=-30,vmax=30,cmap='seismic')\n",
+    "        ax.set_yticklabels([])\n",
+    "        ax.set_xlabel('Longitude', fontsize=14)\n",
+    "        ax.text(0.0, 1.03, '(l) ', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    i = i + 1\n",
+    "    \n",
+    "fig.subplots_adjust(wspace=0.1,hspace=0.2)\n",
+    "\n",
+    "cb_ax = fig.add_axes([0.92, 0.56, 0.015, 0.32]) # xcenter/ycenter/width/height\n",
+    "cbar = fig.colorbar(im,cax=cb_ax,orientation='vertical',shrink=0.95,extend='both')\n",
+    "cbar.set_label(label='K day$^{-1}$', size='13',labelpad=1)\n",
+    "cbar.ax.tick_params(labelsize=13)\n",
+    "\n",
+    "\n",
+    "plt.text(.5, 0.902, 'Coarse-grained LEM CRH', transform=fig.transFigure, horizontalalignment='center',fontsize=15)\n",
+    "plt.text(.5, 0.702, 'NWP CRH, grid-box clouds', transform=fig.transFigure, horizontalalignment='center',fontsize=15)\n",
+    "plt.text(.5, 0.502, 'Coarse-grained LEM CRH - NWP CRH, grid-box homogeneous clouds', transform=fig.transFigure, horizontalalignment='center',fontsize=15)\n",
+    "plt.text(.5, 0.302, 'Coarse-grained LEM CRH - NWP CRH, homogeneous clouds with fractional cloud cover ', transform=fig.transFigure, horizontalalignment='center',fontsize=15)\n",
+    "\n",
+    "plt.savefig('sfigure2.png', bbox_inches = 'tight',dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bf9d53b5-d026-47cf-a4ed-a9afe1cb6d5f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pycrh",
+   "language": "python",
+   "name": "pycrh"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/plots4paper/figure11.pdf b/plots4paper/figure11.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..fa5f1a1471e82fc2638f1f25fced266d2c0238f7
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diff --git a/plots4paper/figure12.ipynb b/plots4paper/figure12.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..d28c6a4c81bb4540827dad869eb54ca67aea4dc0
--- /dev/null
+++ b/plots4paper/figure12.ipynb
@@ -0,0 +1,376 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "115f93b6-8a4f-4f98-a0dc-2c79cceb1a7a",
+   "metadata": {},
+   "source": [
+    "# Figure 12\n",
+    "\n",
+    "**Figure 12.** Net CRH uncertainties as a function of horizontal scale from 300 m to approximately 500 km for all LEM domains. For the\n",
+    "uncertainty due to the ice-optical parameterization, the CRH difference between the ice schemes of Fu and the ice scheme of Baum with the general habit mixture is used. Note the different y-axes in the panels.\n",
+    "\n",
+    "---\n",
+    "@ Behrooz Keshtgar, KIT 2024"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c69547d-1bf2-4b27-957a-95e15f48812c",
+   "metadata": {},
+   "source": [
+    "## 1- load python packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "039407e9-ae55-437b-8d36-b67a5aa76fa2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import xarray as xr\n",
+    "import pandas as pd\n",
+    "import colorlegend\n",
+    "from matplotlib.ticker import (MultipleLocator, AutoMinorLocator)\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bf9aea5-6e6e-46c9-925d-ce53a9042b21",
+   "metadata": {},
+   "source": [
+    "For reference, print package versions to screen:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "090549dc-8a05-4a15-a43a-2bef20f073d4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "xarrary:    0.16.0\n",
+      "numpy:      1.23.5\n",
+      "matplotlib: 3.3.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('xarrary:   ', xr.__version__)\n",
+    "print('numpy:     ', np.__version__)\n",
+    "import matplotlib; print('matplotlib:', matplotlib.__version__); del matplotlib"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3048f403-40bd-49af-9ab4-96793473069a",
+   "metadata": {},
+   "source": [
+    "## 2- Loading datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c8763751-2796-4396-8a83-f9e23adb0266",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Vertical profiles of MAD of CRH from different offline radiation calculation at different resolution for each LEM domain\n",
+    "ds_out = xr.open_dataset('/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/postprocessed_data_for_publication/for_publication/figure11.nc')\n",
+    "# make a copy\n",
+    "ds = ds_out.drop_vars(['res1','res2','height','rho_mean_icon_dom01','rho_mean_icon_dom02','rho_mean_icon_dom03','rho_mean_icon_dom04'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "872417af-0e22-4887-8302-df6e590da9bb",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## 3- Calculating weighted vertical mean of mean absolute differences (MAD)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e8a7f11d-26e0-45d2-8c31-232282b3fa49",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def calc_weight(rho):\n",
+    "    dp = np.zeros((140))\n",
+    "    dp[1:-1] = (rho[2:] - rho[:-2]) \n",
+    "    dp[0] = (rho[1] - rho[0]) \n",
+    "    dp[-1] = (rho[-1] - rho[-2])\n",
+    "    dp = dp[::-1]\n",
+    "    return dp\n",
+    "    \n",
+    "var_lists = {}\n",
+    "for var in ds.variables:\n",
+    "    if var.startswith('dom01'):\n",
+    "        var_lists[var] = []\n",
+    "        # thickness for weight vertical mean\n",
+    "        dp = calc_weight(ds_out['rho_mean_icon_dom01'].values)\n",
+    "        tmp_list = []\n",
+    "        for i in range(len(ds[var])):\n",
+    "            tmp = ((ds[var][i][0:28].values * dp[0:28]).sum())/(dp[0:28].sum())\n",
+    "            tmp_list.append(tmp) \n",
+    "        var_lists[var].append(tmp_list)\n",
+    "        \n",
+    "    elif var.startswith('dom02'):\n",
+    "        var_lists[var] = []\n",
+    "        # thickness for weight vertical mean\n",
+    "        dp = calc_weight(ds_out['rho_mean_icon_dom02'].values)\n",
+    "        tmp_list = []\n",
+    "        for i in range(len(ds[var])):\n",
+    "            tmp = ((ds[var][i][0:92].values * dp[0:92]).sum())/(dp[0:92].sum())\n",
+    "            tmp_list.append(tmp) \n",
+    "        var_lists[var].append(tmp_list)\n",
+    "        \n",
+    "    elif var.startswith('dom03'):\n",
+    "        var_lists[var] = []\n",
+    "        # thickness for weight vertical mean\n",
+    "        dp = calc_weight(ds_out['rho_mean_icon_dom03'].values)\n",
+    "        tmp_list = []\n",
+    "        for i in range(len(ds[var])):\n",
+    "            tmp = ((ds[var][i][0:92].values * dp[0:92]).sum())/(dp[0:92].sum())\n",
+    "            tmp_list.append(tmp) \n",
+    "        var_lists[var].append(tmp_list) \n",
+    "        \n",
+    "    elif var.startswith('dom04'):\n",
+    "        var_lists[var] = []\n",
+    "        # thickness for weight vertical mean\n",
+    "        dp = calc_weight(ds_out['rho_mean_icon_dom04'].values)\n",
+    "        tmp_list = []\n",
+    "        for i in range(len(ds[var])):\n",
+    "            tmp = ((ds[var][i][0:92].values * dp[0:92]).sum())/(dp[0:92].sum())\n",
+    "            tmp_list.append(tmp) \n",
+    "        var_lists[var].append(tmp_list)      "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f5a483de-2599-46a4-923f-932309dd5663",
+   "metadata": {},
+   "source": [
+    "## 4- Plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "bf9d53b5-d026-47cf-a4ed-a9afe1cb6d5f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x374.4 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x1 = np.array([0.3, 2.5, 5, 10, 50, 100, 500])\n",
+    "x2 = np.array([2.5, 5, 10, 50, 100, 500])\n",
+    "\n",
+    "x_ticks = np.array([0.3, 2.5, 10, 100, 500])\n",
+    "x_int = ['0.3','2.5','10','100','500']\n",
+    "\n",
+    "# plot\n",
+    "fig, axes = plt.subplots(nrows=1, ncols=4, figsize=(20, 5.2))\n",
+    "\n",
+    "i = 0\n",
+    "\n",
+    "for ax in axes.flat:\n",
+    "    ax.tick_params(labelsize=14)\n",
+    "    ax.spines['top'].set_color('none')\n",
+    "    ax.spines['right'].set_color('none')\n",
+    "    \n",
+    "    if i == 0:\n",
+    "\n",
+    "        # Set x-axis to logarithmic scale\n",
+    "        ax.set_xscale('log')\n",
+    "\n",
+    "        ax.plot(x1, var_lists['dom01_3d_nt_error'][0],color='#000075',lw=3,label='3D effects')\n",
+    "        #ax.plot(x1, var_lists['dom01_3d_lw_error'][0],color='#000075',lw=2,ls='dashed')\n",
+    "        #ax.plot(x1, var_lists['dom01_3d_sw_error'][0],color='#000075',lw=2,ls='dotted')\n",
+    "        \n",
+    "        ax.plot(x2, var_lists['dom01_vo_nt_error'][0],color='#469990',lw=3,label='Horizontal heterogeneity\\nand vertical overlap')\n",
+    "        #ax.plot(x2, var_lists['dom01_vo_lw_error'][0],color='#469990',lw=2,ls='dashed')\n",
+    "        #ax.plot(x2, var_lists['dom01_vo_sw_error'][0],color='#469990',lw=2,ls='dotted')\n",
+    "        \n",
+    "        ax.set_ylabel('CRH uncertainty (K day$^{-1}$)', fontsize=14)#,labelpad=10)\n",
+    "        ax.set_xlabel('Horizontal scale (km)', fontsize=14)#,labelpad=10)\n",
+    "        \n",
+    "        ax.set_title('Shallow cumulus', fontsize=14)#,labelpad=10)\n",
+    "        ax.text(0.01, 1.03, '(a)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "        ax.spines['left'].set_bounds(0,2.5)\n",
+    "        ax.set_yticks(np.linspace(0,2.5,6))\n",
+    "        ax.set_ylim([0,2.5])\n",
+    "        \n",
+    "        ax.set_xticks(x_ticks)\n",
+    "        ax.set_xticklabels(x_int)\n",
+    "        ax.set_xlim([0.25,500])\n",
+    "        ax.spines['bottom'].set_bounds(0.3,500)\n",
+    "\n",
+    "    if i == 1:\n",
+    "\n",
+    "        # Set x-axis to logarithmic scale\n",
+    "        ax.set_xscale('log')\n",
+    "\n",
+    "        ax.plot(x1, var_lists['dom02_3d_nt_error'][0],color='#000075',lw=3,label='3D effects')\n",
+    "        #ax.plot(x1, var_lists['dom02_3d_lw_error'][0],color='#000075',lw=2,ls='dashed')\n",
+    "        #ax.plot(x1, var_lists['dom02_3d_sw_error'][0],color='#000075',lw=2,ls='dotted')\n",
+    "        \n",
+    "        ax.plot(x2, var_lists['dom02_vo_nt_error'][0],color='#469990',lw=3,label='Horizontal heterogeneity\\nand vertical overlap')\n",
+    "        #ax.plot(x2, var_lists['dom02_vo_lw_error'][0],color='#469990',lw=2,ls='dashed')\n",
+    "        #ax.plot(x2, var_lists['dom02_vo_sw_error'][0],color='#469990',lw=2,ls='dotted')\n",
+    "        \n",
+    "        ax.plot(x1, var_lists['dom02_ic_nt_error'][0],color='#e6194B',lw=3,label='Ice-optical parameterization')\n",
+    "        #ax.plot(x1, var_lists['dom02_ic_lw_error'][0],color='#e6194B',lw=2,ls='dashed')\n",
+    "        #ax.plot(x1, var_lists['dom02_ic_sw_error'][0],color='#e6194B',lw=2,ls='dotted')\n",
+    "        \n",
+    "        ax.set_xlabel('Horizontal scale (km)', fontsize=14)\n",
+    "        \n",
+    "        ax.set_title('WCB ascent', fontsize=14)\n",
+    "        ax.text(0.01, 1.03, '(b)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "        ax.spines['left'].set_bounds(0,0.75)\n",
+    "        ax.set_yticks(np.linspace(0,0.75,6))\n",
+    "        ax.set_ylim([0,0.75])    \n",
+    "        \n",
+    "        ax.set_xticks(x_ticks)\n",
+    "        ax.set_xticklabels(x_int)\n",
+    "        ax.set_xlim([0.25,500])\n",
+    "        ax.spines['bottom'].set_bounds(0.3,500)\n",
+    "        \n",
+    "    if i == 2:\n",
+    "        # Create an empty list to store the max values\n",
+    "        \n",
+    "        ax.plot(x1, var_lists['dom03_3d_nt_error'][0],color='#000075',lw=3,label='3D effects')\n",
+    "        #ax.plot(x1, var_lists['dom03_3d_lw_error'][0],color='#000075',lw=2,ls='dashed')\n",
+    "        #ax.plot(x1, var_lists['dom03_3d_sw_error'][0],color='#000075',lw=2,ls='dotted')\n",
+    "        \n",
+    "        ax.plot(x2, var_lists['dom03_vo_nt_error'][0],color='#469990',lw=3,label='Cloud horizontal heterogeneity,\\nwith overlap assumption')\n",
+    "        #ax.plot(x2, var_lists['dom03_vo_lw_error'][0],color='#469990',lw=2,ls='dashed')\n",
+    "        #ax.plot(x2, var_lists['dom03_vo_sw_error'][0],color='#469990',lw=2,ls='dotted')\n",
+    "        \n",
+    "        ax.plot(x1, var_lists['dom03_ic_nt_error'][0],color='#e6194B',lw=3,label='Ice-optical parameterization')\n",
+    "        #ax.plot(x1, var_lists['dom03_ic_lw_error'][0],color='#e6194B',lw=2,ls='dashed')\n",
+    "        #ax.plot(x1, var_lists['dom03_ic_sw_error'][0],color='#e6194B',lw=2,ls='dotted')\n",
+    "\n",
+    "        ax.set_xscale('log')\n",
+    "        #ax.set_ylabel('CRH uncertainty (K day$^{-1}$)', fontsize=14)#,labelpad=10)\n",
+    "        ax.set_xlabel('Horizontal scale (km)', fontsize=14)#,labelpad=10)\n",
+    "        ax.set_title('WCB cyclonic outflow', fontsize=14)#,labelpad=10)\n",
+    "        ax.text(0.01, 1.03, '(c)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "        ax.spines['left'].set_bounds(0,0.5)\n",
+    "        ax.set_yticks(np.linspace(0,0.5,6))\n",
+    "        ax.set_ylim([0,0.5])\n",
+    "        \n",
+    "        ax.set_xticks(x_ticks)\n",
+    "        ax.set_xticklabels(x_int)\n",
+    "        ax.set_xlim([0.25,500])\n",
+    "        ax.spines['bottom'].set_bounds(0.3,500)\n",
+    "        \n",
+    "        lg=colorlegend.color_legend(ax,loc=1,fsize=11)   \n",
+    "        \n",
+    "    if i == 3:\n",
+    "        \n",
+    "        ax.plot(x1, var_lists['dom04_3d_nt_error'][0],color='#000075',lw=3,label='3D effects')\n",
+    "        #ax.plot(x1, var_lists['dom04_3d_lw_error'][0],color='#000075',lw=2,ls='dashed')\n",
+    "        #ax.plot(x1, var_lists['dom04_3d_sw_error'][0],color='#000075',lw=2,ls='dotted')\n",
+    "        \n",
+    "        ax.plot(x2, var_lists['dom04_vo_nt_error'][0],color='#469990',lw=3,label='Horizontal heterogeneity\\nand vertical overlap')\n",
+    "        #ax.plot(x2, var_lists['dom04_vo_lw_error'][0],color='#469990',lw=2,ls='dashed')\n",
+    "        #ax.plot(x2, var_lists['dom04_vo_sw_error'][0],color='#469990',lw=2,ls='dotted')\n",
+    "        \n",
+    "        ax.plot(x1, var_lists['dom04_ic_nt_error'][0],color='#e6194B',lw=3,label='Ice-optical parameterization')\n",
+    "        #ax.plot(x1, var_lists['dom04_ic_lw_error'][0],color='#e6194B',lw=2,ls='dashed')\n",
+    "        #ax.plot(x1, var_lists['dom04_ic_sw_error'][0],color='#e6194B',lw=2,ls='dotted')\n",
+    "        \n",
+    "        ax.set_xscale('log')\n",
+    "        \n",
+    "        #ax.set_ylabel('CRH uncertainty (K day$^{-1}$)', fontsize=14)#,labelpad=10)\n",
+    "        ax.set_xlabel('Horizontal scale (km)', fontsize=14)#,labelpad=10)\n",
+    "        ax.set_title('WCB anticyclonic outflow', fontsize=14)#,labelpad=10)\n",
+    "        ax.text(0.01, 1.03, '(d)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "        ax.spines['left'].set_bounds(0,0.25)\n",
+    "        ax.set_yticks(np.linspace(0,0.25,6))\n",
+    "        ax.set_ylim([0,0.25])\n",
+    "        \n",
+    "        ax.set_xticks(x_ticks)\n",
+    "        ax.set_xticklabels(x_int)\n",
+    "        ax.set_xlim([0.25,500])\n",
+    "        ax.spines['bottom'].set_bounds(0.3,500)\n",
+    "        \n",
+    "        #ax.text(1.2, 0.75, 'solid: Net', color='gray', size=12, va=\"top\")\n",
+    "        #ax.text(1.2, 0.70, 'dashed: Longwave', color='gray', size=12, va=\"top\")\n",
+    "        #ax.text(1.2, 0.65, 'dotted: Shortwave', color='gray', size=12, va=\"top\")\n",
+    "        \n",
+    "        \n",
+    "    i = i + 1\n",
+    "\n",
+    "fig.subplots_adjust(wspace=0.2)\n",
+    "plt.savefig('figure12.pdf', bbox_inches = 'tight')\n",
+    "plt.savefig('figure12.png', bbox_inches = 'tight',dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "84d19413-7be2-4789-bb3a-fddab174ade0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pycrh",
+   "language": "python",
+   "name": "pycrh"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/plots4paper/figure12.pdf b/plots4paper/figure12.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..16d710981ead48cd06093cb67869cab3cff371a8
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diff --git a/plots4paper/figure2.ipynb b/plots4paper/figure2.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..f45185919cd75b6fc81bf54805352b155367a719
--- /dev/null
+++ b/plots4paper/figure2.ipynb
@@ -0,0 +1,591 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "fd7e7d31-662b-4c80-9cf1-74565756f22e",
+   "metadata": {},
+   "source": [
+    "# Figure 2\n",
+    "\n",
+    "**Figure 2.** Profiles of domain-averaged cloud hydrometeor contents and total cloud fraction, decomposed into the contributions from liquid, ice, and mixed-phase clouds for all four LEM domains. Thin lines show profiles for nine snapshots between domain local hours 12:30 to 16:30. The thick lines show time-averaged profiles. The threshold used to determine cloudy boxes for both ice and liquid water contents is 10−8 kg kg−1 (Costa-Surós et al., 2020). Note the different y-axes for panels (a) and (b).\n",
+    "\n",
+    "---\n",
+    "@ Behrooz Keshtgar, KIT 2024"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c9d6b860-5669-460a-85e3-9b4cc7fd6ba0",
+   "metadata": {},
+   "source": [
+    "## 1- load python packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "bc71da64-aaec-4035-8689-3f8be0728fbc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import xarray as xr\n",
+    "import pandas as pd\n",
+    "import matplotlib as mpl\n",
+    "import colorlegend\n",
+    "from matplotlib.ticker import (MultipleLocator, AutoMinorLocator)\n",
+    "import matplotlib.colors as mcolors\n",
+    "from matplotlib import cm\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04dd62ca-cf4b-42a2-85ef-2469f19bf6fc",
+   "metadata": {},
+   "source": [
+    "For reference, print package versions to screen:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "41f5bd93-ad99-44d4-ade6-2d7684bf34b6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "xarrary:    0.16.0\n",
+      "numpy:      1.23.5\n",
+      "matplotlib: 3.3.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('xarrary:   ', xr.__version__)\n",
+    "print('numpy:     ', np.__version__)\n",
+    "import matplotlib; print('matplotlib:', matplotlib.__version__); del matplotlib"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4abf13cf-75c8-434d-a32c-c216c0d4cf80",
+   "metadata": {},
+   "source": [
+    "**Since datasets are large, I use DASK to speed up my analysis**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "236bf46a-2a26-41b0-982f-2e0a580b4fa1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table style=\"border: 2px solid white;\">\n",
+       "<tr>\n",
+       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
+       "<h3 style=\"text-align: left;\">Client</h3>\n",
+       "<ul style=\"text-align: left; list-style: none; margin: 0; padding: 0;\">\n",
+       "  <li><b>Scheduler: </b>tcp://127.0.0.1:34619</li>\n",
+       "  <li><b>Dashboard: </b><a href='/user/b381185/levante-spawner-preset//proxy/8787/status' target='_blank'>/user/b381185/levante-spawner-preset//proxy/8787/status</a></li>\n",
+       "</ul>\n",
+       "</td>\n",
+       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
+       "<h3 style=\"text-align: left;\">Cluster</h3>\n",
+       "<ul style=\"text-align: left; list-style:none; margin: 0; padding: 0;\">\n",
+       "  <li><b>Workers: </b>16</li>\n",
+       "  <li><b>Cores: </b>256</li>\n",
+       "  <li><b>Memory: </b>252.72 GB</li>\n",
+       "</ul>\n",
+       "</td>\n",
+       "</tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Client: 'tcp://127.0.0.1:34619' processes=16 threads=256, memory=252.72 GB>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import dask\n",
+    "from dask.distributed import Client, progress, wait\n",
+    "dask.config.config.get('distributed').get('dashboard').update({'link':'{JUPYTERHUB_SERVICE_PREFIX}/proxy/{port}/status'})\n",
+    "client = Client()\n",
+    "client"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9af0ecad-9a06-4b17-9c7f-948c2e9180aa",
+   "metadata": {},
+   "source": [
+    "## 2- Loading datasets "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "db4e1350-0029-4443-acd4-973b225f8fb3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    " # Dictionary for loading datasets for the 4 simulations\n",
+    "simdict = {\n",
+    "         'ICON_LEM_DOM01_lon25_lat40_300m' : {'name':'Shallow cumulus'          ,'res':'300m', 'radiation':4, 'mphy':4}, # Only cloud radiation\n",
+    "         'ICON_LEM_DOM02_lon40_lat44_300m' : {'name':'WCB ascent'               ,'res':'300m', 'radiation':4, 'mphy':4}, # Only cloud radiation\n",
+    "         'ICON_LEM_DOM03_lon30_lat53_300m' : {'name':'WCB cyclonic outflow'     ,'res':'300m', 'radiation':4, 'mphy':4}, # Only cloud radiation\n",
+    "         'ICON_LEM_DOM04_lon50_lat48_300m' : {'name':'WCB anticyclonic outflow' ,'res':'300m', 'radiation':4, 'mphy':4}  # Only cloud radiation\n",
+    "          }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "f36e1364-bdc7-4a52-8430-a105fabba3b4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Working on loading data for ICON_LEM_DOM01_lon25_lat40_300m\n",
+      "Working on loading data for ICON_LEM_DOM02_lon40_lat44_300m\n",
+      "Working on loading data for ICON_LEM_DOM03_lon30_lat53_300m\n",
+      "Working on loading data for ICON_LEM_DOM04_lon50_lat48_300m\n"
+     ]
+    }
+   ],
+   "source": [
+    "# adjusting time steps\n",
+    "rng = pd.date_range('2022-01-05-06', periods=25, freq=\"30 min\")\n",
+    "df = pd.DataFrame({ 'Date': rng })\n",
+    "t =  df.values[:,0]\n",
+    "\n",
+    "def load_simulations():\n",
+    "    ds_list = []\n",
+    "    for sim in list(simdict.keys()): \n",
+    "        print('Working on loading data for', sim)\n",
+    "        path = '/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/'+sim+'/'\n",
+    "        fname1  = path+\"icon-cld3d*.nc\"\n",
+    "        ds_var1 = xr.open_mfdataset(fname1,combine='by_coords',chunks={'time': 1, 'height': 3},parallel=True)[['tot_qc_dia','tot_qi_dia','clc','reff_qc_ecrad','reff_qi_ecrad']]\n",
+    "        # total cloud condensate\n",
+    "        ds_var1['tot_q_dia'] = ds_var1['tot_qc_dia'] + ds_var1['tot_qi_dia']\n",
+    "        # density \n",
+    "        fname2  = path+\"icon-atm3d*.nc\"\n",
+    "        ds_var2 = xr.open_mfdataset(fname2,combine='by_coords',chunks={'time': 1, 'height': 3},parallel=True)[['rho']]\n",
+    "        ds = xr.merge([ds_var1,ds_var2])\n",
+    "        ds.coords['time'] = t\n",
+    "        ds_list.append(ds)\n",
+    "    return ds_list\n",
+    "#----------------------------------\n",
+    "ds_list = load_simulations()\n",
+    "#----------------------------------\n",
+    "# height values at full-levels\n",
+    "z_ifc = xr.open_dataset('/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/ICON_LEM_DOM02_lon40_lat44_300m/icon-atm3d_ML_20220105T120033Z.nc')[\"z_ifc\"].isel(ncells=20000)\n",
+    "zfull  = ((z_ifc - z_ifc.diff('height_3')/2).values)*1e-3 # km"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "80419359-5a80-4508-8a02-8223e7670f3d",
+   "metadata": {},
+   "source": [
+    "## 3- Average profiles of cloud hydrometeor contents and total cloud fraction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "711e37dc-9925-4851-a760-f932cfc7db1e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "working on simulation: 0\n",
+      "calculating domain averages of LWC and IWC\n",
+      "deriving vertical profiles cloud fractions\n",
+      "working on simulation: 1\n",
+      "calculating domain averages of LWC and IWC\n",
+      "deriving vertical profiles cloud fractions\n",
+      "working on simulation: 2\n",
+      "calculating domain averages of LWC and IWC\n",
+      "deriving vertical profiles cloud fractions\n",
+      "working on simulation: 3\n",
+      "calculating domain averages of LWC and IWC\n",
+      "deriving vertical profiles cloud fractions\n"
+     ]
+    }
+   ],
+   "source": [
+    "# deriving required variables\n",
+    "d_list = []\n",
+    "for i in range(len(ds_list)):\n",
+    "    print('working on simulation:',i)\n",
+    "    # selecting time steps, and excluding LTBC boundary data points\n",
+    "    ds = ds_list[i].sel(time=slice('2022-01-05T10:00:00.000000000','2022-01-05T14:00:00.000000000')).isel(ncells=slice(55000,3489474))\n",
+    "\n",
+    "    # liquid water/ ice content\n",
+    "    ds['LWC'] = (1000*(ds['tot_qc_dia'] * ds['rho'])) # g/m3\n",
+    "    ds['IWC'] = (1000*(ds['tot_qi_dia'] * ds['rho'])) # g/m3\n",
+    "    ds['CLD'] = (1000*(ds['tot_q_dia'] * ds['rho']))  # g/m3\n",
+    "\n",
+    "    # applying the threshold\n",
+    "    thres = 1e-8 # kg/kg\n",
+    "    print('calculating domain averages of LWC and IWC')\n",
+    "    \n",
+    "    lwc_mean = ds['LWC'].where(ds['tot_qc_dia'] > thres).fillna(0.0).mean(dim=['ncells']).compute()\n",
+    "    iwc_mean = ds['IWC'].where(ds['tot_qi_dia'] > thres).fillna(0.0).mean(dim=['ncells']).compute()\n",
+    "  \n",
+    "    # for cloud fraction \n",
+    "    # empty data arrays\n",
+    "    clci = np.zeros((ds.time.size,ds.height.size))\n",
+    "    clcw = np.zeros((ds.time.size,ds.height.size))\n",
+    "    clcm = np.zeros((ds.time.size,ds.height.size))\n",
+    "    clct = np.zeros((ds.time.size,ds.height.size))\n",
+    "    \n",
+    "    print('deriving vertical profiles cloud fractions')\n",
+    "    \n",
+    "    clct = ds['tot_q_dia'].where(ds['tot_q_dia'] > thres).count(dim=['ncells']).values/len(ds.ncells)\n",
+    "    # mixed-phase cloud fraction\n",
+    "    clcm = ds['tot_q_dia'].where((ds['tot_qc_dia'] > thres)&(ds['tot_qi_dia'] > thres)).count(dim=['ncells']).values/len(ds.ncells)\n",
+    "    # pure ice cloud fraction\n",
+    "    clci = ds['tot_q_dia'].where((ds['tot_qi_dia'] > thres)&(ds['tot_qc_dia'] < thres)).count(dim=['ncells']).values/len(ds.ncells)\n",
+    "    # pure water cloud fraction\n",
+    "    clcw = ds['tot_q_dia'].where((ds['tot_qc_dia'] > thres)&(ds['tot_qi_dia'] < thres)).count(dim=['ncells']).values/len(ds.ncells)\n",
+    "    \n",
+    "    list_var = [lwc_mean,iwc_mean,clci,clcw,clcm,clct]\n",
+    "    d_list.append(list_var)\n",
+    "    \n",
+    "    del lwc_mean,iwc_mean,clci,clcw,clcm,clct,ds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e551dbbc-081c-45cb-8c1d-44e5e64e5394",
+   "metadata": {},
+   "source": [
+    "### For data publication"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "90668a33-5186-4b7c-a88c-84ff8c46787b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# creating a dataset and save for data publication\n",
+    "ds_out = xr.Dataset(\n",
+    "    data_vars={\n",
+    "        \"lwc_mean_dom01\"  : (d_list[0][0].dims, d_list[0][0].data),\n",
+    "        \"iwc_mean_dom01\"  : (d_list[0][0].dims, d_list[0][1].data),\n",
+    "        \"clci_frc_dom01\"  : (d_list[0][0].dims, d_list[0][2]),\n",
+    "        \"clcw_frc_dom01\"  : (d_list[0][0].dims, d_list[0][3]),\n",
+    "        \"clcm_frc_dom01\"  : (d_list[0][0].dims, d_list[0][4]),\n",
+    "        \"clct_frc_dom01\"  : (d_list[0][0].dims, d_list[0][5]),\n",
+    "        \n",
+    "        \"lwc_mean_dom02\"  : (d_list[0][0].dims, d_list[1][0].data),\n",
+    "        \"iwc_mean_dom02\"  : (d_list[0][0].dims, d_list[1][1].data),\n",
+    "        \"clci_frc_dom02\"  : (d_list[0][0].dims, d_list[1][2]),\n",
+    "        \"clcw_frc_dom02\"  : (d_list[0][0].dims, d_list[1][3]),\n",
+    "        \"clcm_frc_dom02\"  : (d_list[0][0].dims, d_list[1][4]),\n",
+    "        \"clct_frc_dom02\"  : (d_list[0][0].dims, d_list[1][5]),\n",
+    "        \n",
+    "        \"lwc_mean_dom03\"  : (d_list[0][0].dims, d_list[2][0].data),\n",
+    "        \"iwc_mean_dom03\"  : (d_list[0][0].dims, d_list[2][1].data),\n",
+    "        \"clci_frc_dom03\"  : (d_list[0][0].dims, d_list[2][2]),\n",
+    "        \"clcw_frc_dom03\"  : (d_list[0][0].dims, d_list[2][3]),\n",
+    "        \"clcm_frc_dom03\"  : (d_list[0][0].dims, d_list[2][4]),\n",
+    "        \"clct_frc_dom03\"  : (d_list[0][0].dims, d_list[2][5]),\n",
+    "        \n",
+    "        \"lwc_mean_dom04\"  : (d_list[0][0].dims, d_list[3][0].data),\n",
+    "        \"iwc_mean_dom04\"  : (d_list[0][0].dims, d_list[3][1].data),\n",
+    "        \"clci_frc_dom04\"  : (d_list[0][0].dims, d_list[3][2]),\n",
+    "        \"clcw_frc_dom04\"  : (d_list[0][0].dims, d_list[3][3]),\n",
+    "        \"clcm_frc_dom04\"  : (d_list[0][0].dims, d_list[3][4]),\n",
+    "        \"clct_frc_dom04\"  : (d_list[0][0].dims, d_list[3][5]),\n",
+    "        \n",
+    "    },\n",
+    "    coords=d_list[0][0].coords)\n",
+    "ds_out = ds_out.assign(z_mc=zfull)\n",
+    "\n",
+    "ds_out.attrs['description'] = 'Vertical profiles of cloud water, ice mass content, and cloud fractions for each LEM domain'\n",
+    "ds_out.to_netcdf('/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/postprocessed_data_for_publication/for_publication/figure2.nc')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "15ac3190-be4f-4d5b-850d-03f7811084fe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ds_out = xr.open_dataset('/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/postprocessed_data_for_publication/for_publication/figure2.nc')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "9bd5590d-3342-4cbc-bed9-a1d76055ccab",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 8 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(nrows=2, ncols=4, figsize=(20, 10))\n",
+    "\n",
+    "i = 0\n",
+    "\n",
+    "for ax in axes.flat:\n",
+    "    \n",
+    "    ax.tick_params(labelsize=14)\n",
+    "    ax.spines['top'].set_color('none')\n",
+    "    ax.spines['right'].set_color('none')\n",
+    "    \n",
+    "    if i in [4,5,6,7]:\n",
+    "    \n",
+    "        ax.spines['bottom'].set_bounds(0,100)\n",
+    "        ax.set_xticks(np.arange(0,120,20))\n",
+    "        ax.set_xlim([-5,100])\n",
+    "        \n",
+    "    if i in [0,1,2,3]:    \n",
+    "        ax.spines['bottom'].set_bounds(0,1.5*1e-1)\n",
+    "        ax.set_xticks(np.arange(0,2,0.5)*1e-1)\n",
+    "        ax.set_xlim([-0.08*1e-1,1.5*1e-1])\n",
+    "        \n",
+    "    #--------------------------------------------------------------------------------------    \n",
+    "    if i == 0:\n",
+    "\n",
+    "        for t in range(9):\n",
+    "            ax.plot(ds_out['lwc_mean_dom01'][t],zfull,color='#4363d8',lw=1,alpha=0.5)\n",
+    "            ax.plot(ds_out['iwc_mean_dom01'][t],zfull,color='#e6194B',lw=1,alpha=0.5)\n",
+    "\n",
+    "        ax.plot(ds_out['lwc_mean_dom01'].mean('time'),zfull,color='#4363d8',label='Liquid',lw=3)\n",
+    "        ax.plot(ds_out['iwc_mean_dom01'].mean('time'),zfull,color='#e6194B',label='Ice',lw=3)\n",
+    "        \n",
+    "        lg=colorlegend.color_legend(ax,loc=1,fsize=14)\n",
+    "        ax.set_ylabel('Height (km)', fontsize=14)\n",
+    "        ax.set_xlabel('Ice/liquid water content (g m$^{-3}$)', fontsize=14)\n",
+    "        ax.text(0.0, 1.03, '(a)', transform=ax.transAxes, \n",
+    "            size=15 )\n",
+    "        ax.set_title('Shallow cumulus', fontsize=15,pad=15)\n",
+    "\n",
+    "        ax.set_ylim([0,3])\n",
+    "\n",
+    "    if i == 1:\n",
+    "\n",
+    "        for t in range(9):\n",
+    "            ax.plot(ds_out['lwc_mean_dom02'][t],zfull,color='#4363d8',lw=1,alpha=0.5)\n",
+    "            ax.plot(ds_out['iwc_mean_dom02'][t],zfull,color='#e6194B',lw=1,alpha=0.5)\n",
+    "\n",
+    "        ax.plot(ds_out['lwc_mean_dom02'].mean('time'),zfull,color='#4363d8',label='Liquid',lw=3)\n",
+    "        ax.plot(ds_out['iwc_mean_dom02'].mean('time'),zfull,color='#e6194B',label='Ice',lw=3)\n",
+    "        \n",
+    "        #ax.set_yticklabels([])\n",
+    "        ax.set_xlabel('Ice/liquid water content (g m$^{-3}$)', fontsize=14)\n",
+    "        ax.text(0.0, 1.03, '(c)', transform=ax.transAxes, \n",
+    "            size=15 )\n",
+    "        ax.set_title('WCB ascent', fontsize=15,pad=15)\n",
+    "        \n",
+    "        ax.spines['left'].set_bounds(0,12)\n",
+    "        ax.set_yticks(np.arange(0,14,2))\n",
+    "        ax.set_ylim([0,12])\n",
+    "\n",
+    "    if i == 2:\n",
+    "        \n",
+    "        for t in range(9):\n",
+    "            ax.plot(ds_out['lwc_mean_dom03'][t],zfull,color='#4363d8',lw=1,alpha=0.5)\n",
+    "            ax.plot(ds_out['iwc_mean_dom03'][t],zfull,color='#e6194B',lw=1,alpha=0.5)\n",
+    "\n",
+    "        ax.plot(ds_out['lwc_mean_dom03'].mean('time'),zfull,color='#4363d8',label='Liquid',lw=3)\n",
+    "        ax.plot(ds_out['iwc_mean_dom03'].mean('time'),zfull,color='#e6194B',label='Ice',lw=3)\n",
+    "        \n",
+    "        ax.set_yticklabels([])\n",
+    "        ax.set_xlabel('Ice/liquid water content (g m$^{-3}$)', fontsize=14)\n",
+    "        ax.text(0.0, 1.03, '(e)', transform=ax.transAxes, \n",
+    "            size=15 )\n",
+    "        ax.set_title('WCB cyclonic outflow', fontsize=15,pad=15)\n",
+    "        \n",
+    "        ax.spines['left'].set_bounds(0,12)\n",
+    "        ax.set_yticks(np.arange(0,14,2))\n",
+    "        ax.set_ylim([0,12])\n",
+    "\n",
+    "    if i == 3:\n",
+    "        \n",
+    "        for t in range(9):\n",
+    "            ax.plot(ds_out['lwc_mean_dom04'][t],zfull,color='#4363d8',lw=1,alpha=0.5)\n",
+    "            ax.plot(ds_out['iwc_mean_dom04'][t],zfull,color='#e6194B',lw=1,alpha=0.5)\n",
+    "\n",
+    "        ax.plot(ds_out['lwc_mean_dom04'].mean('time'),zfull,color='#4363d8',label='Liquid',lw=3)\n",
+    "        ax.plot(ds_out['iwc_mean_dom04'].mean('time'),zfull,color='#e6194B',label='Ice',lw=3)\n",
+    "        \n",
+    "        ax.set_yticklabels([])\n",
+    "        ax.set_xlabel('Ice/liquid water water content (g m$^{-3}$)', fontsize=14)\n",
+    "        ax.text(0.0, 1.03, '(g)', transform=ax.transAxes, \n",
+    "            size=15 )\n",
+    "        ax.set_title('WCB anticyclonic outflow', fontsize=15,pad=15)\n",
+    "        \n",
+    "        ax.spines['left'].set_bounds(0,12)\n",
+    "        ax.set_yticks(np.arange(0,14,2))\n",
+    "        ax.set_ylim([0,12])\n",
+    "        \n",
+    "    #--------------------------------------------------------------------------------------------------------------\n",
+    "    if i == 4:\n",
+    "        \n",
+    "        for t in range(9):\n",
+    "            ax.plot(ds_out['clct_frc_dom01'][t]*1e2,zfull,color='#000000',lw=1,alpha=0.5)\n",
+    "         \n",
+    "        ax.plot(ds_out['clcw_frc_dom01'].mean('time')*1e2,zfull,color='#4363d8',label='Liquid',lw=3)    \n",
+    "        ax.plot(ds_out['clci_frc_dom01'].mean('time')*1e2,zfull,color='#e6194B',label='Ice',lw=3)    \n",
+    "        ax.plot(ds_out['clcm_frc_dom01'].mean('time')*1e2,zfull,color='#f58231',label='Mixed-phase',lw=3)\n",
+    "        ax.plot(ds_out['clct_frc_dom01'].mean('time')*1e2,zfull,color='#000000',label='Total',lw=1.5)   \n",
+    "        \n",
+    "        lg=colorlegend.color_legend(ax,loc=1,fsize=14)\n",
+    "        ax.set_ylabel('Height (Km)', fontsize=14)\n",
+    "        ax.set_xlabel('Cloud fraction (%)', fontsize=14)\n",
+    "        ax.text(0.0, 1.03, '(b)', transform=ax.transAxes, \n",
+    "            size=15 )\n",
+    "        \n",
+    "        ax.set_ylim([0,3])\n",
+    "        \n",
+    "        \n",
+    "    if i == 5:\n",
+    "        \n",
+    "        for t in range(9):\n",
+    "            ax.plot(ds_out['clct_frc_dom02'][t]*1e2,zfull,color='#000000',lw=1,alpha=0.5)\n",
+    "         \n",
+    "        ax.plot(ds_out['clcw_frc_dom02'].mean('time')*1e2,zfull,color='#4363d8',label='Liquid',lw=3)    \n",
+    "        ax.plot(ds_out['clci_frc_dom02'].mean('time')*1e2,zfull,color='#e6194B',label='Ice',lw=3)    \n",
+    "        ax.plot(ds_out['clcm_frc_dom02'].mean('time')*1e2,zfull,color='#f58231',label='Mixed-phase',lw=3)\n",
+    "        ax.plot(ds_out['clct_frc_dom02'].mean('time')*1e2,zfull,color='#000000',label='Total',lw=1.5)\n",
+    "        \n",
+    "        #lg=colorlegend.color_legend(ax,loc=1,fsize=14)\n",
+    "        #ax.set_yticklabels([])\n",
+    "        ax.set_xlabel('Cloud fraction (%)', fontsize=14)\n",
+    "        ax.text(0.0, 1.03, '(d)', transform=ax.transAxes, \n",
+    "            size=15 )\n",
+    "        \n",
+    "        ax.spines['left'].set_bounds(0,12)\n",
+    "        ax.set_yticks(np.arange(0,14,2))\n",
+    "        ax.set_ylim([0,12])\n",
+    "        \n",
+    "    if i == 6:\n",
+    "        \n",
+    "        for t in range(9):\n",
+    "            ax.plot(ds_out['clct_frc_dom03'][t]*1e2,zfull,color='#000000',lw=1,alpha=0.5)\n",
+    "         \n",
+    "        ax.plot(ds_out['clcw_frc_dom03'].mean('time')*1e2,zfull,color='#4363d8',label='Liquid',lw=3)    \n",
+    "        ax.plot(ds_out['clci_frc_dom03'].mean('time')*1e2,zfull,color='#e6194B',label='Ice',lw=3)    \n",
+    "        ax.plot(ds_out['clcm_frc_dom03'].mean('time')*1e2,zfull,color='#f58231',label='Mixed-phase',lw=3)\n",
+    "        ax.plot(ds_out['clct_frc_dom03'].mean('time')*1e2,zfull,color='#000000',label='Total',lw=1.5)\n",
+    "        \n",
+    "        \n",
+    "        #lg=colorlegend.color_legend(ax,loc=1,fsize=14)\n",
+    "        ax.set_yticklabels([])\n",
+    "        ax.set_xlabel('Cloud fraction (%)', fontsize=14)\n",
+    "        ax.text(0.0, 1.03, '(f)', transform=ax.transAxes, \n",
+    "            size=15 )\n",
+    "        \n",
+    "        ax.spines['left'].set_bounds(0,12)\n",
+    "        ax.set_yticks(np.arange(0,14,2))\n",
+    "        ax.set_ylim([0,12])\n",
+    "        \n",
+    "    if i == 7:\n",
+    "        \n",
+    "        for t in range(9):\n",
+    "            ax.plot(ds_out['clct_frc_dom04'][t]*1e2,zfull,color='#000000',lw=1,alpha=0.5)\n",
+    "         \n",
+    "        ax.plot(ds_out['clcw_frc_dom04'].mean('time')*1e2,zfull,color='#4363d8',label='Liquid',lw=3)    \n",
+    "        ax.plot(ds_out['clci_frc_dom04'].mean('time')*1e2,zfull,color='#e6194B',label='Ice',lw=3)    \n",
+    "        ax.plot(ds_out['clcm_frc_dom04'].mean('time')*1e2,zfull,color='#f58231',label='Mixed-phase',lw=3)\n",
+    "        ax.plot(ds_out['clct_frc_dom04'].mean('time')*1e2,zfull,color='#000000',label='Total',lw=1.5)\n",
+    "        \n",
+    "        \n",
+    "        #lg=colorlegend.color_legend(ax,loc=1,fsize=14)\n",
+    "        ax.set_yticklabels([])\n",
+    "        ax.set_xlabel('Cloud fraction (%)', fontsize=14)\n",
+    "        ax.text(0.0, 1.03, '(h)', transform=ax.transAxes, \n",
+    "            size=15 )\n",
+    "        \n",
+    "        ax.spines['left'].set_bounds(0,12)\n",
+    "        ax.set_yticks(np.arange(0,14,2))\n",
+    "        ax.set_ylim([0,12])\n",
+    "        \n",
+    "        \n",
+    "    i = i + 1\n",
+    "\n",
+    "fig.subplots_adjust(wspace=0.2,hspace=0.35) \n",
+    "\n",
+    "plt.savefig('figure2.pdf', bbox_inches = 'tight')\n",
+    "plt.savefig('figure2.png', bbox_inches = 'tight',dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "43a00048-9b94-46fa-9980-29c21b28d060",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "client.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "29e6e8e2-b605-433b-9d8b-78476c0a84a9",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pycrh",
+   "language": "python",
+   "name": "pycrh"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/plots4paper/figure2.pdf b/plots4paper/figure2.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..1e910ddae309311d3c81920b4a836e12000790a2
Binary files /dev/null and b/plots4paper/figure2.pdf differ
diff --git a/plots4paper/figure3.ipynb b/plots4paper/figure3.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..ab236f93915f9e1c25784a39920473f340f0ff63
--- /dev/null
+++ b/plots4paper/figure3.ipynb
@@ -0,0 +1,204 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "115f93b6-8a4f-4f98-a0dc-2c79cceb1a7a",
+   "metadata": {},
+   "source": [
+    "# Figure 3\n",
+    "\n",
+    "**Figure 3.**  Illustration of the method to derive homogeneous NWP clouds from LEM clouds. The plot shows a layer of grid boxes. (a) Clouds in the LEM simulation. (b) Homogeneous grid-box cloud at a resolution of 2.5 km. (c) Homogeneous cloud with fractional cloud cover at a resolution of 2.5 km.\n",
+    "\n",
+    "---\n",
+    "@ Behrooz Keshtgar, KIT 2024"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c69547d-1bf2-4b27-957a-95e15f48812c",
+   "metadata": {},
+   "source": [
+    "## 1- load python packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "039407e9-ae55-437b-8d36-b67a5aa76fa2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.cm as cm\n",
+    "from matplotlib.colors import LinearSegmentedColormap, ListedColormap"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bf9aea5-6e6e-46c9-925d-ce53a9042b21",
+   "metadata": {},
+   "source": [
+    "For reference, print package versions to screen:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "090549dc-8a05-4a15-a43a-2bef20f073d4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "xarrary:    0.16.0\n",
+      "numpy:      1.23.5\n",
+      "matplotlib: 3.3.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('xarrary:   ', xr.__version__)\n",
+    "print('numpy:     ', np.__version__)\n",
+    "import matplotlib; print('matplotlib:', matplotlib.__version__); del matplotlib"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "3049f12b-2f60-49d8-9703-cb13196cae5f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Blues = cm.get_cmap('Blues', 256)\n",
+    "Blues_n = Blues(np.linspace(0, 1, 256))\n",
+    "white = np.array([1, 1, 1, 1])\n",
+    "Blues_n[0:1, :] = white\n",
+    "Blues_n = ListedColormap(Blues_n)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "4de0bb84-a98b-485b-8f71-65fa382e0195",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "N = 9\n",
+    "# make an empty data set\n",
+    "data = np.zeros((N, N))\n",
+    "# fill in some fake data\n",
+    "for j in range(4)[::-1]:\n",
+    "    data[N//2 - j : N//2 + j +1, N//2 - j : N//2 + j +1] = -(j-4)\n",
+    "# center    \n",
+    "data[4,4] = 4\n",
+    "\n",
+    "fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(15, 5))\n",
+    "i = 0\n",
+    "\n",
+    "for ax in axes.flat:\n",
+    "    if i == 0:\n",
+    "        ax.pcolor(np.arange(10),np.arange(10),data,cmap=Blues_n,vmin=0,vmax=4)\n",
+    "        for x in range(N + 1):\n",
+    "            ax.axhline(x, lw=1, color='k', zorder=5)\n",
+    "            ax.axvline(x, lw=1, color='k', zorder=5)\n",
+    "        ax.set_xticks([])\n",
+    "        ax.set_yticks([])\n",
+    "        \n",
+    "        ax.set_title('LEM cloud', fontsize=15,pad=15)\n",
+    "        ax.annotate('300 m', xy=(0.055, -0.05), xytext=(0.055, -0.18), xycoords='axes fraction', \n",
+    "            fontsize=14, ha='center', va='bottom',\n",
+    "            bbox=dict(boxstyle=None, fc='white'),\n",
+    "            arrowprops=dict(arrowstyle='-[, widthB=0.9, lengthB=0.5', lw=1.0))\n",
+    "        \n",
+    "        ax.text(0.0, 1.03, '(a)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 1:    \n",
+    "        ax.pcolor(np.arange(10),np.arange(10),data*0+data.mean(),cmap=Blues_n,vmin=0,vmax=4)\n",
+    "        #for x in range(N + 1):\n",
+    "        #    ax.axhline(x, lw=1, color='k', zorder=5)\n",
+    "        #    ax.axvline(x, lw=1, color='k', zorder=5)\n",
+    "        ax.set_xticks([])\n",
+    "        ax.set_yticks([])\n",
+    "        \n",
+    "        ax.set_title('Homogeneous grid-box cloud', fontsize=15,pad=15)\n",
+    "        ax.annotate('≈ 2.5 km', xy=(0.5, -0.05), xytext=(0.5, -0.18), xycoords='axes fraction', \n",
+    "            fontsize=14, ha='center', va='bottom',\n",
+    "            bbox=dict(boxstyle=None, fc='white'),\n",
+    "            arrowprops=dict(arrowstyle='-[, widthB=9.3, lengthB=0.5', lw=1.0))\n",
+    "        \n",
+    "        ax.text(0.0, 1.03, '(b)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 2:      \n",
+    "        cloudy = data[np.nonzero(data)]\n",
+    "        clc = cloudy.mean()\n",
+    "        clct = data*0.0\n",
+    "        clct[:,0:6] = clc\n",
+    "        ax.pcolor(np.arange(10),np.arange(10),clct,cmap=Blues_n,vmin=0,vmax=4)\n",
+    "        #for x in range(N + 1):\n",
+    "        #    ax.axhline(x, lw=1, color='k', zorder=5)\n",
+    "        #    ax.axvline(x, lw=1, color='k', zorder=5)\n",
+    "        ax.set_xticks([])\n",
+    "        ax.set_yticks([])\n",
+    "        \n",
+    "        ax.set_title('Homogeneous cloud with\\nfractional cloud cover', fontsize=15,pad=15)\n",
+    "        ax.annotate('Clear-sky', xy=(0.84, -0.05), xytext=(0.84, -0.18), xycoords='axes fraction', \n",
+    "            fontsize=14, ha='center', va='bottom',\n",
+    "            bbox=dict(boxstyle=None, fc='white'),\n",
+    "            arrowprops=dict(arrowstyle='-[, widthB=3.1, lengthB=0.5', lw=1.0))\n",
+    "        \n",
+    "        ax.text(0.0, 1.03, '(c)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    i = i + 1\n",
+    "fig.subplots_adjust(wspace=0.1)\n",
+    "\n",
+    "plt.savefig('figure3.pdf',bbox_inches = 'tight')\n",
+    "plt.savefig('figure3.png', bbox_inches = 'tight',dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5c449926-ef13-409c-b698-4d5b9400725c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pycrh",
+   "language": "python",
+   "name": "pycrh"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/plots4paper/figure3.pdf b/plots4paper/figure3.pdf
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diff --git a/plots4paper/figure4.ipynb b/plots4paper/figure4.ipynb
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index 0000000000000000000000000000000000000000..ee4cd4212b32808bcdab55799bbf17aef1b191cd
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+++ b/plots4paper/figure4.ipynb
@@ -0,0 +1,515 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "115f93b6-8a4f-4f98-a0dc-2c79cceb1a7a",
+   "metadata": {},
+   "source": [
+    "# Figure 4\n",
+    "\n",
+    "**Figure 4.** Domain and time averages of shortwave, longwave, and net CRH. Profiles are shown for the (a) shallow cumulus, (b) WCB ascent, (c) WCB cyclonic outflow, and (d) WCB anticyclonic outflow regions. The ICON CRH is shown in dashed lines, and the CRH derived from the offline reference 1D radiative transfer calculation is shown in solid lines. Note the different x- and y-axes between panels (a) and (b-d).\n",
+    "\n",
+    "---\n",
+    "@ Behrooz Keshtgar, KIT 2024"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c69547d-1bf2-4b27-957a-95e15f48812c",
+   "metadata": {},
+   "source": [
+    "## 1- load python packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "039407e9-ae55-437b-8d36-b67a5aa76fa2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import xarray as xr\n",
+    "import pandas as pd\n",
+    "import colorlegend\n",
+    "from matplotlib.ticker import (MultipleLocator, AutoMinorLocator)\n",
+    "import matplotlib.colors as mcolors\n",
+    "from matplotlib import cm\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bf9aea5-6e6e-46c9-925d-ce53a9042b21",
+   "metadata": {},
+   "source": [
+    "For reference, print package versions to screen:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "090549dc-8a05-4a15-a43a-2bef20f073d4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "xarrary:    0.16.0\n",
+      "numpy:      1.23.5\n",
+      "matplotlib: 3.3.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('xarrary:   ', xr.__version__)\n",
+    "print('numpy:     ', np.__version__)\n",
+    "import matplotlib; print('matplotlib:', matplotlib.__version__); del matplotlib"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "05c4817c-8fda-4e9f-9f42-8b74b7af5adf",
+   "metadata": {},
+   "source": [
+    "**Since datasets are large, I use DASK to speed up my analysis**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "daa2683a-2fb1-43ba-ab6e-415dce04ee27",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table style=\"border: 2px solid white;\">\n",
+       "<tr>\n",
+       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
+       "<h3 style=\"text-align: left;\">Client</h3>\n",
+       "<ul style=\"text-align: left; list-style: none; margin: 0; padding: 0;\">\n",
+       "  <li><b>Scheduler: </b>tcp://127.0.0.1:39473</li>\n",
+       "  <li><b>Dashboard: </b><a href='/user/b381185/levante-spawner-preset//proxy/8787/status' target='_blank'>/user/b381185/levante-spawner-preset//proxy/8787/status</a></li>\n",
+       "</ul>\n",
+       "</td>\n",
+       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
+       "<h3 style=\"text-align: left;\">Cluster</h3>\n",
+       "<ul style=\"text-align: left; list-style:none; margin: 0; padding: 0;\">\n",
+       "  <li><b>Workers: </b>16</li>\n",
+       "  <li><b>Cores: </b>256</li>\n",
+       "  <li><b>Memory: </b>252.72 GB</li>\n",
+       "</ul>\n",
+       "</td>\n",
+       "</tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Client: 'tcp://127.0.0.1:39473' processes=16 threads=256, memory=252.72 GB>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import dask\n",
+    "from dask.distributed import Client, progress, wait\n",
+    "dask.config.config.get('distributed').get('dashboard').update({'link':'{JUPYTERHUB_SERVICE_PREFIX}/proxy/{port}/status'})\n",
+    "client = Client()\n",
+    "client"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1f71995-2a2b-49a4-9ab3-c56a389a5964",
+   "metadata": {},
+   "source": [
+    "## 2- Loading datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "d8792fad-2a58-45e3-86a9-eac4d7cdeff8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    " # Dictionary for loading datasets for the 4 simulations\n",
+    "domdict = {\n",
+    "         'shallow_cumulus'          : {'res':'300m'}, \n",
+    "         'WCB_ascent'               : {'res':'300m'}, \n",
+    "         'WCB_cyclonic_outflow'     : {'res':'300m'}, \n",
+    "         'WCB_anticyclonic_outflow' : {'res':'300m'}\n",
+    "          }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "254e75ec-03ae-4c40-b179-df701eb323b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Working on loading data for shallow_cumulus\n",
+      "Working on loading data for WCB_ascent\n",
+      "Working on loading data for WCB_cyclonic_outflow\n",
+      "Working on loading data for WCB_anticyclonic_outflow\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Loading icon-pp datasets\n",
+    "def load_data():\n",
+    "    list_icon = []\n",
+    "    for dom in list(domdict.keys()):\n",
+    "        path = '/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/postprocessed_data_for_publication/'\n",
+    "        print('Working on loading data for', dom)\n",
+    "        ds = xr.open_dataset(path+dom+'/icon_pp_data.nc').chunk(chunks={'time': 1, 'height': 10})\n",
+    "        list_icon.append(ds)\n",
+    "    return list_icon\n",
+    "#-------------------------------------------------------------------------------------------------\n",
+    "list_icon=load_data()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "44080844-1f1e-4df7-a462-e7abcd0cd7f6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Working on loading data for shallow_cumulus\n",
+      "Working on loading data for WCB_ascent\n",
+      "Working on loading data for WCB_cyclonic_outflow\n",
+      "Working on loading data for WCB_anticyclonic_outflow\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Loading icon-pp datasets\n",
+    "def load_data():\n",
+    "    list_icon = []\n",
+    "    for dom in list(domdict.keys()):\n",
+    "        path = '/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/postprocessed_data_for_publication/'\n",
+    "        print('Working on loading data for', dom)\n",
+    "        ds = xr.open_dataset(path+dom+'/libradtran_pp_twostr_Fu.nc').chunk(chunks={'time': 1, 'height': 10})\n",
+    "        list_icon.append(ds)\n",
+    "    return list_icon\n",
+    "#-------------------------------------------------------------------------------------------------\n",
+    "list_libradtran=load_data()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aa998ccb-3c0e-4f92-9cc5-5d35f659f60b",
+   "metadata": {},
+   "source": [
+    "## 3- Average profiles of CRH"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "cea78232-b730-4039-8ded-a9fdd3f8adce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# excluding boundaries and takin domain average\n",
+    "# icon & libradtran datasets\n",
+    "for dom in range(len(list_icon)):\n",
+    "    for var in ['lwcrh','swcrh']:\n",
+    "        list_icon[dom][var+'_mean'] = list_icon[dom][var].isel(lon=slice(10,list_icon[dom].lon.size-10),lat=slice(5,list_icon[dom].lat.size-5)).mean(dim=['lat','lon','time']).compute()\n",
+    "        list_libradtran[dom][var+'_mean'] = list_libradtran[dom][var].isel(lon=slice(10,list_libradtran[dom].lon.size-10),lat=slice(5,list_libradtran[dom].lat.size-5)).mean(dim=['lat','lon','time']).compute()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9177e0b2-2d1a-4fde-ae44-bed15472d302",
+   "metadata": {},
+   "source": [
+    "### For data publication"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "c967d238-a92b-4041-815a-cc539b3fa7c6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# creating a dataset and save for data publication\n",
+    "ds_out = xr.Dataset(\n",
+    "    data_vars={\n",
+    "        \"swcrh_mean_libradtran_dom01\"  : (list_libradtran[0]['swcrh_mean'].dims, list_libradtran[0]['swcrh_mean'].data),\n",
+    "        \"lwcrh_mean_libradtran_dom01\"  : (list_libradtran[0]['lwcrh_mean'].dims, list_libradtran[0]['lwcrh_mean'].data),\n",
+    "        \"swcrh_mean_icon_dom01\"        : (list_icon[0]['swcrh_mean'].dims, list_icon[0]['swcrh_mean'].data),\n",
+    "        \"lwcrh_mean_icon_dom01\"        : (list_icon[0]['lwcrh_mean'].dims, list_icon[0]['lwcrh_mean'].data),\n",
+    "        \n",
+    "        \"swcrh_mean_libradtran_dom02\"  : (list_libradtran[1]['swcrh_mean'].dims, list_libradtran[1]['swcrh_mean'].data),\n",
+    "        \"lwcrh_mean_libradtran_dom02\"  : (list_libradtran[1]['lwcrh_mean'].dims, list_libradtran[1]['lwcrh_mean'].data),\n",
+    "        \"swcrh_mean_icon_dom02\"        : (list_icon[1]['swcrh_mean'].dims, list_icon[1]['swcrh_mean'].data),\n",
+    "        \"lwcrh_mean_icon_dom02\"        : (list_icon[1]['lwcrh_mean'].dims, list_icon[1]['lwcrh_mean'].data),\n",
+    "        \n",
+    "        \"swcrh_mean_libradtran_dom03\"  : (list_libradtran[2]['swcrh_mean'].dims, list_libradtran[2]['swcrh_mean'].data),\n",
+    "        \"lwcrh_mean_libradtran_dom03\"  : (list_libradtran[2]['lwcrh_mean'].dims, list_libradtran[2]['lwcrh_mean'].data),\n",
+    "        \"swcrh_mean_icon_dom03\"        : (list_icon[2]['swcrh_mean'].dims, list_icon[2]['swcrh_mean'].data),\n",
+    "        \"lwcrh_mean_icon_dom03\"        : (list_icon[2]['lwcrh_mean'].dims, list_icon[2]['lwcrh_mean'].data),\n",
+    "        \n",
+    "        \"swcrh_mean_libradtran_dom04\"  : (list_libradtran[3]['swcrh_mean'].dims, list_libradtran[3]['swcrh_mean'].data),\n",
+    "        \"lwcrh_mean_libradtran_dom04\"  : (list_libradtran[3]['lwcrh_mean'].dims, list_libradtran[3]['lwcrh_mean'].data),\n",
+    "        \"swcrh_mean_icon_dom04\"        : (list_icon[3]['swcrh_mean'].dims, list_icon[3]['swcrh_mean'].data),\n",
+    "        \"lwcrh_mean_icon_dom04\"        : (list_icon[3]['lwcrh_mean'].dims, list_icon[3]['lwcrh_mean'].data),       \n",
+    "    },\n",
+    "    coords=list_libradtran[0]['swcrh_mean'].coords)\n",
+    "ds_out = ds_out.assign(z_mc=list_libradtran[0]['z_mc'])\n",
+    "\n",
+    "ds_out.attrs['description'] = 'Vertical profiles of CRH from icon simulations and offline radiation calculation for each LEM domain'\n",
+    "ds_out.to_netcdf('/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/postprocessed_data_for_publication/for_publication/figure4.nc')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "601eedee-6297-4986-af2b-06bea2dfda19",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ds_out = xr.open_dataset('/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/postprocessed_data_for_publication/for_publication/figure4.nc')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "69ba8135-1543-4231-a841-bef5032b5e4d",
+   "metadata": {},
+   "source": [
+    "## 4- Plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "1503686e-6047-4198-8172-e67c140a38fb",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x360 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot\n",
+    "fig, axes = plt.subplots(nrows=1, ncols=4, figsize=(20, 5))\n",
+    "i = 0\n",
+    "\n",
+    "z = ds_out['z_mc']\n",
+    "\n",
+    "for ax in axes.flat:\n",
+    "    ax.tick_params(labelsize=14)\n",
+    "    \n",
+    "    if i == 0:\n",
+    "        ax.spines['top'].set_color('none')\n",
+    "        ax.spines['right'].set_color('none')\n",
+    "        ax.spines['left'].set_color('none')\n",
+    "        \n",
+    "        ax.plot(ds_out['swcrh_mean_libradtran_dom01'],z,color='#e6194B',label='Shortwave',lw=3)\n",
+    "        ax.plot(ds_out['swcrh_mean_icon_dom01'],z,color='#e6194B',linestyle='dashed',lw=3)\n",
+    "        \n",
+    "        ax.plot(ds_out['lwcrh_mean_libradtran_dom01'],z,color='#4363d8',label='Longwave',lw=3)\n",
+    "        ax.plot(ds_out['lwcrh_mean_icon_dom01'],z,color='#4363d8',linestyle='dashed',lw=3)\n",
+    "        \n",
+    "        icon_ntcrh = ds_out['swcrh_mean_icon_dom01'] + ds_out['lwcrh_mean_icon_dom01']\n",
+    "        libradtran_ntcrh = ds_out['swcrh_mean_libradtran_dom01'] + ds_out['lwcrh_mean_libradtran_dom01']\n",
+    "        \n",
+    "        ax.plot(libradtran_ntcrh,z,color='#000000',label='Net',lw=3)\n",
+    "        ax.plot(icon_ntcrh,z,color='#000000',linestyle='dashed',lw=3)\n",
+    "        \n",
+    "        ax.axvline(x=0, ymin=0.0, ymax=1,c='black', lw=1)\n",
+    "        ax.set_xlabel('Cloud radiative heating (K day$^{-1}$)', fontsize=14)#,labelpad=10)\n",
+    "        ax.set_ylabel('Height (km)', fontsize=14)\n",
+    "        #ax.set_yticklabels([])\n",
+    "        ax.text(0.0, 1.03, '(a)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "        ax.spines['bottom'].set_bounds(-10,10)\n",
+    "        ax.set_xticks(np.linspace(-10,10,5))\n",
+    "        ax.set_xlim([-10.4,10])\n",
+    "        \n",
+    "        lg=colorlegend.color_legend(ax,loc=1,fsize=14)\n",
+    "        ax.set_title('Shallow cumulus', fontsize=15,pad=15)\n",
+    "        ax.set_ylim([0,3])\n",
+    "        \n",
+    "        ax.text(-9, 2.85, \"solid: Offline\", color=\"gray\", size=14, va=\"top\")\n",
+    "        ax.text(-9, 2.6, \"dashed: ICON\", color=\"gray\", size=14, va=\"top\")\n",
+    "        \n",
+    "    \n",
+    "    if i == 1:\n",
+    "        ax.spines['top'].set_color('none')\n",
+    "        ax.spines['right'].set_color('none')\n",
+    "        ax.spines['left'].set_color('none')\n",
+    "        \n",
+    "        ax.plot(ds_out['swcrh_mean_libradtran_dom02'],z,color='#e6194B',label='Shortwave',lw=3)\n",
+    "        ax.plot(ds_out['swcrh_mean_icon_dom02'],z,color='#e6194B',linestyle='dashed',lw=3)\n",
+    "        \n",
+    "        ax.plot(ds_out['lwcrh_mean_libradtran_dom02'],z,color='#4363d8',label='Longwave',lw=3)\n",
+    "        ax.plot(ds_out['lwcrh_mean_icon_dom02'],z,color='#4363d8',linestyle='dashed',lw=3)\n",
+    "        \n",
+    "        icon_ntcrh = ds_out['swcrh_mean_icon_dom02'] + ds_out['lwcrh_mean_icon_dom02']\n",
+    "        libradtran_ntcrh = ds_out['swcrh_mean_libradtran_dom02'] + ds_out['lwcrh_mean_libradtran_dom02']\n",
+    "        \n",
+    "        ax.plot(libradtran_ntcrh,z,color='#000000',label='Net',lw=3)\n",
+    "        ax.plot(icon_ntcrh,z,color='#000000',linestyle='dashed',lw=3)\n",
+    "        \n",
+    "        ax.axvline(x=0, ymin=0.0, ymax=1,c='black', lw=1)\n",
+    "        ax.set_xlabel('Cloud radiative heating (K day$^{-1}$)', fontsize=14)#,labelpad=10)\n",
+    "        #ax.set_ylabel('Height (km)', fontsize=14)\n",
+    "        #ax.set_yticklabels([])\n",
+    "        ax.text(0.0, 1.03, '(b)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "        ax.spines['bottom'].set_bounds(-5,5)\n",
+    "        ax.set_xticks(np.linspace(-5,5,5))\n",
+    "        ax.set_xlim([-5.2,5])\n",
+    "        \n",
+    "        #lg=colorlegend.color_legend(ax,loc=1,fsize=14)\n",
+    "        ax.set_title('WCB ascent', fontsize=15,pad=15)\n",
+    "        ax.set_ylim([0,12])\n",
+    "        \n",
+    "    if i == 2:\n",
+    "        \n",
+    "        ax.spines['top'].set_color('none')\n",
+    "        ax.spines['right'].set_color('none')\n",
+    "        ax.spines['left'].set_color('none')\n",
+    "        \n",
+    "        ax.plot(ds_out['swcrh_mean_libradtran_dom03'],z,color='#e6194B',label='Shortwave',lw=3)\n",
+    "        ax.plot(ds_out['swcrh_mean_icon_dom03'],z,color='#e6194B',linestyle='dashed',lw=3)\n",
+    "        \n",
+    "        ax.plot(ds_out['lwcrh_mean_libradtran_dom03'],z,color='#4363d8',label='Longwave',lw=3)\n",
+    "        ax.plot(ds_out['lwcrh_mean_icon_dom03'],z,color='#4363d8',linestyle='dashed',lw=3)\n",
+    "        \n",
+    "        icon_ntcrh = ds_out['swcrh_mean_icon_dom03'] + ds_out['lwcrh_mean_icon_dom03']\n",
+    "        libradtran_ntcrh = ds_out['swcrh_mean_libradtran_dom03'] + ds_out['lwcrh_mean_libradtran_dom03']\n",
+    "        \n",
+    "        ax.plot(libradtran_ntcrh,z,color='#000000',label='Net',lw=3)\n",
+    "        ax.plot(icon_ntcrh,z,color='#000000',linestyle='dashed',lw=3)\n",
+    "        \n",
+    "        ax.axvline(x=0, ymin=0.0, ymax=1,c='black', lw=1)\n",
+    "        ax.set_xlabel('Cloud radiative heating (K day$^{-1}$)', fontsize=14)#,labelpad=10)\n",
+    "        #ax.set_ylabel('Height (km)', fontsize=14)\n",
+    "        ax.set_yticklabels([])\n",
+    "        ax.text(0.0, 1.03, '(c)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "        ax.spines['bottom'].set_bounds(-5,5)\n",
+    "        ax.set_xticks(np.linspace(-5,5,5))\n",
+    "        ax.set_xlim([-5.2,5])\n",
+    "        \n",
+    "        #lg=colorlegend.color_legend(ax,loc=5,fsize=11)\n",
+    "        ax.set_title('WCB cyclonic outflow', fontsize=15,pad=15)\n",
+    "        ax.set_ylim([0,12])\n",
+    "        \n",
+    "    if i == 3:\n",
+    "        \n",
+    "        ax.spines['top'].set_color('none')\n",
+    "        ax.spines['right'].set_color('none')\n",
+    "        ax.spines['left'].set_color('none')\n",
+    "        \n",
+    "        ax.plot(ds_out['swcrh_mean_libradtran_dom04'],z,color='#e6194B',label='Shortwave',lw=3)\n",
+    "        ax.plot(ds_out['swcrh_mean_icon_dom04'],z,color='#e6194B',linestyle='dashed',lw=3)\n",
+    "        \n",
+    "        ax.plot(ds_out['lwcrh_mean_libradtran_dom04'],z,color='#4363d8',label='Longwave',lw=3)\n",
+    "        ax.plot(ds_out['lwcrh_mean_icon_dom04'],z,color='#4363d8',linestyle='dashed',lw=3)\n",
+    "        \n",
+    "        icon_ntcrh = ds_out['swcrh_mean_icon_dom04'] + ds_out['lwcrh_mean_icon_dom04']\n",
+    "        libradtran_ntcrh = ds_out['swcrh_mean_libradtran_dom04'] + ds_out['lwcrh_mean_libradtran_dom04']\n",
+    "        \n",
+    "        ax.plot(libradtran_ntcrh,z,color='#000000',label='Net',lw=3)\n",
+    "        ax.plot(icon_ntcrh,z,color='#000000',linestyle='dashed',lw=3)\n",
+    "        \n",
+    "        ax.axvline(x=0, ymin=0.0, ymax=1,c='black', lw=1)\n",
+    "        ax.set_xlabel('Cloud radiative heating (K day$^{-1}$)', fontsize=14)#,labelpad=10)\n",
+    "        #ax.set_ylabel('Height (km)', fontsize=14)\n",
+    "        ax.set_yticklabels([])\n",
+    "        ax.text(0.0, 1.03, '(d)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "        ax.spines['bottom'].set_bounds(-5,5)\n",
+    "        ax.set_xticks(np.linspace(-5,5,5))\n",
+    "        ax.set_xlim([-5.2,5])\n",
+    "        \n",
+    "        #lg=colorlegend.color_legend(ax,loc=5,fsize=11)\n",
+    "        ax.set_title('WCB anticyclonic outflow', fontsize=15,pad=15)\n",
+    "        ax.set_ylim([0,12])    \n",
+    "        \n",
+    "    i = i + 1    \n",
+    "    \n",
+    "fig.subplots_adjust(wspace=0.2,hspace=0.35) \n",
+    "#plt.savefig('figure4.pdf', bbox_inches = 'tight')\n",
+    "#plt.savefig('figure4.png', bbox_inches = 'tight',dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "9e1b9f26-6d8f-4acb-808d-cad4f65e87cf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "client.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "36af53c9-ffe2-49f6-aba5-d000d45b266f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pycrh",
+   "language": "python",
+   "name": "pycrh"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/plots4paper/figure4.pdf b/plots4paper/figure4.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..70977c89566e17574f593312330a740eecb537e8
Binary files /dev/null and b/plots4paper/figure4.pdf differ
diff --git a/plots4paper/figure5.ipynb b/plots4paper/figure5.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..ca275fce4ca2d962eb73c9c33eceb7ed849a35f3
--- /dev/null
+++ b/plots4paper/figure5.ipynb
@@ -0,0 +1,420 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "115f93b6-8a4f-4f98-a0dc-2c79cceb1a7a",
+   "metadata": {},
+   "source": [
+    "# Figure 5 \n",
+    "\n",
+    "Figure 5. Cross-sections of shortwave, longwave and net all-sky radiative heating visualized by a logarithmic color scale for shallow cumulus\n",
+    "clouds southwest of the cyclone center. The upper row shows 3D calculations, the middle row shows 1D calculations, and the lower row\n",
+    "shows the differences between the 3D and 1D calculations. The cross sections are at domain local hour 16:30 and -11.5° longitude and\n",
+    "between 37.5° and 38° north. The solar zenith angle is 65°. Note that the impression of a lower solar zenith angle in the figure is due to the\n",
+    "aspect ratio of the figures\n",
+    "\n",
+    "---\n",
+    "@ Behrooz Keshtgar, KIT 2024"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c69547d-1bf2-4b27-957a-95e15f48812c",
+   "metadata": {},
+   "source": [
+    "## 1- load python packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "039407e9-ae55-437b-8d36-b67a5aa76fa2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import xarray as xr\n",
+    "import pandas as pd\n",
+    "import colorlegend\n",
+    "from matplotlib.ticker import (MultipleLocator, AutoMinorLocator)\n",
+    "import matplotlib.colors as mcolors\n",
+    "from matplotlib import cm\n",
+    "from matplotlib.colors import LinearSegmentedColormap,SymLogNorm\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bf9aea5-6e6e-46c9-925d-ce53a9042b21",
+   "metadata": {},
+   "source": [
+    "For reference, print package versions to screen:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "090549dc-8a05-4a15-a43a-2bef20f073d4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "xarrary:    0.16.0\n",
+      "numpy:      1.23.5\n",
+      "matplotlib: 3.3.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('xarrary:   ', xr.__version__)\n",
+    "print('numpy:     ', np.__version__)\n",
+    "import matplotlib; print('matplotlib:', matplotlib.__version__); del matplotlib"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1f71995-2a2b-49a4-9ab3-c56a389a5964",
+   "metadata": {},
+   "source": [
+    "## 2- Loading datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "01a5539f-d500-4c0b-83de-e0e95f4c5638",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "ds_mystic = xr.open_dataset('/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/postprocessed_data_for_publication/shallow_cumulus/libradtran_pp_mystic_RH_Fu.nc')\n",
+    "ds_mystic_ica = xr.open_dataset('/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/postprocessed_data_for_publication/shallow_cumulus/libradtran_pp_mystic_ica_RH_Fu.nc')\n",
+    "\n",
+    "ds_mystic['lat'] = np.linspace(37,43,1686)\n",
+    "ds_mystic_ica['lat'] = np.linspace(37,43,1686)\n",
+    "#lon = np.linspace(22,28,2064)\n",
+    "ds_mystic = ds_mystic.sel(lat=slice(37.5,38)).isel(lon=1550)\n",
+    "ds_mystic_ica = ds_mystic_ica.sel(lat=slice(37.5,38)).isel(lon=1550)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9b189c02-88f3-4104-bbde-c3170c95380b",
+   "metadata": {},
+   "source": [
+    "### For data publication"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "4055402c-e2da-4d76-a8e4-d2aa92505c60",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# creating a dataset and save for data publication\n",
+    "ds_out = xr.Dataset(\n",
+    "    data_vars={\n",
+    "        \"swrh_3d\"     : (ds_mystic['swrh'].dims, ds_mystic['swrh'].data),\n",
+    "        \"lwrh_3d\"     : (ds_mystic['lwrh'].dims, ds_mystic['lwrh'].data),\n",
+    "        \"swrh_1d\"     : (ds_mystic_ica['swrh'].dims, ds_mystic_ica['swrh'].data),\n",
+    "        \"lwrh_1d\"     : (ds_mystic_ica['lwrh'].dims, ds_mystic_ica['lwrh'].data),\n",
+    "        \n",
+    "    },\n",
+    "    coords=ds_mystic['swrh'].coords)\n",
+    "ds_out = ds_out.assign(z_mc=ds_mystic['z_mc'])\n",
+    "\n",
+    "ds_out.attrs['description'] = ' Cross-sections of RH at hour 16:30 and -11.5° longitude for 3D and 1D radiative transfer calculations in the shallow cumulus domain'\n",
+    "ds_out.to_netcdf('/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/postprocessed_data_for_publication/for_publication/figure5.nc')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "65bfdb41-ddb3-48c6-9461-8115558f53aa",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "ds_out = xr.open_dataset('/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/postprocessed_data_for_publication/for_publication/figure5.nc')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "94c94371-dcd9-41e4-bf5e-9df73495d3af",
+   "metadata": {},
+   "source": [
+    "## 3- Plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "f33e2bc0-3735-4154-a22f-247708d2abc8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1450.24x673.875 with 11 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(nrows=3, ncols=3, figsize=(25, 8))\n",
+    "\n",
+    "# coordinates for plotting\n",
+    "lat = ds_out['lat']\n",
+    "height = ds_out['z_mc']\n",
+    "cbar_ticks = [-100, -10, -1, 0, 1, 10, 100]\n",
+    "\n",
+    "#colors = plt.cm.RdBu_r(np.linspace(0.5, 1, 128))\n",
+    "# Create a custom colormap\n",
+    "#custom_cmap = LinearSegmentedColormap.from_list('CustomRdBuReds', colors)\n",
+    "\n",
+    "i = 0\n",
+    "\n",
+    "for ax in axes.flat:\n",
+    "    ax.tick_params(labelsize=18)\n",
+    "    #ax.set_xlim([37.5,38])\n",
+    "    ax.set_ylim([0,2])\n",
+    "    \n",
+    "    ##########################################\n",
+    "    # Shortwave\n",
+    "    ##########################################\n",
+    "    if i == 0:\n",
+    "        Z = ds_out['swrh_3d'].transpose().values\n",
+    "        im1 = ax.pcolor(lat,height,Z,norm=SymLogNorm(linthresh=1, linscale=1,vmin=-100, vmax=100),cmap='RdBu_r', shading='auto')\n",
+    "        ax.set_ylabel('Height (km)', fontsize=19)\n",
+    "        ax.set_title('Shortwave radiative heating', fontsize=20)\n",
+    "        ax.set_xticklabels([])\n",
+    "        ax.text(0.0, 1.065, '(a) 3D', transform=ax.transAxes, \n",
+    "            size=20)\n",
+    "        \n",
+    "    if i == 3:\n",
+    "        Z = ds_out['swrh_1d'].transpose().values\n",
+    "        im1 = ax.pcolor(lat,height,Z,norm=SymLogNorm(linthresh=1, linscale=1,vmin=-100, vmax=100),cmap='RdBu_r', shading='auto')\n",
+    "        ax.set_ylabel('Height (km)', fontsize=19)\n",
+    "        ax.set_xticklabels([])\n",
+    "        ax.text(0.0, 1.065, '(b) 1D', transform=ax.transAxes, \n",
+    "            size=20)\n",
+    "        \n",
+    "    if i == 6:\n",
+    "        diff = (ds_out['swrh_3d']-ds_out['swrh_1d']).transpose().values\n",
+    "        im2 = ax.pcolor(lat,height,diff,vmin=-20,vmax=20,cmap='seismic', shading='auto')\n",
+    "        ax.set_ylabel('Height (km)', fontsize=19)\n",
+    "        ax.text(0.0, 1.065, '(c) 3D - 1D', transform=ax.transAxes, \n",
+    "            size=20)\n",
+    "        ax.set_xlabel('Latitude', fontsize=19)\n",
+    "        \n",
+    "    ##########################################\n",
+    "    # Shortwave\n",
+    "    ##########################################\n",
+    "    if i == 1:\n",
+    "        Z = ds_out['lwrh_3d'].transpose().values\n",
+    "        im3 = ax.pcolor(lat,height,Z,norm=SymLogNorm(linthresh=1, linscale=1,vmin=-100, vmax=100),cmap='RdBu_r', shading='auto')\n",
+    "        ax.set_title('Longwave radiative heating', fontsize=20)\n",
+    "        ax.set_xticklabels([])\n",
+    "        ax.set_yticklabels([])\n",
+    "        ax.text(0.0, 1.065, '(d) 3D', transform=ax.transAxes, \n",
+    "            size=20)\n",
+    "        \n",
+    "    if i == 4:\n",
+    "        Z = ds_out['lwrh_1d'].transpose().values\n",
+    "        im3 = ax.pcolor(lat,height,Z,norm=SymLogNorm(linthresh=1, linscale=1,vmin=-100, vmax=100),cmap='RdBu_r', shading='auto')\n",
+    "        ax.set_xticklabels([])\n",
+    "        ax.set_yticklabels([])\n",
+    "        ax.text(0.0, 1.065, '(e) 1D', transform=ax.transAxes, \n",
+    "            size=20)\n",
+    "        \n",
+    "    if i == 7:\n",
+    "        diff = (ds_out['lwrh_3d']-ds_out['lwrh_1d']).transpose().values\n",
+    "        im4 = ax.pcolor(lat,height,diff,vmin=-20,vmax=20,cmap='seismic', shading='auto')\n",
+    "        ax.text(0.0, 1.065, '(f) 3D - 1D', transform=ax.transAxes, \n",
+    "            size=20)\n",
+    "        ax.set_xticks([37.6,37.7,37.8,37.9])\n",
+    "        ax.set_yticklabels([])\n",
+    "        ax.set_xlabel('Latitude', fontsize=19)\n",
+    "        \n",
+    "    ##########################################\n",
+    "    # Net\n",
+    "    ##########################################    \n",
+    "        \n",
+    "    if i == 2:\n",
+    "        net = (ds_out['swrh_3d']+ds_out['lwrh_3d']).transpose().values\n",
+    "        im5 = ax.pcolor(lat,height,net,norm=SymLogNorm(linthresh=1, linscale=1,vmin=-100, vmax=100),cmap='RdBu_r', shading='auto')\n",
+    "        ax.text(0.0, 1.065, '(g) 3D', transform=ax.transAxes, \n",
+    "            size=20)\n",
+    "        ax.set_title('Net radiative heating', fontsize=20)\n",
+    "        ax.set_yticklabels([])\n",
+    "        ax.set_xticklabels([])\n",
+    "        \n",
+    "    if i == 5:\n",
+    "        net = (ds_out['swrh_1d']+ds_out['lwrh_1d']).transpose().values\n",
+    "        im5 = ax.pcolor(lat,height,net,norm=SymLogNorm(linthresh=1, linscale=1,vmin=-100, vmax=100),cmap='RdBu_r', shading='auto')\n",
+    "        ax.text(0.0, 1.065, '(h) 1D', transform=ax.transAxes, \n",
+    "            size=20)\n",
+    "        \n",
+    "        ax.set_yticklabels([])\n",
+    "        ax.set_xticklabels([])\n",
+    "        \n",
+    "        cb_ax = fig.add_axes([0.16, -0.02, 0.7, 0.04]) # xcenter/ycenter/width/height\n",
+    "        cbar = fig.colorbar(im5,cax=cb_ax,orientation='horizontal',shrink=0.95,extend='both')\n",
+    "        cbar.set_label(label='radiative heating (K day$^{-1}$)', size='17',labelpad=0.1)\n",
+    "        cbar.ax.tick_params(labelsize=17)\n",
+    "        # Set colorbar ticks and labels\n",
+    "        cbar.set_ticks(cbar_ticks)\n",
+    "        cbar.set_ticklabels(cbar_ticks)\n",
+    "        ax.set_yticklabels([])\n",
+    "        \n",
+    "    if i == 8:\n",
+    "        net3d = (ds_out['swrh_3d']+ds_out['lwrh_3d']).transpose().values\n",
+    "        net1d = (ds_out['swrh_1d']+ds_out['lwrh_1d']).transpose().values\n",
+    "        diff = net3d - net1d\n",
+    "        im6 = ax.pcolor(lat,height,diff,vmin=-20,vmax=20,cmap='seismic', shading='auto')\n",
+    "        ax.text(0.0, 1.065, '(i) 3D - 1D', transform=ax.transAxes, \n",
+    "            size=20)\n",
+    "        \n",
+    "        cb_ax = fig.add_axes([0.16, -0.15, 0.7, 0.04]) # xcenter/ycenter/width/height\n",
+    "        cbar = fig.colorbar(im6,cax=cb_ax,orientation='horizontal',shrink=0.95,extend='both')\n",
+    "        cbar.set_label(label='radiative heating difference (K day$^{-1}$)', size='17',labelpad=0.1)\n",
+    "        cbar.ax.tick_params(labelsize=17)   \n",
+    "        ax.set_yticklabels([])\n",
+    "        ax.set_xlabel('Latitude', fontsize=19)\n",
+    "        \n",
+    "    i = i + 1     \n",
+    "    \n",
+    "fig.subplots_adjust(wspace=0.025, hspace=0.4)\n",
+    "plt.savefig('figure5.png', bbox_inches = 'tight',dpi=300)\n",
+    "plt.savefig('figure5.pdf', format='pdf', bbox_inches='tight', dpi=300, compression=6)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dbdccb61-f973-4aed-834a-90c9906345fa",
+   "metadata": {},
+   "source": [
+    "### extra plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "c4e9d14b-c59c-4427-b318-68824be8da8d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x432 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# adjusting colorbar\n",
+    "divnorm=mcolors.TwoSlopeNorm(vmin=-160., vcenter=1, vmax=30)\n",
+    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(12, 6))\n",
+    "\n",
+    "# coordinates for plotting\n",
+    "lat = np.linspace(37,43,1686)\n",
+    "lon = np.linspace(22,28,2064)\n",
+    "#lon[1550]-38\n",
+    "\n",
+    "i = 0\n",
+    "\n",
+    "for ax in axes.flat:\n",
+    "    ax.tick_params(labelsize=15)\n",
+    "    ax.set_xlim([37.2,37.3])\n",
+    "    ax.set_ylim([0,11])\n",
+    "    if i == 0:\n",
+    "        Z = ds_lib1[1].ddt_dom1.isel(time=8,lon=1550).transpose().values\n",
+    "        im1 = ax.pcolor(lat,z_fl2,Z,norm=mcolors.LogNorm(vmin=0.1, vmax=50),\n",
+    "                   cmap='plasma', shading='auto')\n",
+    "        ax.set_ylabel('Height (km)', fontsize=15)\n",
+    "        ax.set_xlabel('Latitude', fontsize=15)\n",
+    "        #ax.set_title('Shortwave radiative heating', fontsize=15)\n",
+    "        #ax.set_xticklabels([])\n",
+    "        \n",
+    "        ax.text(0.0, 1.02, '(a) 3D', transform=ax.transAxes, \n",
+    "            size=15)\n",
+    "       \n",
+    "        \n",
+    "    if i == 1:\n",
+    "        Z = ds_lib1[3].ddt_dom1.isel(time=8,lon=1550).transpose().values\n",
+    "        ax.pcolor(lat,z_fl2,Z,norm=mcolors.LogNorm(vmin=0.1, vmax=50),\n",
+    "                   cmap='plasma', shading='auto')\n",
+    "        #ax.set_title('LW RH', fontsize=16)\n",
+    "        #ax.set_xticklabels([])\n",
+    "        #ax.set_ylabel('Height (km)', fontsize=15)\n",
+    "        ax.set_xlabel('Latitude', fontsize=15)\n",
+    "        ax.text(0.0, 1.02, '(b) 1D', transform=ax.transAxes, \n",
+    "            size=15)\n",
+    "        \n",
+    "    i = i + 1\n",
+    "    \n",
+    "#fig.subplots_adjust(wspace=0.17, hspace=0.4)\n",
+    "\n",
+    "cb_ax = fig.add_axes([0.92, 0.15, 0.02, 0.7]) # xcenter/ycenter/width/height\n",
+    "cbar = fig.colorbar(im1,cax=cb_ax,orientation='vertical',shrink=0.95)\n",
+    "cbar.set_label(label='K day$^{-1}$', size='15',labelpad=0.1)\n",
+    "cbar.ax.tick_params(labelsize=16)\n",
+    "\n",
+    "#plt.savefig('figure5.pdf', bbox_inches = 'tight')\n",
+    "#plt.savefig('figure5_2.png', bbox_inches = 'tight',dpi=100)   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "97b853b1-a30d-4c3f-b831-edf43b2c2d78",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pycrh",
+   "language": "python",
+   "name": "pycrh"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/plots4paper/figure5.pdf b/plots4paper/figure5.pdf
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diff --git a/plots4paper/figure6.ipynb b/plots4paper/figure6.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..7e9269dc9b2e7cb482ba3498357ef804663ab241
--- /dev/null
+++ b/plots4paper/figure6.ipynb
@@ -0,0 +1,504 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "115f93b6-8a4f-4f98-a0dc-2c79cceb1a7a",
+   "metadata": {},
+   "source": [
+    "# Figure 6\n",
+    "\n",
+    "**Figure 6.** Average profiles of shortwave, longwave, and net 3D cloud radiative effects for the four cyclone regions. 3D cloud radiative effects are calculated as the difference in domain mean CRH between 3D (MYSTIC) and 1D (MYSTIC-ICA) radiative transfer calculations. The thin lines in panel (a) show the 3D radiative effects for nine snapshots between domain local hours 12:20 to 16:30, with the legend indicating the time and domain mean solar zenith angles for all snapshots. Note the different x-axes in panels.\n",
+    "\n",
+    "---\n",
+    "@ Behrooz Keshtgar, KIT 2024"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c69547d-1bf2-4b27-957a-95e15f48812c",
+   "metadata": {},
+   "source": [
+    "## 1- load python packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "039407e9-ae55-437b-8d36-b67a5aa76fa2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import xarray as xr\n",
+    "import pandas as pd\n",
+    "import colorlegend\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bf9aea5-6e6e-46c9-925d-ce53a9042b21",
+   "metadata": {},
+   "source": [
+    "For reference, print package versions to screen:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "090549dc-8a05-4a15-a43a-2bef20f073d4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "xarrary:    0.16.0\n",
+      "numpy:      1.23.5\n",
+      "matplotlib: 3.3.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('xarrary:   ', xr.__version__)\n",
+    "print('numpy:     ', np.__version__)\n",
+    "import matplotlib; print('matplotlib:', matplotlib.__version__); del matplotlib"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "05c4817c-8fda-4e9f-9f42-8b74b7af5adf",
+   "metadata": {},
+   "source": [
+    "**Since datasets are large, I use DASK to speed up my analysis**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "daa2683a-2fb1-43ba-ab6e-415dce04ee27",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table style=\"border: 2px solid white;\">\n",
+       "<tr>\n",
+       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
+       "<h3 style=\"text-align: left;\">Client</h3>\n",
+       "<ul style=\"text-align: left; list-style: none; margin: 0; padding: 0;\">\n",
+       "  <li><b>Scheduler: </b>tcp://127.0.0.1:43429</li>\n",
+       "  <li><b>Dashboard: </b><a href='/user/b381185/levante-spawner-preset//proxy/8787/status' target='_blank'>/user/b381185/levante-spawner-preset//proxy/8787/status</a></li>\n",
+       "</ul>\n",
+       "</td>\n",
+       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
+       "<h3 style=\"text-align: left;\">Cluster</h3>\n",
+       "<ul style=\"text-align: left; list-style:none; margin: 0; padding: 0;\">\n",
+       "  <li><b>Workers: </b>16</li>\n",
+       "  <li><b>Cores: </b>256</li>\n",
+       "  <li><b>Memory: </b>522.84 GB</li>\n",
+       "</ul>\n",
+       "</td>\n",
+       "</tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Client: 'tcp://127.0.0.1:43429' processes=16 threads=256, memory=522.84 GB>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import dask\n",
+    "from dask.distributed import Client, progress, wait\n",
+    "dask.config.config.get('distributed').get('dashboard').update({'link':'{JUPYTERHUB_SERVICE_PREFIX}/proxy/{port}/status'})\n",
+    "client = Client()\n",
+    "client"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1f71995-2a2b-49a4-9ab3-c56a389a5964",
+   "metadata": {},
+   "source": [
+    "## 2- Loading datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "d8792fad-2a58-45e3-86a9-eac4d7cdeff8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Dictionary for loading datasets for the 4 LEM domains\n",
+    "domdict = {\n",
+    "         'shallow_cumulus'          : {'res':'300m'}, \n",
+    "         'WCB_ascent'               : {'res':'300m'}, \n",
+    "         'WCB_cyclonic_outflow'     : {'res':'300m'}, \n",
+    "         'WCB_anticyclonic_outflow' : {'res':'300m'}\n",
+    "          }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "bfff1bc1-4eed-4ddc-8e71-fa5ef73eebc6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Working on loading data for shallow_cumulus\n",
+      "Working on loading data for WCB_ascent\n",
+      "Working on loading data for WCB_cyclonic_outflow\n",
+      "Working on loading data for WCB_anticyclonic_outflow\n",
+      "Working on loading data for shallow_cumulus\n",
+      "Working on loading data for WCB_ascent\n",
+      "Working on loading data for WCB_cyclonic_outflow\n",
+      "Working on loading data for WCB_anticyclonic_outflow\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Loading icon-pp datasets\n",
+    "def load_data(solver):\n",
+    "    list_icon = []\n",
+    "    for dom in list(domdict.keys()):\n",
+    "        path = '/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/postprocessed_data_for_publication/'\n",
+    "        print('Working on loading data for', dom)\n",
+    "        ds = xr.open_dataset(path+dom+'/libradtran_pp_'+solver+'_Fu.nc').chunk(chunks={'time': 1, 'height': 10})\n",
+    "        list_icon.append(ds)\n",
+    "    return list_icon\n",
+    "#-------------------------------------------------------------------------------------------------\n",
+    "list_libradtran_mystic=load_data('mystic')\n",
+    "list_libradtran_mystic_ica=load_data('mystic_ica')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e621a885-2a34-41b9-8bac-572a77338820",
+   "metadata": {},
+   "source": [
+    "## 3- Average profiles of CRH"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "671cd54f-59fc-4227-a8cb-210776b9046a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# excluding boundaries and takin domain average\n",
+    "# MYSTIC & MYSTIC_ICA datasets\n",
+    "for dom in range(len(list_libradtran_mystic)):\n",
+    "    for var in ['lwcrh','swcrh']:\n",
+    "        list_libradtran_mystic[dom][var+'_smean'] = list_libradtran_mystic[dom][var].isel(lon=slice(10,list_libradtran_mystic[dom].lon.size-10),lat=slice(5,list_libradtran_mystic[dom].lat.size-5)).mean(dim=['lat','lon']).compute()\n",
+    "        list_libradtran_mystic_ica[dom][var+'_smean'] = list_libradtran_mystic_ica[dom][var].isel(lon=slice(10,list_libradtran_mystic_ica[dom].lon.size-10),lat=slice(5,list_libradtran_mystic_ica[dom].lat.size-5)).mean(dim=['lat','lon']).compute()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "94c94371-dcd9-41e4-bf5e-9df73495d3af",
+   "metadata": {},
+   "source": [
+    "### For data publication"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "94bb6adf-abd8-41ae-9754-0447f9a873a7",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "## domain mean solar zenith angles for legends:\n",
+    "T1 = np.array([\"37.92852833275016\",\"37.80226949178842\",\"37.69671682401419\",\"37.61500059570729\",\"37.55681047321208\",\"37.52023226519184\",\"38.915064020556464\",\"38.791717045432485\",\"38.69130644707883\",\"38.6124667555098\",\"38.55532033675725\",\"38.52108367831912\",\"39.89756930574191\",\"39.78077150359704\",\"39.683843308414886\",\"39.60774709076623\",\"39.55259379419657\",\"39.51723294416274\",\"40.88531971355025\",\"40.77170908179705\",\"40.67900799236836\",\"40.60554511010893\",\"40.55100744451595\",\"40.518144268424\",\"41.871281536401376\",\"41.761552227572196\",\"41.6697178725893\",\"41.59878136419815\",\"41.54843872264263\",\"41.51671092273384\",\"42.86007797171621\",\"42.75409691648596\",\"42.66536686448403\",\"42.59687090328268\",\"42.54830405297876\",\"42.51766989988411\"])\n",
+    "T2 = np.array([\"37.52804257737442\",\"37.5708485262013\",\"37.63525646429392\",\"37.72313692501985\",\"37.8348295917875\",\"37.96714186785375\",\"38.528617289510315\",\"38.56887963670958\",\"38.63201025502695\",\"38.716806415256485\",\"38.82312129316935\",\"38.95233862619279\",\"39.52450470667009\",\"39.56568310493516\",\"39.62660989841232\",\"39.708454681810146\",\"39.81108852936032\",\"39.9335640002251\",\"40.525161204083375\",\"40.56362348781788\",\"40.623754587836636\",\"40.70276780222646\",\"40.801001173719364\",\"40.92008212134916\",\"41.52348564954213\",\"41.560619813285165\",\"41.6163653520573\",\"41.69266371028226\",\"41.78984320404155\",\"41.90485564921136\",\"42.52421348857957\",\"42.56006496745941\",\"42.61384687101609\",\"42.68752934071401\",\"42.78142156329275\",\"42.892511490806875\"])\n",
+    "T3 = np.array([\"38.393382140661586\",\"38.600978593063516\",\"38.82890610307521\",\"39.07773477712322\",\"39.348481903382506\",\"39.6369378833718\",\"39.363927225231144\",\"39.56395129822498\",\"39.784699610121365\",\"40.02524626154755\",\"40.285220442690964\",\"40.56617571466739\",\"40.331149307066646\",\"40.526690604926856\",\"40.74007105812733\",\"40.97265672879202\",\"41.22411566204011\",\"41.49379378885967\",\"41.30414264361065\",\"41.49143810158553\",\"41.699415052597566\",\"41.92430775667623\",\"42.16714492937311\",\"42.42841883599341\",\"42.27593517464186\",\"42.45696514886261\",\"42.65580677226942\",\"42.87331074609787\",\"43.110431242317176\",\"43.36325730818952\",\"43.2510471213355\",\"43.426024665409656\",\"43.61821460873848\",\"43.82854736490248\",\"44.05795260030291\",\"44.30257970328838\"])\n",
+    "T4 = np.array([\"40.44165487593281\",\"40.79517341482291\",\"41.16627500474074\",\"41.55440607164541\",\"41.96130014675722\",\"42.3820719537748\",\"41.34524839499175\",\"41.687713869889386\",\"42.047414045180304\",\"42.42379713129446\",\"42.81641054514186\",\"43.22699840313858\",\"42.248183702305916\",\"42.58211023033625\",\"42.930771883486365\",\"43.29576097771013\",\"43.67665358752518\",\"44.073184679411355\",\"43.15879696979142\",\"43.48031768182554\",\"43.82026177828612\",\"44.17414985378856\",\"44.54367293360984\",\"44.9283562661723\",\"44.070430251562\",\"44.38192973303821\",\"44.70931291912708\",\"45.052446552360756\",\"45.41291230741974\",\"45.786172037339895\",\"44.98720091724115\",\"45.288938133166084\",\"45.60614178243116\",\"45.93876085295428\",\"46.2883428092579\",\"46.65042970436143\"])\n",
+    "T5 = np.array([\"43.50211594568874\",\"43.97527181528498\",\"44.46282048306486\",\"44.96331269500535\",\"45.47915438678598\",\"46.005096395553494\",\"44.3150466682018\",\"44.77547815392289\",\"45.249264736469335\",\"45.73632122616192\",\"46.23620735741138\",\"46.75087221962785\",\"45.130128921162566\",\"45.579619773504504\",\"46.04039981451561\",\"46.514289111307946\",\"47.00087692831748\",\"47.50032350352602\",\"45.95476018415644\",\"46.389379144360724\",\"46.83961452490772\",\"47.30054956113451\",\"47.77451281827034\",\"48.26020496320508\",\"46.782802646933796\",\"47.20510812386976\",\"47.640895397666284\",\"48.089170949561236\",\"48.552118961213885\",\"49.024824418691146\",\"47.61788468437165\",\"48.0281029336541\",\"48.451546100315205\",\"48.887329737304704\",\"49.337583804221204\",\"49.797473922510704\"])\n",
+    "T6 = np.array([\"47.37298481099091\",\"47.93921762829051\",\"48.51690408097273\",\"49.1039174123586\",\"49.70323218372862\",\"50.30944323805887\",\"48.08432474158632\",\"48.637522367927055\",\"49.20052156792213\",\"49.77369095450026\",\"50.35664786987366\",\"50.95153566217326\",\"48.799956197111236\",\"49.34116735117\",\"49.89048639142745\",\"50.44995524366404\",\"51.01921032206644\",\"51.598821137349184\",\"49.52631380121321\",\"50.05158099135184\",\"50.58971472382445\",\"51.135574465877916\",\"51.692030283912295\",\"52.25711994962813\",\"50.257933853848876\",\"50.76986420180333\",\"51.29288921767152\",\"51.82532763166959\",\"52.36989087901145\",\"52.92146463286269\",\"50.99796155460589\",\"51.496677035303684\",\"52.006364170309126\",\"52.52544459678632\",\"53.05656813151169\",\"53.59469125116787\"])\n",
+    "T7 = np.array([\"51.86525278681334\",\"52.50163138171818\",\"53.14708458600082\",\"53.79892021300535\",\"54.460587741235656\",\"55.12664473307323\",\"52.4729318119723\",\"53.09676204632896\",\"53.72743690884621\",\"54.365731742394566\",\"55.01135058606687\",\"55.66655438303498\",\"53.086105823806854\",\"53.697745531359175\",\"54.314860099119265\",\"54.939646274813256\",\"55.57180679440118\",\"56.21228177556476\",\"53.710287788163946\",\"54.30582397615151\",\"54.911834221627075\",\"55.523085692916375\",\"56.14288368017729\",\"56.76873351633969\",\"54.34076022365604\",\"54.92277239554441\",\"55.51380541502224\",\"56.111594587658\",\"56.71929353868736\",\"57.331720241866364\",\"54.98022277571468\",\"55.54872124023649\",\"56.1261978658082\",\"56.71049383377817\",\"57.30468131154461\",\"57.9036473467872\"])\n",
+    "T8 = np.array([\"56.82200151394028\",\"57.51028685462972\",\"58.20580702908052\",\"58.905428907703616\",\"59.612941706258766\",\"60.32294837016508\",\"57.32873029450165\",\"58.00528311267857\",\"58.686343393951205\",\"59.37302749942263\",\"60.06509818942504\",\"60.76490058032287\",\"57.84131104011324\",\"58.505892757586004\",\"59.17387410932022\",\"59.847556632941895\",\"60.5267054615\",\"61.21256539012508\",\"58.36435865451906\",\"59.01316289095367\",\"59.670471969779854\",\"60.331039354919255\",\"60.99853073445001\",\"61.6699836082924\",\"58.89394241575616\",\"59.52948715602527\",\"60.17235119183574\",\"60.81979754299718\",\"61.47531349684327\",\"62.13373380049016\",\"59.43232983181762\",\"60.054530295128494\",\"60.68405285944021\",\"61.3182481911775\",\"61.96053628082873\",\"62.60580514800309\"])\n",
+    "T9 = np.array([\"62.12047306956876\",\"62.84662980092738\",\"63.57863706129193\",\"64.312980592758\",\"65.05370823983753\",\"65.79550053493416\",\"62.53087253131712\",\"63.246180395366196\",\"63.96416740688413\",\"64.68622745650455\",\"65.41218301461255\",\"66.1444058308444\",\"62.94682454485622\",\"63.650520562215156\",\"64.35602268760988\",\"65.06567569716411\",\"65.77930370776433\",\"66.49839779078314\",\"63.372092113011014\",\"64.06053517876158\",\"64.75588599847472\",\"65.45296073560695\",\"66.15567487915047\",\"66.86068908837552\",\"63.80349610985502\",\"64.47912423001112\",\"65.16072632129466\",\"65.84514350953924\",\"66.53613331646902\",\"67.22858765262404\",\"64.24289592060603\",\"64.9055655925753\",\"65.57421869649646\",\"66.24579206083693\",\"66.92396218595137\",\"67.60368642673636\"])\n",
+    "\n",
+    "# Calculate mean values\n",
+    "T1 = np.mean(T1.astype(np.float))\n",
+    "T2 = np.mean(T2.astype(np.float))\n",
+    "T3 = np.mean(T3.astype(np.float))\n",
+    "T4 = np.mean(T4.astype(np.float))\n",
+    "T5 = np.mean(T5.astype(np.float))\n",
+    "T6 = np.mean(T6.astype(np.float))\n",
+    "T7 = np.mean(T7.astype(np.float))\n",
+    "T8 = np.mean(T8.astype(np.float))\n",
+    "T9 = np.mean(T9.astype(np.float))\n",
+    "\n",
+    "# Create xarray DataArray\n",
+    "sza = xr.DataArray([T1, T2, T3, T4, T5, T6, T7, T8, T9],dims=list_libradtran_mystic[0]['time'].dims)\n",
+    "# local times of the domains \n",
+    "hour = ['12:30', '13:00', '13:30', '14:00', '14:30', '15:00', '15:30', '16:00', '16:30']\n",
+    "# Add the 'hour' dimension to the DataArray\n",
+    "sza = sza.assign_coords(time=hour)\n",
+    "\n",
+    "# creating a dataset and save for data publication\n",
+    "ds_out = xr.Dataset(\n",
+    "    data_vars={\n",
+    "        \"swcrh_smean_mystic_dom01\"     : (list_libradtran_mystic[0]['swcrh_smean'].dims, list_libradtran_mystic[0]['swcrh_smean'].data),\n",
+    "        \"lwcrh_smean_mystic_dom01\"     : (list_libradtran_mystic[0]['lwcrh_smean'].dims, list_libradtran_mystic[0]['lwcrh_smean'].data),\n",
+    "        \"swcrh_smean_mystic_ica_dom01\" : (list_libradtran_mystic_ica[0]['swcrh_smean'].dims, list_libradtran_mystic_ica[0]['swcrh_smean'].data),\n",
+    "        \"lwcrh_smean_mystic_ica_dom01\" : (list_libradtran_mystic_ica[0]['lwcrh_smean'].dims, list_libradtran_mystic_ica[0]['lwcrh_smean'].data),\n",
+    "        \n",
+    "        \"swcrh_smean_mystic_dom02\"     : (list_libradtran_mystic[1]['swcrh_smean'].dims, list_libradtran_mystic[1]['swcrh_smean'].data),\n",
+    "        \"lwcrh_smean_mystic_dom02\"     : (list_libradtran_mystic[1]['lwcrh_smean'].dims, list_libradtran_mystic[1]['lwcrh_smean'].data),\n",
+    "        \"swcrh_smean_mystic_ica_dom02\" : (list_libradtran_mystic_ica[1]['swcrh_smean'].dims, list_libradtran_mystic_ica[1]['swcrh_smean'].data),\n",
+    "        \"lwcrh_smean_mystic_ica_dom02\" : (list_libradtran_mystic_ica[1]['lwcrh_smean'].dims, list_libradtran_mystic_ica[1]['lwcrh_smean'].data),\n",
+    "        \n",
+    "        \"swcrh_smean_mystic_dom03\"     : (list_libradtran_mystic[2]['swcrh_smean'].dims, list_libradtran_mystic[2]['swcrh_smean'].data),\n",
+    "        \"lwcrh_smean_mystic_dom03\"     : (list_libradtran_mystic[2]['lwcrh_smean'].dims, list_libradtran_mystic[2]['lwcrh_smean'].data),\n",
+    "        \"swcrh_smean_mystic_ica_dom03\" : (list_libradtran_mystic_ica[2]['swcrh_smean'].dims, list_libradtran_mystic_ica[2]['swcrh_smean'].data),\n",
+    "        \"lwcrh_smean_mystic_ica_dom03\" : (list_libradtran_mystic_ica[2]['lwcrh_smean'].dims, list_libradtran_mystic_ica[2]['lwcrh_smean'].data),\n",
+    "        \n",
+    "        \"swcrh_smean_mystic_dom04\"     : (list_libradtran_mystic[3]['swcrh_smean'].dims, list_libradtran_mystic[3]['swcrh_smean'].data),\n",
+    "        \"lwcrh_smean_mystic_dom04\"     : (list_libradtran_mystic[3]['lwcrh_smean'].dims, list_libradtran_mystic[3]['lwcrh_smean'].data),\n",
+    "        \"swcrh_smean_mystic_ica_dom04\" : (list_libradtran_mystic_ica[3]['swcrh_smean'].dims, list_libradtran_mystic_ica[3]['swcrh_smean'].data),\n",
+    "        \"lwcrh_smean_mystic_ica_dom04\" : (list_libradtran_mystic_ica[3]['lwcrh_smean'].dims, list_libradtran_mystic_ica[3]['lwcrh_smean'].data),\n",
+    "        \n",
+    "    },\n",
+    "    coords=list_libradtran_mystic[0]['swcrh_smean'].coords)\n",
+    "ds_out = ds_out.assign(z_mc=list_libradtran_mystic[0]['z_mc'])\n",
+    "ds_out = ds_out.assign(sza=sza)\n",
+    "\n",
+    "ds_out.attrs['description'] = 'Vertical profiles of CRH from MYSTIC and MYSTIC_ICA radiation calculation for each LEM domain'\n",
+    "ds_out.to_netcdf('/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/postprocessed_data_for_publication/for_publication/figure6.nc')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "6e8ef15a-ba9d-4c08-aafd-e8f1afae90a6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "ds_out = xr.open_dataset('/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/postprocessed_data_for_publication/for_publication/figure6.nc')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5aa424cd-5298-4485-bb6d-adf925c77b37",
+   "metadata": {},
+   "source": [
+    "## 4- Plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "b134ab4d-43f7-4627-81a9-9945c46314ee",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x360 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cmapr = cm.Reds(np.linspace(0.2,1,9))\n",
+    "cmapb = cm.Blues(np.linspace(0.2,1,9))\n",
+    "\n",
+    "fig, axes = plt.subplots(nrows=1, ncols=4, figsize=(20, 5))\n",
+    "i = 0\n",
+    "\n",
+    "z = ds_out['z_mc']\n",
+    "\n",
+    "for ax in axes.flat:\n",
+    "    ax.tick_params(labelsize=14)\n",
+    "    ax.spines['top'].set_color('none')\n",
+    "    ax.spines['right'].set_color('none')\n",
+    "    ax.spines['left'].set_color('none')\n",
+    "    \n",
+    "    if i == 0:\n",
+    "        \n",
+    "        # at each time step\n",
+    "        for t in range(9):\n",
+    "            lw3d = ds_out['lwcrh_smean_mystic_dom01'][t].values - ds_out['lwcrh_smean_mystic_ica_dom01'][t].values\n",
+    "            ax.plot(lw3d,z,color=cmapb[t],lw=1,alpha=1)\n",
+    "            sw3d = ds_out['swcrh_smean_mystic_dom01'][t].values - ds_out['swcrh_smean_mystic_ica_dom01'][t].values\n",
+    "            ax.plot(sw3d,z,color=cmapr[t],lw=1,alpha=1)\n",
+    "        \n",
+    "        # time mean\n",
+    "        lw3d = ds_out['lwcrh_smean_mystic_dom01'].mean('time').values - ds_out['lwcrh_smean_mystic_ica_dom01'].mean('time').values\n",
+    "        sw3d = ds_out['swcrh_smean_mystic_dom01'].mean('time').values - ds_out['swcrh_smean_mystic_ica_dom01'].mean('time').values\n",
+    "        net3d = lw3d + sw3d\n",
+    "\n",
+    "        ax.set_title('Shallow cumulus', fontsize=15,pad=15)\n",
+    "        ax.plot(sw3d,z,color='#e6194B',lw=3)\n",
+    "        ax.plot(lw3d,z,color='#4363d8',lw=3)\n",
+    "        ax.plot(net3d,z,color='#000000',lw=3)\n",
+    "        \n",
+    "        ax.axvline(x=0, ymin=0.0, ymax=1,c='black', lw=1)\n",
+    "        ax.set_ylabel('Height (km)', fontsize=14)\n",
+    "        ax.set_xlabel('3D cloud radiative effects (K day$^{-1}$)', fontsize=14)#,labelpad=10)\n",
+    "        ax.text(0.0, 1.06, '(a)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "        ax.set_ylim([0,3])\n",
+    "        \n",
+    "        ax.spines['bottom'].set_bounds(-0.3,0.3)\n",
+    "        ax.set_xticks(np.linspace(-0.3,0.3,5))\n",
+    "        ax.set_xlim([-0.315,0.3])\n",
+    "        \n",
+    "        ax.text(-0.29, 2.9, \"12:30, 40.15°\", color=cmapr[0], size=13)\n",
+    "        ax.text(-0.29, 2.7, \"13:00, 40.20°\", color=cmapr[1], size=13)\n",
+    "        ax.text(-0.29, 2.5, \"13:30, 41.40°\", color=cmapr[2], size=13)\n",
+    "        ax.text(-0.29, 2.3, \"14:00, 43.60°\", color=cmapr[3], size=13)\n",
+    "        ax.text(-0.29, 2.1, \"14:30, 46.70°\", color=cmapr[4], size=13)\n",
+    "        \n",
+    "        ax.text(0.02, 2.9, \"15:00, 50.50°\", color=cmapr[5], size=13)\n",
+    "        ax.text(0.02, 2.7, \"15:30, 55.00°\", color=cmapr[6], size=13)\n",
+    "        ax.text(0.02, 2.5, \"16:00, 59.80°\", color=cmapr[7], size=13)\n",
+    "        ax.text(0.02, 2.3, \"16:30, 65.00°\", color=cmapr[8], size=13)\n",
+    "        \n",
+    "    if i == 1:\n",
+    "        \n",
+    "        # time mean\n",
+    "        lw3d = ds_out['lwcrh_smean_mystic_dom02'].mean('time').values - ds_out['lwcrh_smean_mystic_ica_dom02'].mean('time').values\n",
+    "        sw3d = ds_out['swcrh_smean_mystic_dom02'].mean('time').values - ds_out['swcrh_smean_mystic_ica_dom02'].mean('time').values\n",
+    "        net3d = lw3d + sw3d\n",
+    "\n",
+    "        ax.set_title('WCB ascent', fontsize=15,pad=15)\n",
+    "        ax.plot(sw3d,z,color='#e6194B',lw=3,label='Shortwave')\n",
+    "        ax.plot(lw3d,z,color='#4363d8',lw=3,label='Longwave')\n",
+    "        ax.plot(net3d,z,color='#000000',lw=3,label='Net')\n",
+    "        lg=colorlegend.color_legend(ax,loc=1,fsize=14)\n",
+    "        \n",
+    "        ax.axvline(x=0, ymin=0.0, ymax=1,c='black', lw=1)\n",
+    "        ax.set_xlabel('3D cloud radiative effects (K day$^{-1}$)', fontsize=14)\n",
+    "        ax.text(0.0, 1.06, '(b)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "        ax.set_ylim([0,12])\n",
+    "        \n",
+    "        ax.spines['bottom'].set_bounds(-0.1,0.1)\n",
+    "        ax.set_xticks(np.linspace(-0.1,0.1,5))\n",
+    "        ax.set_xlim([-0.105,0.1])\n",
+    "        \n",
+    "    if i == 2:\n",
+    "        \n",
+    "        # time mean\n",
+    "        lw3d = ds_out['lwcrh_smean_mystic_dom03'].mean('time').values - ds_out['lwcrh_smean_mystic_ica_dom03'].mean('time').values\n",
+    "        sw3d = ds_out['swcrh_smean_mystic_dom03'].mean('time').values - ds_out['swcrh_smean_mystic_ica_dom03'].mean('time').values\n",
+    "        net3d = lw3d + sw3d\n",
+    "        \n",
+    "        ax.set_title('WCB cyclonic outflow', fontsize=15,pad=15)\n",
+    "        ax.plot(sw3d,z,color='#e6194B',lw=3,label='Shortwave')\n",
+    "        ax.plot(lw3d,z,color='#4363d8',lw=3,label='Longwave')\n",
+    "        ax.plot(net3d,z,color='#000000',lw=3,label='Net')\n",
+    "        \n",
+    "        ax.axvline(x=0, ymin=0.0, ymax=1,c='black', lw=1)\n",
+    "        ax.set_xlabel('3D cloud radiative effects (K day$^{-1}$)', fontsize=14)\n",
+    "        ax.text(0.0, 1.06, '(c)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "        ax.set_ylim([0,12])\n",
+    "        ax.set_yticklabels([])\n",
+    "        \n",
+    "        ax.spines['bottom'].set_bounds(-0.04,0.04)\n",
+    "        ax.set_xticks(np.linspace(-0.04,0.04,5))\n",
+    "        ax.set_xlim([-0.042,0.04])\n",
+    "    \n",
+    "    if i == 3:\n",
+    "        \n",
+    "        # time mean\n",
+    "        lw3d = ds_out['lwcrh_smean_mystic_dom04'].mean('time').values - ds_out['lwcrh_smean_mystic_ica_dom04'].mean('time').values\n",
+    "        sw3d = ds_out['swcrh_smean_mystic_dom04'].mean('time').values - ds_out['swcrh_smean_mystic_ica_dom04'].mean('time').values\n",
+    "        net3d = lw3d + sw3d\n",
+    "        \n",
+    "        ax.set_title('WCB anticyclonic outflow', fontsize=15,pad=15)\n",
+    "        ax.plot(sw3d,z,color='#e6194B',lw=3,label='Shortwave')\n",
+    "        ax.plot(lw3d,z,color='#4363d8',lw=3,label='Longwave')\n",
+    "        ax.plot(net3d,z,color='#000000',lw=3,label='Net')\n",
+    "        \n",
+    "        ax.axvline(x=0, ymin=0.0, ymax=1,c='black', lw=1)\n",
+    "        ax.set_xlabel('3D cloud radiative effects (K day$^{-1}$)', fontsize=14)\n",
+    "        ax.text(0.0, 1.06, '(d)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "        ax.set_ylim([0,12])\n",
+    "        ax.set_yticklabels([])\n",
+    "        \n",
+    "        ax.spines['bottom'].set_bounds(-0.04,0.04)\n",
+    "        ax.set_xticks(np.linspace(-0.04,0.04,5))\n",
+    "        ax.set_xlim([-0.042,0.04])\n",
+    "        \n",
+    "    i = i + 1    \n",
+    "\n",
+    "fig.subplots_adjust(wspace=0.2,hspace=0.35)     \n",
+    "plt.savefig('figure6.pdf', bbox_inches = 'tight')\n",
+    "#plt.savefig('figure6.png', bbox_inches = 'tight',dpi=300)    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "6cf439d2-42c5-41f2-a80d-5ed2df441da1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "client.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "97b853b1-a30d-4c3f-b831-edf43b2c2d78",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pycrh",
+   "language": "python",
+   "name": "pycrh"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/plots4paper/figure6.pdf b/plots4paper/figure6.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..12fddaa287dfcbcccc2bd59fc09dff5d320e84c0
Binary files /dev/null and b/plots4paper/figure6.pdf differ
diff --git a/plots4paper/figure7.ipynb b/plots4paper/figure7.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..c394575cc97c78c6a467934b871ce3cd4598195a
--- /dev/null
+++ b/plots4paper/figure7.ipynb
@@ -0,0 +1,290 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "115f93b6-8a4f-4f98-a0dc-2c79cceb1a7a",
+   "metadata": {},
+   "source": [
+    "# Figure 7\n",
+    "\n",
+    "**Figure 7.**  Cross-sections of (a) shortwave, (b) longwave, and (c) net CRH calculated with the ice-optical parameterization of Fu in the WCB ascent region. Panels (d-f) show CRH differences between the ice schemes of Fu and the ice scheme of Baum with the general habit mixture (Fu - Baum). Cross-sections are shown at domain local hour 14:30 and at 3â—¦ longitude.\n",
+    "\n",
+    "---\n",
+    "@ Behrooz Keshtgar, KIT 2024"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c69547d-1bf2-4b27-957a-95e15f48812c",
+   "metadata": {},
+   "source": [
+    "## 1- load python packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "039407e9-ae55-437b-8d36-b67a5aa76fa2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import xarray as xr\n",
+    "import pandas as pd\n",
+    "import colorlegend\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bf9aea5-6e6e-46c9-925d-ce53a9042b21",
+   "metadata": {},
+   "source": [
+    "For reference, print package versions to screen:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "090549dc-8a05-4a15-a43a-2bef20f073d4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "xarrary:    0.16.0\n",
+      "numpy:      1.23.5\n",
+      "matplotlib: 3.3.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('xarrary:   ', xr.__version__)\n",
+    "print('numpy:     ', np.__version__)\n",
+    "import matplotlib; print('matplotlib:', matplotlib.__version__); del matplotlib"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1f71995-2a2b-49a4-9ab3-c56a389a5964",
+   "metadata": {},
+   "source": [
+    "## 2- Loading datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d8792fad-2a58-45e3-86a9-eac4d7cdeff8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds_twostr_fu = xr.open_dataset('/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/postprocessed_data_for_publication/WCB_ascent/libradtran_pp_twostr_Fu.nc')\n",
+    "ds_twostr_baum = xr.open_dataset('/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/postprocessed_data_for_publication/WCB_ascent/libradtran_pp_twostr_Baum_ghm.nc')\n",
+    "\n",
+    "ds_twostr_fu['lat'] = np.linspace(41,47,1686)\n",
+    "ds_twostr_baum['lat'] = np.linspace(41,47,1686)\n",
+    "\n",
+    "ds_twostr_fu = ds_twostr_fu.isel(time=4,lon=1050)\n",
+    "ds_twostr_baum = ds_twostr_baum.isel(time=4,lon=1050)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c5ddaaa4-33ff-491a-bcb3-b9fb7750525f",
+   "metadata": {},
+   "source": [
+    "### For data publication"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "bcb8562c-f8b0-4780-984c-77967fa01358",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# creating a dataset and save for data publication\n",
+    "ds_out = xr.Dataset(\n",
+    "    data_vars={\n",
+    "        \"swcrh_fu_dom02\"     : (ds_twostr_fu['swcrh'].dims, ds_twostr_fu['swcrh'].data),\n",
+    "        \"lwcrh_fu_dom02\"     : (ds_twostr_fu['lwcrh'].dims, ds_twostr_fu['lwcrh'].data),\n",
+    "        \"swcrh_baum_dom02\"     : (ds_twostr_baum['swcrh'].dims, ds_twostr_baum['swcrh'].data),\n",
+    "        \"lwcrh_baum_dom02\"     : (ds_twostr_baum['lwcrh'].dims, ds_twostr_baum['lwcrh'].data),\n",
+    "        \n",
+    "    },\n",
+    "    coords=ds_twostr_fu['swcrh'].coords)\n",
+    "ds_out = ds_out.assign(z_mc=ds_twostr_fu['z_mc'])\n",
+    "\n",
+    "ds_out.attrs['description'] = ' Cross-sections of CRH at hour 14:30 and lon 3° with ice optics of Fu and Baum_ghm in the WCB ascent region'\n",
+    "ds_out.to_netcdf('/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/postprocessed_data_for_publication/for_publication/figure7.nc')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "69ba8135-1543-4231-a841-bef5032b5e4d",
+   "metadata": {},
+   "source": [
+    "## 4- Plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "441946cb-f178-46b3-85bd-fdde5a0801af",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.514, 0.92, 'Fu scheme')"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x792 with 8 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(15, 11))\n",
+    "i = 0\n",
+    "\n",
+    "z = ds_twostr_fu['z_mc']\n",
+    "lat = ds_out['lat']\n",
+    "#lon = np.linspace(-1,5,2064)\n",
+    "\n",
+    "for ax in axes.flat:\n",
+    "    ax.tick_params(labelsize=14)\n",
+    "    ax.set_ylim([0,12])\n",
+    "    \n",
+    "    if i == 0:\n",
+    "        swcrh = ds_out['swcrh_fu_dom02']\n",
+    "        im0 = ax.pcolor(lat,z,swcrh.transpose(),cmap='seismic',vmin=-25,vmax=25)\n",
+    "        ax.set_title(' Shortwave CRH', fontsize=14)\n",
+    "        ax.set_xticklabels([])\n",
+    "        ax.set_ylabel('Height (km)', fontsize=14)\n",
+    "        ax.text(0.0, 1.03, '(a) ', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 1:\n",
+    "        lwcrh = ds_out['lwcrh_fu_dom02']\n",
+    "        im0 = ax.pcolor(lat,z,lwcrh.transpose(),cmap='seismic',vmin=-25,vmax=25)\n",
+    "        ax.set_title(' Longwave CRH', fontsize=14)\n",
+    "        ax.set_xticklabels([])\n",
+    "        ax.set_yticklabels([])\n",
+    "        ax.text(0.0, 1.03, '(b) ', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "        \n",
+    "    if i == 2:\n",
+    "        ntcrh = ds_out['swcrh_fu_dom02'] + ds_out['lwcrh_fu_dom02']\n",
+    "        im0 = ax.pcolor(lat,z,ntcrh.transpose(),cmap='seismic',vmin=-25,vmax=25)\n",
+    "        ax.set_title(' Net CRH', fontsize=14)\n",
+    "        ax.set_xticklabels([])\n",
+    "        ax.set_yticklabels([])\n",
+    "        ax.text(0.0, 1.03, '(c) ', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    ####################\n",
+    "    \n",
+    "    if i == 3:\n",
+    "        diffswcrh = ds_out['swcrh_fu_dom02'] - ds_out['swcrh_baum_dom02']\n",
+    "        im1 = ax.pcolor(lat,z,diffswcrh.transpose(),cmap='seismic',vmin=-5,vmax=5)\n",
+    "        ax.set_xlabel('Latitude', fontsize=14)\n",
+    "        ax.set_title(' Shortwave CRH difference', fontsize=14)\n",
+    "        ax.set_ylabel('Height (km)', fontsize=14)\n",
+    "        ax.text(0.0, 1.03, '(d) ', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 4:\n",
+    "        difflwcrh = ds_out['lwcrh_fu_dom02'] - ds_out['lwcrh_baum_dom02']\n",
+    "        im1 = ax.pcolor(lat,z,difflwcrh.transpose(),cmap='seismic',vmin=-5,vmax=5)\n",
+    "        ax.set_xlabel('Latitude', fontsize=14)\n",
+    "        ax.set_title(' Longwave CRH difference', fontsize=14)\n",
+    "        ax.set_yticklabels([])\n",
+    "        ax.text(0.0, 1.03, '(e) ', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "        \n",
+    "    if i == 5:\n",
+    "        ntcrh_fu   = ds_out['swcrh_fu_dom02'] + ds_out['lwcrh_fu_dom02']\n",
+    "        ntcrh_baum = ds_out['swcrh_baum_dom02'] + ds_out['lwcrh_baum_dom02']\n",
+    "        diffnt = ntcrh_fu - ntcrh_baum\n",
+    "        im1 = ax.pcolor(lat,z,diffnt.transpose(),cmap='seismic',vmin=-5,vmax=5)\n",
+    "        ax.set_xlabel('Latitude', fontsize=14)\n",
+    "        ax.set_title(' Net CRH difference', fontsize=14)\n",
+    "        ax.set_yticklabels([])\n",
+    "        ax.text(0.0, 1.03, '(f) ', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    i = i + 1\n",
+    "    \n",
+    "fig.subplots_adjust(wspace=0.1,hspace=0.25) \n",
+    "\n",
+    "cb_ax = fig.add_axes([0.92, 0.5425, 0.015, 0.335]) # xcenter/ycenter/width/height\n",
+    "cbar = fig.colorbar(im0,cax=cb_ax,orientation='vertical',shrink=0.95,extend='both')\n",
+    "cbar.set_label(label='K day$^{-1}$', size='13',labelpad=1)\n",
+    "cbar.ax.tick_params(labelsize=13)\n",
+    "\n",
+    "cb_ax = fig.add_axes([0.92, 0.125, 0.015, 0.335]) # xcenter/ycenter/width/height\n",
+    "cbar = fig.colorbar(im1,cax=cb_ax,orientation='vertical',shrink=0.95,extend='both')\n",
+    "cbar.set_label(label='K day$^{-1}$', size='13',labelpad=1)\n",
+    "cbar.ax.tick_params(labelsize=13)\n",
+    "\n",
+    "plt.text(.512, 0.505, 'Fu scheme - Baum scheme', transform=fig.transFigure, horizontalalignment='center',fontsize=17)\n",
+    "plt.text(.514, 0.92, 'Fu scheme', transform=fig.transFigure, horizontalalignment='center',fontsize=17)\n",
+    "\n",
+    "#plt.savefig('figure7.png', bbox_inches = 'tight',dpi=300)\n",
+    "#plt.savefig('figure7.pdf', format='pdf', bbox_inches='tight', dpi=300, compression=6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "38dee131-bdc4-4884-a59a-cc7003b1517c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pycrh",
+   "language": "python",
+   "name": "pycrh"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
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diff --git a/plots4paper/figure8.ipynb b/plots4paper/figure8.ipynb
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+++ b/plots4paper/figure8.ipynb
@@ -0,0 +1,562 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "115f93b6-8a4f-4f98-a0dc-2c79cceb1a7a",
+   "metadata": {},
+   "source": [
+    "# Figure 8\n",
+    "\n",
+    "**Figure 8.** Impact of ice-optical parameterization on time- and domain-averaged CRH in the three WCB regions. The CRH differences are\n",
+    "calculated between the radiative transfer calculations with the ice schemes of Fu and Baum (Fu - Baum). The differences between Fu and\n",
+    "Baum with general habit mixture (ghm), solid column (sc), and rough-aggregated (ra) habits are shown with solid lines, dashed lines, and\n",
+    "dotted lines, as is indicated in the legend.\n",
+    "\n",
+    "---\n",
+    "@ Behrooz Keshtgar, KIT 2024"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c69547d-1bf2-4b27-957a-95e15f48812c",
+   "metadata": {},
+   "source": [
+    "## 1- load python packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "039407e9-ae55-437b-8d36-b67a5aa76fa2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import xarray as xr\n",
+    "import pandas as pd\n",
+    "import colorlegend\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bf9aea5-6e6e-46c9-925d-ce53a9042b21",
+   "metadata": {},
+   "source": [
+    "For reference, print package versions to screen:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "090549dc-8a05-4a15-a43a-2bef20f073d4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "xarrary:    0.16.0\n",
+      "numpy:      1.23.5\n",
+      "matplotlib: 3.3.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('xarrary:   ', xr.__version__)\n",
+    "print('numpy:     ', np.__version__)\n",
+    "import matplotlib; print('matplotlib:', matplotlib.__version__); del matplotlib"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "05c4817c-8fda-4e9f-9f42-8b74b7af5adf",
+   "metadata": {},
+   "source": [
+    "**Since datasets are large, I use DASK to speed up my analysis**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "daa2683a-2fb1-43ba-ab6e-415dce04ee27",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table style=\"border: 2px solid white;\">\n",
+       "<tr>\n",
+       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
+       "<h3 style=\"text-align: left;\">Client</h3>\n",
+       "<ul style=\"text-align: left; list-style: none; margin: 0; padding: 0;\">\n",
+       "  <li><b>Scheduler: </b>tcp://127.0.0.1:42425</li>\n",
+       "  <li><b>Dashboard: </b><a href='/user/b381185/levante-spawner-preset//proxy/8787/status' target='_blank'>/user/b381185/levante-spawner-preset//proxy/8787/status</a></li>\n",
+       "</ul>\n",
+       "</td>\n",
+       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
+       "<h3 style=\"text-align: left;\">Cluster</h3>\n",
+       "<ul style=\"text-align: left; list-style:none; margin: 0; padding: 0;\">\n",
+       "  <li><b>Workers: </b>16</li>\n",
+       "  <li><b>Cores: </b>256</li>\n",
+       "  <li><b>Memory: </b>522.84 GB</li>\n",
+       "</ul>\n",
+       "</td>\n",
+       "</tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Client: 'tcp://127.0.0.1:42425' processes=16 threads=256, memory=522.84 GB>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import dask\n",
+    "from dask.distributed import Client, progress, wait\n",
+    "dask.config.config.get('distributed').get('dashboard').update({'link':'{JUPYTERHUB_SERVICE_PREFIX}/proxy/{port}/status'})\n",
+    "client = Client()\n",
+    "client"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1f71995-2a2b-49a4-9ab3-c56a389a5964",
+   "metadata": {},
+   "source": [
+    "## 2- Loading datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d8792fad-2a58-45e3-86a9-eac4d7cdeff8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Dictionary for loading datasets for the 4 LEM domains\n",
+    "domdict = {\n",
+    "         'WCB_ascent'               : {'res':'300m'}, \n",
+    "         'WCB_cyclonic_outflow'     : {'res':'300m'}, \n",
+    "         'WCB_anticyclonic_outflow' : {'res':'300m'}\n",
+    "          }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "bfff1bc1-4eed-4ddc-8e71-fa5ef73eebc6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Working on loading data for WCB_ascent\n",
+      "Working on loading data for WCB_cyclonic_outflow\n",
+      "Working on loading data for WCB_anticyclonic_outflow\n",
+      "Working on loading data for WCB_ascent\n",
+      "Working on loading data for WCB_cyclonic_outflow\n",
+      "Working on loading data for WCB_anticyclonic_outflow\n",
+      "Working on loading data for WCB_ascent\n",
+      "Working on loading data for WCB_cyclonic_outflow\n",
+      "Working on loading data for WCB_anticyclonic_outflow\n",
+      "Working on loading data for WCB_ascent\n",
+      "Working on loading data for WCB_cyclonic_outflow\n",
+      "Working on loading data for WCB_anticyclonic_outflow\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Loading icon-pp datasets\n",
+    "def load_data(optic):\n",
+    "    list_icon = []\n",
+    "    for dom in list(domdict.keys()):\n",
+    "        path = '/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/postprocessed_data_for_publication/'\n",
+    "        print('Working on loading data for', dom)\n",
+    "        ds = xr.open_dataset(path+dom+'/libradtran_pp_twostr_'+optic+'.nc').chunk(chunks={'time': 1, 'height': 10})\n",
+    "        list_icon.append(ds)\n",
+    "    return list_icon\n",
+    "#-------------------------------------------------------------------------------------------------\n",
+    "list_libradtran_fu       = load_data('Fu')\n",
+    "list_libradtran_baum_ghm = load_data('Baum_ghm')\n",
+    "list_libradtran_baum_sc  = load_data('Baum_sc')\n",
+    "list_libradtran_baum_rg  = load_data('Baum_rg')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aa998ccb-3c0e-4f92-9cc5-5d35f659f60b",
+   "metadata": {},
+   "source": [
+    "## 3- Average profiles of CRH"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "cea78232-b730-4039-8ded-a9fdd3f8adce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# excluding boundaries and takin domain average\n",
+    "# twostr with Fu, Baum (ghm, sc, rg) datasets\n",
+    "for dom in range(len(list_libradtran_fu)):\n",
+    "    for var in ['lwcrh','swcrh']:\n",
+    "        list_libradtran_fu[dom][var+'_mean'] = list_libradtran_fu[dom][var].isel(lon=slice(10,list_libradtran_fu[dom].lon.size-10),lat=slice(5,list_libradtran_fu[dom].lat.size-5)).mean(dim=['lat','lon','time']).compute()\n",
+    "        list_libradtran_baum_ghm[dom][var+'_mean'] = list_libradtran_baum_ghm[dom][var].isel(lon=slice(10,list_libradtran_baum_ghm[dom].lon.size-10),lat=slice(5,list_libradtran_baum_ghm[dom].lat.size-5)).mean(dim=['lat','lon','time']).compute()\n",
+    "        list_libradtran_baum_sc[dom][var+'_mean'] = list_libradtran_baum_sc[dom][var].isel(lon=slice(10,list_libradtran_baum_sc[dom].lon.size-10),lat=slice(5,list_libradtran_baum_sc[dom].lat.size-5)).mean(dim=['lat','lon','time']).compute()\n",
+    "        list_libradtran_baum_rg[dom][var+'_mean'] = list_libradtran_baum_rg[dom][var].isel(lon=slice(10,list_libradtran_baum_rg[dom].lon.size-10),lat=slice(5,list_libradtran_baum_rg[dom].lat.size-5)).mean(dim=['lat','lon','time']).compute()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c5ddaaa4-33ff-491a-bcb3-b9fb7750525f",
+   "metadata": {},
+   "source": [
+    "### For data publication"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "efa0e678-319b-43a8-9247-6fd6ca7f64e7",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# creating a dataset and save for data publication\n",
+    "ds_out = xr.Dataset(\n",
+    "    data_vars={\n",
+    "        \"swcrh_mean_fu_dom02\"     : (list_libradtran_fu[0]['swcrh_mean'].dims, list_libradtran_fu[0]['swcrh_mean'].data),\n",
+    "        \"lwcrh_mean_fu_dom02\"     : (list_libradtran_fu[0]['lwcrh_mean'].dims, list_libradtran_fu[0]['lwcrh_mean'].data),\n",
+    "        \"swcrh_mean_Baum_ghm_dom02\" : (list_libradtran_baum_ghm[0]['swcrh_mean'].dims, list_libradtran_baum_ghm[0]['swcrh_mean'].data),\n",
+    "        \"lwcrh_mean_Baum_ghm_dom02\" : (list_libradtran_baum_ghm[0]['lwcrh_mean'].dims, list_libradtran_baum_ghm[0]['lwcrh_mean'].data),\n",
+    "        \"swcrh_mean_Baum_sc_dom02\" : (list_libradtran_baum_sc[0]['swcrh_mean'].dims, list_libradtran_baum_sc[0]['swcrh_mean'].data),\n",
+    "        \"lwcrh_mean_Baum_sc_dom02\" : (list_libradtran_baum_sc[0]['lwcrh_mean'].dims, list_libradtran_baum_sc[0]['lwcrh_mean'].data),\n",
+    "        \"swcrh_mean_Baum_rg_dom02\" : (list_libradtran_baum_rg[0]['swcrh_mean'].dims, list_libradtran_baum_rg[0]['swcrh_mean'].data),\n",
+    "        \"lwcrh_mean_Baum_rg_dom02\" : (list_libradtran_baum_rg[0]['lwcrh_mean'].dims, list_libradtran_baum_rg[0]['lwcrh_mean'].data),\n",
+    "        \n",
+    "        \"swcrh_mean_fu_dom03\"     : (list_libradtran_fu[1]['swcrh_mean'].dims, list_libradtran_fu[1]['swcrh_mean'].data),\n",
+    "        \"lwcrh_mean_fu_dom03\"     : (list_libradtran_fu[1]['lwcrh_mean'].dims, list_libradtran_fu[1]['lwcrh_mean'].data),\n",
+    "        \"swcrh_mean_Baum_ghm_dom03\" : (list_libradtran_baum_ghm[1]['swcrh_mean'].dims, list_libradtran_baum_ghm[1]['swcrh_mean'].data),\n",
+    "        \"lwcrh_mean_Baum_ghm_dom03\" : (list_libradtran_baum_ghm[1]['lwcrh_mean'].dims, list_libradtran_baum_ghm[1]['lwcrh_mean'].data),\n",
+    "        \"swcrh_mean_Baum_sc_dom03\" : (list_libradtran_baum_sc[1]['swcrh_mean'].dims, list_libradtran_baum_sc[1]['swcrh_mean'].data),\n",
+    "        \"lwcrh_mean_Baum_sc_dom03\" : (list_libradtran_baum_sc[1]['lwcrh_mean'].dims, list_libradtran_baum_sc[1]['lwcrh_mean'].data),\n",
+    "        \"swcrh_mean_Baum_rg_dom03\" : (list_libradtran_baum_rg[1]['swcrh_mean'].dims, list_libradtran_baum_rg[1]['swcrh_mean'].data),\n",
+    "        \"lwcrh_mean_Baum_rg_dom03\" : (list_libradtran_baum_rg[1]['lwcrh_mean'].dims, list_libradtran_baum_rg[1]['lwcrh_mean'].data),\n",
+    "        \n",
+    "        \"swcrh_mean_fu_dom04\"     : (list_libradtran_fu[2]['swcrh_mean'].dims, list_libradtran_fu[2]['swcrh_mean'].data),\n",
+    "        \"lwcrh_mean_fu_dom04\"     : (list_libradtran_fu[2]['lwcrh_mean'].dims, list_libradtran_fu[2]['lwcrh_mean'].data),\n",
+    "        \"swcrh_mean_Baum_ghm_dom04\" : (list_libradtran_baum_ghm[2]['swcrh_mean'].dims, list_libradtran_baum_ghm[2]['swcrh_mean'].data),\n",
+    "        \"lwcrh_mean_Baum_ghm_dom04\" : (list_libradtran_baum_ghm[2]['lwcrh_mean'].dims, list_libradtran_baum_ghm[2]['lwcrh_mean'].data),\n",
+    "        \"swcrh_mean_Baum_sc_dom04\" : (list_libradtran_baum_sc[2]['swcrh_mean'].dims, list_libradtran_baum_sc[2]['swcrh_mean'].data),\n",
+    "        \"lwcrh_mean_Baum_sc_dom04\" : (list_libradtran_baum_sc[2]['lwcrh_mean'].dims, list_libradtran_baum_sc[2]['lwcrh_mean'].data),\n",
+    "        \"swcrh_mean_Baum_rg_dom04\" : (list_libradtran_baum_rg[2]['swcrh_mean'].dims, list_libradtran_baum_rg[2]['swcrh_mean'].data),\n",
+    "        \"lwcrh_mean_Baum_rg_dom04\" : (list_libradtran_baum_rg[2]['lwcrh_mean'].dims, list_libradtran_baum_rg[2]['lwcrh_mean'].data), \n",
+    "    },\n",
+    "    coords=list_libradtran_fu[0]['swcrh_mean'].coords)\n",
+    "ds_out = ds_out.assign(z_mc=list_libradtran_fu[0]['z_mc'])\n",
+    "\n",
+    "ds_out.attrs['description'] = 'Vertical profiles of CRH from twostr radiation calculations with different ice-optics for each LEM domain'\n",
+    "ds_out.to_netcdf('/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/postprocessed_data_for_publication/for_publication/figure8.nc')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "69ba8135-1543-4231-a841-bef5032b5e4d",
+   "metadata": {},
+   "source": [
+    "## 4- Plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "f4580285-54e4-4aa7-8d1b-dbc922415c30",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x720 with 6 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot\n",
+    "fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(15, 10))\n",
+    "i = 0\n",
+    "\n",
+    "z = ds_out['z_mc']\n",
+    "\n",
+    "for ax in axes.flat:\n",
+    "    ax.tick_params(labelsize=14)\n",
+    "    ax.spines['top'].set_color('none')\n",
+    "    ax.spines['right'].set_color('none')\n",
+    "    ax.spines['left'].set_color('none')\n",
+    "    \n",
+    "    if i == 0:\n",
+    "        # SW diff\n",
+    "        difflw_1 = ds_out['lwcrh_mean_fu_dom02'].values - ds_out['lwcrh_mean_Baum_ghm_dom02'].values\n",
+    "        difflw_2 = ds_out['lwcrh_mean_fu_dom02'].values - ds_out['lwcrh_mean_Baum_sc_dom02'].values\n",
+    "        difflw_3 = ds_out['lwcrh_mean_fu_dom02'].values - ds_out['lwcrh_mean_Baum_rg_dom02'].values\n",
+    "        # LW diff\n",
+    "        diffsw_1 = ds_out['swcrh_mean_fu_dom02'].values - ds_out['swcrh_mean_Baum_ghm_dom02'].values\n",
+    "        diffsw_2 = ds_out['swcrh_mean_fu_dom02'].values - ds_out['swcrh_mean_Baum_sc_dom02'].values\n",
+    "        diffsw_3 = ds_out['swcrh_mean_fu_dom02'].values - ds_out['swcrh_mean_Baum_rg_dom02'].values\n",
+    "        \n",
+    "        \n",
+    "        ax.plot(diffsw_1,z,color='#e6194B',lw=3)\n",
+    "        ax.plot(diffsw_2,z,color='#e6194B',linestyle='dashed',lw=3)\n",
+    "        ax.plot(diffsw_3,z,color='#e6194B',linestyle='dotted',lw=3)\n",
+    "\n",
+    "        ax.plot(difflw_1,z,color='#4363d8',lw=3)\n",
+    "        ax.plot(difflw_2,z,color='#4363d8',linestyle='dashed',lw=3)\n",
+    "        ax.plot(difflw_3,z,color='#4363d8',linestyle='dotted',lw=3)\n",
+    "        \n",
+    "        ax.axvline(x=0, ymin=0.0, ymax=1,c='black', lw=1)\n",
+    "        ax.set_title('WCB ascent', fontsize=15,pad=15)\n",
+    "        ax.set_ylabel('Height (km)', fontsize=14)\n",
+    "        ax.set_xlabel('CRH difference (K day$^{-1}$)', fontsize=14)#,labelpad=10)\n",
+    "        ax.text(0.0, 1.06, '(a)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "        ax.set_ylim([0,12])\n",
+    "        \n",
+    "        ax.spines['bottom'].set_bounds(-0.4,0.4)\n",
+    "        ax.set_xticks(np.linspace(-0.4,0.4,5))\n",
+    "        ax.set_xlim([-0.42,0.4])\n",
+    "    \n",
+    "    if i == 1:\n",
+    "        # SW diff\n",
+    "        difflw_1 = ds_out['lwcrh_mean_fu_dom03'].values - ds_out['lwcrh_mean_Baum_ghm_dom03'].values\n",
+    "        difflw_2 = ds_out['lwcrh_mean_fu_dom03'].values - ds_out['lwcrh_mean_Baum_sc_dom03'].values\n",
+    "        difflw_3 = ds_out['lwcrh_mean_fu_dom03'].values - ds_out['lwcrh_mean_Baum_rg_dom03'].values\n",
+    "        # LW diff\n",
+    "        diffsw_1 = ds_out['swcrh_mean_fu_dom03'].values - ds_out['swcrh_mean_Baum_ghm_dom03'].values\n",
+    "        diffsw_2 = ds_out['swcrh_mean_fu_dom03'].values - ds_out['swcrh_mean_Baum_sc_dom03'].values\n",
+    "        diffsw_3 = ds_out['swcrh_mean_fu_dom03'].values - ds_out['swcrh_mean_Baum_rg_dom03'].values\n",
+    "        \n",
+    "        ax.plot(diffsw_1,z,color='#e6194B',lw=3)\n",
+    "        ax.plot(diffsw_2,z,color='#e6194B',linestyle='dashed',lw=3)\n",
+    "        ax.plot(diffsw_3,z,color='#e6194B',linestyle='dotted',lw=3)\n",
+    "\n",
+    "        ax.plot(difflw_1,z,color='#4363d8',lw=3)\n",
+    "        ax.plot(difflw_2,z,color='#4363d8',linestyle='dashed',lw=3)\n",
+    "        ax.plot(difflw_3,z,color='#4363d8',linestyle='dotted',lw=3)\n",
+    "\n",
+    "        \n",
+    "        ax.axvline(x=0, ymin=0.0, ymax=1,c='black', lw=1)\n",
+    "        ax.set_title('WCB cyclonic outflow', fontsize=15,pad=15)\n",
+    "        ax.set_xlabel('CRH difference (K day$^{-1}$)', fontsize=14)\n",
+    "        ax.text(0.0, 1.06, '(c)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "        ax.set_ylim([0,12])\n",
+    "        ax.set_yticklabels([])\n",
+    "        \n",
+    "        ax.spines['bottom'].set_bounds(-0.4,0.4)\n",
+    "        ax.set_xticks(np.linspace(-0.4,0.4,5))\n",
+    "        ax.set_xlim([-0.42,0.4])\n",
+    "        \n",
+    "        ax.text(0.03, 11.8, \"Shortwave\", color=\"#e6194B\", size=14, ha=\"left\", va=\"top\")\n",
+    "        ax.text(0.03, 11, \"Longwave\", color='#4363d8', size=14, ha=\"left\", va=\"top\")\n",
+    "        #ax.text(0.03, 10.25, \"Total\", color='#000000', size=14, va=\"top\",alpha=0.5)\n",
+    "        ax.text(0.03, 10.2, \"solid: Fu - Baum_ghm\", color=\"gray\", size=14, va=\"top\")\n",
+    "        ax.text(0.03, 9.4, \"dashed: Fu - Baum_sc\", color=\"gray\", size=14, va=\"top\")\n",
+    "        ax.text(0.03, 8.6, \"dotted: Fu - Baum_ra\", color=\"gray\", size=14, va=\"top\")\n",
+    "        \n",
+    "    if i == 2:\n",
+    "        \n",
+    "        # SW diff\n",
+    "        difflw_1 = ds_out['lwcrh_mean_fu_dom04'].values - ds_out['lwcrh_mean_Baum_ghm_dom04'].values\n",
+    "        difflw_2 = ds_out['lwcrh_mean_fu_dom04'].values - ds_out['lwcrh_mean_Baum_sc_dom04'].values\n",
+    "        difflw_3 = ds_out['lwcrh_mean_fu_dom04'].values - ds_out['lwcrh_mean_Baum_rg_dom04'].values\n",
+    "        # LW diff\n",
+    "        diffsw_1 = ds_out['swcrh_mean_fu_dom04'].values - ds_out['swcrh_mean_Baum_ghm_dom04'].values\n",
+    "        diffsw_2 = ds_out['swcrh_mean_fu_dom04'].values - ds_out['swcrh_mean_Baum_sc_dom04'].values\n",
+    "        diffsw_3 = ds_out['swcrh_mean_fu_dom04'].values - ds_out['swcrh_mean_Baum_rg_dom04'].values\n",
+    "        \n",
+    "        ax.plot(diffsw_1,z,color='#e6194B',lw=3)\n",
+    "        ax.plot(diffsw_2,z,color='#e6194B',linestyle='dashed',lw=3)\n",
+    "        ax.plot(diffsw_3,z,color='#e6194B',linestyle='dotted',lw=3)\n",
+    "\n",
+    "        ax.plot(difflw_1,z,color='#4363d8',lw=3)\n",
+    "        ax.plot(difflw_2,z,color='#4363d8',linestyle='dashed',lw=3)\n",
+    "        ax.plot(difflw_3,z,color='#4363d8',linestyle='dotted',lw=3)\n",
+    "\n",
+    "        \n",
+    "        ax.axvline(x=0, ymin=0.0, ymax=1,c='black', lw=1)\n",
+    "        ax.set_title('WCB anticyclonic outflow', fontsize=15,pad=15)\n",
+    "        ax.set_xlabel('CRH difference (K day$^{-1}$)', fontsize=14)\n",
+    "        ax.text(0.0, 1.06, '(e)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "        ax.set_ylim([0,12])\n",
+    "        ax.set_yticklabels([])\n",
+    "        \n",
+    "        ax.spines['bottom'].set_bounds(-0.4,0.4)\n",
+    "        ax.set_xticks(np.linspace(-0.4,0.4,5))\n",
+    "        ax.set_xlim([-0.42,0.4])\n",
+    "        \n",
+    "        \n",
+    "    if i == 3:\n",
+    "        # SW diff\n",
+    "        difflw_1 = ds_out['lwcrh_mean_fu_dom02'].values - ds_out['lwcrh_mean_Baum_ghm_dom02'].values\n",
+    "        difflw_2 = ds_out['lwcrh_mean_fu_dom02'].values - ds_out['lwcrh_mean_Baum_sc_dom02'].values\n",
+    "        difflw_3 = ds_out['lwcrh_mean_fu_dom02'].values - ds_out['lwcrh_mean_Baum_rg_dom02'].values\n",
+    "        # LW diff\n",
+    "        diffsw_1 = ds_out['swcrh_mean_fu_dom02'].values - ds_out['swcrh_mean_Baum_ghm_dom02'].values\n",
+    "        diffsw_2 = ds_out['swcrh_mean_fu_dom02'].values - ds_out['swcrh_mean_Baum_sc_dom02'].values\n",
+    "        diffsw_3 = ds_out['swcrh_mean_fu_dom02'].values - ds_out['swcrh_mean_Baum_rg_dom02'].values\n",
+    "        \n",
+    "        # net diff\n",
+    "        diffnt_1 = difflw_1 + diffsw_1\n",
+    "        diffnt_2 = difflw_2 + diffsw_2\n",
+    "        diffnt_3 = difflw_3 + diffsw_3\n",
+    "        \n",
+    "        ax.plot(diffnt_1,z,color='#000000',label='net',lw=3)\n",
+    "        ax.plot(diffnt_2,z,color='#000000',linestyle='dashed',label='net',lw=3)\n",
+    "        ax.plot(diffnt_3,z,color='#000000',linestyle='dotted',label='net',lw=3)\n",
+    "        \n",
+    "        ax.axvline(x=0, ymin=0.0, ymax=1,c='black', lw=1)\n",
+    "        ax.set_ylabel('Height (km)', fontsize=14)\n",
+    "        ax.set_xlabel('CRH difference (K day$^{-1}$)', fontsize=14)#,labelpad=10)\n",
+    "        ax.text(0.0, 1.06, '(b)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "        ax.set_ylim([0,12])\n",
+    "        \n",
+    "        ax.spines['bottom'].set_bounds(-0.4,0.4)\n",
+    "        ax.set_xticks(np.linspace(-0.4,0.4,5))\n",
+    "        ax.set_xlim([-0.42,0.4])\n",
+    "        \n",
+    "    if i == 4:\n",
+    "\n",
+    "        # SW diff\n",
+    "        difflw_1 = ds_out['lwcrh_mean_fu_dom03'].values - ds_out['lwcrh_mean_Baum_ghm_dom03'].values\n",
+    "        difflw_2 = ds_out['lwcrh_mean_fu_dom03'].values - ds_out['lwcrh_mean_Baum_sc_dom03'].values\n",
+    "        difflw_3 = ds_out['lwcrh_mean_fu_dom03'].values - ds_out['lwcrh_mean_Baum_rg_dom03'].values\n",
+    "        # LW diff\n",
+    "        diffsw_1 = ds_out['swcrh_mean_fu_dom03'].values - ds_out['swcrh_mean_Baum_ghm_dom03'].values\n",
+    "        diffsw_2 = ds_out['swcrh_mean_fu_dom03'].values - ds_out['swcrh_mean_Baum_sc_dom03'].values\n",
+    "        diffsw_3 = ds_out['swcrh_mean_fu_dom03'].values - ds_out['swcrh_mean_Baum_rg_dom03'].values\n",
+    "        \n",
+    "        # net diff\n",
+    "        diffnt_1 = difflw_1 + diffsw_1\n",
+    "        diffnt_2 = difflw_2 + diffsw_2\n",
+    "        diffnt_3 = difflw_3 + diffsw_3\n",
+    "        \n",
+    "        ax.plot(diffnt_1,z,color='#000000',label='net',lw=3)\n",
+    "        ax.plot(diffnt_2,z,color='#000000',linestyle='dashed',label='net',lw=3)\n",
+    "        ax.plot(diffnt_3,z,color='#000000',linestyle='dotted',label='net',lw=3)\n",
+    "        \n",
+    "        ax.axvline(x=0, ymin=0.0, ymax=1,c='black', lw=1)\n",
+    "        ax.set_xlabel('CRH difference (K day$^{-1}$)', fontsize=14)#,labelpad=10)\n",
+    "        ax.text(0.0, 1.06, '(d)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        ax.set_yticklabels([])\n",
+    "        \n",
+    "        ax.set_ylim([0,12])\n",
+    "        ax.text(0.03, 11.8, \"Net\", color=\"#000000\", size=14, ha=\"left\", va=\"top\")\n",
+    "        \n",
+    "        ax.spines['bottom'].set_bounds(-0.4,0.4)\n",
+    "        ax.set_xticks(np.linspace(-0.4,0.4,5))\n",
+    "        ax.set_xlim([-0.42,0.4])\n",
+    "        \n",
+    "    if i == 5:\n",
+    "        \n",
+    "        # SW diff\n",
+    "        difflw_1 = ds_out['lwcrh_mean_fu_dom04'].values - ds_out['lwcrh_mean_Baum_ghm_dom04'].values\n",
+    "        difflw_2 = ds_out['lwcrh_mean_fu_dom04'].values - ds_out['lwcrh_mean_Baum_sc_dom04'].values\n",
+    "        difflw_3 = ds_out['lwcrh_mean_fu_dom04'].values - ds_out['lwcrh_mean_Baum_rg_dom04'].values\n",
+    "        # LW diff\n",
+    "        diffsw_1 = ds_out['swcrh_mean_fu_dom04'].values - ds_out['swcrh_mean_Baum_ghm_dom04'].values\n",
+    "        diffsw_2 = ds_out['swcrh_mean_fu_dom04'].values - ds_out['swcrh_mean_Baum_sc_dom04'].values\n",
+    "        diffsw_3 = ds_out['swcrh_mean_fu_dom04'].values - ds_out['swcrh_mean_Baum_rg_dom04'].values\n",
+    "        \n",
+    "        # net diff\n",
+    "        diffnt_1 = difflw_1 + diffsw_1\n",
+    "        diffnt_2 = difflw_2 + diffsw_2\n",
+    "        diffnt_3 = difflw_3 + diffsw_3\n",
+    "        \n",
+    "        ax.plot(diffnt_1,z,color='#000000',label='net',lw=3)\n",
+    "        ax.plot(diffnt_2,z,color='#000000',linestyle='dashed',label='net',lw=3)\n",
+    "        ax.plot(diffnt_3,z,color='#000000',linestyle='dotted',label='net',lw=3)\n",
+    "\n",
+    "        ax.axvline(x=0, ymin=0.0, ymax=1,c='black', lw=1)\n",
+    "        ax.set_xlabel('CRH difference (K day$^{-1}$)', fontsize=14)#,labelpad=10)\n",
+    "        ax.text(0.0, 1.06, '(f)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        ax.set_yticklabels([])\n",
+    "        \n",
+    "        ax.set_ylim([0,12])\n",
+    "        \n",
+    "        ax.spines['bottom'].set_bounds(-0.4,0.4)\n",
+    "        ax.set_xticks(np.linspace(-0.4,0.4,5))\n",
+    "        ax.set_xlim([-0.42,0.4])    \n",
+    "        \n",
+    "    i = i + 1    \n",
+    "    \n",
+    "fig.subplots_adjust(wspace=0.2,hspace=0.35) \n",
+    "\n",
+    "plt.savefig('figure8.pdf', bbox_inches = 'tight')\n",
+    "#plt.savefig('figure8.png', bbox_inches = 'tight',dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "9e1b9f26-6d8f-4acb-808d-cad4f65e87cf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "client.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "38dee131-bdc4-4884-a59a-cc7003b1517c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pycrh",
+   "language": "python",
+   "name": "pycrh"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/plots4paper/figure8.pdf b/plots4paper/figure8.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..e4c3bd343badb86fc4d62391c47f3a5b45a1c1da
Binary files /dev/null and b/plots4paper/figure8.pdf differ
diff --git a/plots4paper/figure9.ipynb b/plots4paper/figure9.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..d719e4153ca045e8ab1c9fb337fb12713a6c00b2
--- /dev/null
+++ b/plots4paper/figure9.ipynb
@@ -0,0 +1,899 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "115f93b6-8a4f-4f98-a0dc-2c79cceb1a7a",
+   "metadata": {},
+   "source": [
+    "# Figure 9\n",
+    "\n",
+    "**Figure 9.** Impact of cloud horizontal heterogeneity and vertical overlap on CRH that are not resolved at 2.5 km horizontal resolution. The CRH differences are calculated between the LEM reference calculation and the radiative transfer calculations for the homogeneous NWP clouds (NWP - LEM), with the solid lines for the homogeneous grid-box clouds (without overlap assumption) and the dotted lines for the homogeneous clouds with fractional cloud cover (with overlap assumption). Note the different x and y-axes for panels (a) and (b).\n",
+    "\n",
+    "---\n",
+    "@ Behrooz Keshtgar, KIT 2024"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c69547d-1bf2-4b27-957a-95e15f48812c",
+   "metadata": {},
+   "source": [
+    "## 1- load python packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "039407e9-ae55-437b-8d36-b67a5aa76fa2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import xarray as xr\n",
+    "import pandas as pd\n",
+    "import colorlegend\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bf9aea5-6e6e-46c9-925d-ce53a9042b21",
+   "metadata": {},
+   "source": [
+    "For reference, print package versions to screen:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "090549dc-8a05-4a15-a43a-2bef20f073d4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "xarrary:    0.16.0\n",
+      "numpy:      1.23.5\n",
+      "matplotlib: 3.3.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('xarrary:   ', xr.__version__)\n",
+    "print('numpy:     ', np.__version__)\n",
+    "import matplotlib; print('matplotlib:', matplotlib.__version__); del matplotlib"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "8eef188e-ec34-44e6-bd4b-7672452f9f99",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table style=\"border: 2px solid white;\">\n",
+       "<tr>\n",
+       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
+       "<h3 style=\"text-align: left;\">Client</h3>\n",
+       "<ul style=\"text-align: left; list-style: none; margin: 0; padding: 0;\">\n",
+       "  <li><b>Scheduler: </b>tcp://127.0.0.1:42777</li>\n",
+       "  <li><b>Dashboard: </b><a href='/user/b381185/levante-spawner-preset//proxy/8787/status' target='_blank'>/user/b381185/levante-spawner-preset//proxy/8787/status</a></li>\n",
+       "</ul>\n",
+       "</td>\n",
+       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
+       "<h3 style=\"text-align: left;\">Cluster</h3>\n",
+       "<ul style=\"text-align: left; list-style:none; margin: 0; padding: 0;\">\n",
+       "  <li><b>Workers: </b>16</li>\n",
+       "  <li><b>Cores: </b>256</li>\n",
+       "  <li><b>Memory: </b>522.84 GB</li>\n",
+       "</ul>\n",
+       "</td>\n",
+       "</tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Client: 'tcp://127.0.0.1:42777' processes=16 threads=256, memory=522.84 GB>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import dask\n",
+    "from dask.distributed import Client, progress, wait\n",
+    "dask.config.config.get('distributed').get('dashboard').update({'link':'{JUPYTERHUB_SERVICE_PREFIX}/proxy/{port}/status'})\n",
+    "client = Client()\n",
+    "client"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1f71995-2a2b-49a4-9ab3-c56a389a5964",
+   "metadata": {},
+   "source": [
+    "## 2- Loading datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "d8792fad-2a58-45e3-86a9-eac4d7cdeff8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Dictionary for loading datasets for the 4 LEM domains\n",
+    "domdict = {\n",
+    "         'shallow_cumulus'          : {'res':'300m'}, \n",
+    "         'WCB_ascent'               : {'res':'300m'}, \n",
+    "         'WCB_cyclonic_outflow'     : {'res':'300m'}, \n",
+    "         'WCB_anticyclonic_outflow' : {'res':'300m'}\n",
+    "          }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "bfff1bc1-4eed-4ddc-8e71-fa5ef73eebc6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Working on loading data for shallow_cumulus\n",
+      "Working on loading data for WCB_ascent\n",
+      "Working on loading data for WCB_cyclonic_outflow\n",
+      "Working on loading data for WCB_anticyclonic_outflow\n",
+      "Working on loading data for shallow_cumulus\n",
+      "Working on loading data for WCB_ascent\n",
+      "Working on loading data for WCB_cyclonic_outflow\n",
+      "Working on loading data for WCB_anticyclonic_outflow\n",
+      "Working on loading data for shallow_cumulus\n",
+      "Working on loading data for WCB_ascent\n",
+      "Working on loading data for WCB_cyclonic_outflow\n",
+      "Working on loading data for WCB_anticyclonic_outflow\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Loading icon-pp datasets\n",
+    "def load_data(cloud):\n",
+    "    list_icon = []\n",
+    "    for dom in list(domdict.keys()):\n",
+    "        path = '/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/postprocessed_data_for_publication/'\n",
+    "        print('Working on loading data for', dom)\n",
+    "        ds = xr.open_dataset(path+dom+'/libradtran_pp'+cloud+'twostr_Fu.nc').chunk(chunks={'time': 1, 'height': 10})\n",
+    "        list_icon.append(ds)\n",
+    "    return list_icon\n",
+    "#-------------------------------------------------------------------------------------------------\n",
+    "list_libradtran_lem   = load_data('_')\n",
+    "list_libradtran_nwp   = load_data('_nwpcld_')\n",
+    "list_libradtran_nwpfr = load_data('_nwpfrcld_')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aa998ccb-3c0e-4f92-9cc5-5d35f659f60b",
+   "metadata": {},
+   "source": [
+    "## 3- Average profiles of CRH"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "cea78232-b730-4039-8ded-a9fdd3f8adce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# excluding boundaries and takin domain average\n",
+    "# twostr with LEM and NWP clouds datasets\n",
+    "for dom in range(len(list_libradtran_lem)):\n",
+    "    for var in ['lwcrh','swcrh']:\n",
+    "        list_libradtran_lem[dom][var+'_mean'] = list_libradtran_lem[dom][var].isel(lon=slice(10,list_libradtran_lem[dom].lon.size-10),lat=slice(5,list_libradtran_lem[dom].lat.size-5)).mean(dim=['lat','lon','time']).compute()\n",
+    "        list_libradtran_nwp[dom][var+'_mean'] = list_libradtran_nwp[dom][var].isel(lon=slice(1,list_libradtran_nwp[dom].lon.size-1),lat=slice(1,list_libradtran_nwp[dom].lat.size-1)).mean(dim=['lat','lon','time']).compute()\n",
+    "        list_libradtran_nwpfr[dom][var+'_mean'] = list_libradtran_nwpfr[dom][var].isel(lon=slice(1,list_libradtran_nwpfr[dom].lon.size-1),lat=slice(1,list_libradtran_nwpfr[dom].lat.size-1)).mean(dim=['lat','lon','time']).compute()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "589b99ac-f0a8-4d14-8619-42d288f07be8",
+   "metadata": {},
+   "source": [
+    "### For data publication"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "c302e134-d079-4592-89b2-886b97cd368d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# creating a dataset and save for data publication\n",
+    "ds_out = xr.Dataset(\n",
+    "    data_vars={\n",
+    "        \"swcrh_mean_lem_dom01\"     : (list_libradtran_lem[0]['swcrh_mean'].dims, list_libradtran_lem[0]['swcrh_mean'].data),\n",
+    "        \"lwcrh_mean_lem_dom01\"     : (list_libradtran_lem[0]['lwcrh_mean'].dims, list_libradtran_lem[0]['lwcrh_mean'].data),\n",
+    "        \"swcrh_mean_nwp_dom01\"     : (list_libradtran_nwp[0]['swcrh_mean'].dims, list_libradtran_nwp[0]['swcrh_mean'].data),\n",
+    "        \"lwcrh_mean_nwp_dom01\"     : (list_libradtran_nwp[0]['lwcrh_mean'].dims, list_libradtran_nwp[0]['lwcrh_mean'].data),\n",
+    "        \"swcrh_mean_nwpfr_dom01\" : (list_libradtran_nwpfr[0]['swcrh_mean'].dims, list_libradtran_nwpfr[0]['swcrh_mean'].data),\n",
+    "        \"lwcrh_mean_nwpfr_dom01\" : (list_libradtran_nwpfr[0]['lwcrh_mean'].dims, list_libradtran_nwpfr[0]['lwcrh_mean'].data),\n",
+    "        \n",
+    "        \"swcrh_mean_lem_dom02\"     : (list_libradtran_lem[1]['swcrh_mean'].dims, list_libradtran_lem[1]['swcrh_mean'].data),\n",
+    "        \"lwcrh_mean_lem_dom02\"     : (list_libradtran_lem[1]['lwcrh_mean'].dims, list_libradtran_lem[1]['lwcrh_mean'].data),\n",
+    "        \"swcrh_mean_nwp_dom02\"     : (list_libradtran_nwp[1]['swcrh_mean'].dims, list_libradtran_nwp[1]['swcrh_mean'].data),\n",
+    "        \"lwcrh_mean_nwp_dom02\"     : (list_libradtran_nwp[1]['lwcrh_mean'].dims, list_libradtran_nwp[1]['lwcrh_mean'].data),\n",
+    "        \"swcrh_mean_nwpfr_dom02\" : (list_libradtran_nwpfr[1]['swcrh_mean'].dims, list_libradtran_nwpfr[1]['swcrh_mean'].data),\n",
+    "        \"lwcrh_mean_nwpfr_dom02\" : (list_libradtran_nwpfr[1]['lwcrh_mean'].dims, list_libradtran_nwpfr[1]['lwcrh_mean'].data),\n",
+    "        \n",
+    "        \"swcrh_mean_lem_dom03\"     : (list_libradtran_lem[2]['swcrh_mean'].dims, list_libradtran_lem[2]['swcrh_mean'].data),\n",
+    "        \"lwcrh_mean_lem_dom03\"     : (list_libradtran_lem[2]['lwcrh_mean'].dims, list_libradtran_lem[2]['lwcrh_mean'].data),\n",
+    "        \"swcrh_mean_nwp_dom03\"     : (list_libradtran_nwp[2]['swcrh_mean'].dims, list_libradtran_nwp[2]['swcrh_mean'].data),\n",
+    "        \"lwcrh_mean_nwp_dom03\"     : (list_libradtran_nwp[2]['lwcrh_mean'].dims, list_libradtran_nwp[2]['lwcrh_mean'].data),\n",
+    "        \"swcrh_mean_nwpfr_dom03\" : (list_libradtran_nwpfr[2]['swcrh_mean'].dims, list_libradtran_nwpfr[2]['swcrh_mean'].data),\n",
+    "        \"lwcrh_mean_nwpfr_dom03\" : (list_libradtran_nwpfr[2]['lwcrh_mean'].dims, list_libradtran_nwpfr[2]['lwcrh_mean'].data),\n",
+    "        \n",
+    "        \"swcrh_mean_lem_dom04\"     : (list_libradtran_lem[3]['swcrh_mean'].dims, list_libradtran_lem[3]['swcrh_mean'].data),\n",
+    "        \"lwcrh_mean_lem_dom04\"     : (list_libradtran_lem[3]['lwcrh_mean'].dims, list_libradtran_lem[3]['lwcrh_mean'].data),\n",
+    "        \"swcrh_mean_nwp_dom04\"     : (list_libradtran_nwp[3]['swcrh_mean'].dims, list_libradtran_nwp[3]['swcrh_mean'].data),\n",
+    "        \"lwcrh_mean_nwp_dom04\"     : (list_libradtran_nwp[3]['lwcrh_mean'].dims, list_libradtran_nwp[3]['lwcrh_mean'].data),\n",
+    "        \"swcrh_mean_nwpfr_dom04\" : (list_libradtran_nwpfr[3]['swcrh_mean'].dims, list_libradtran_nwpfr[3]['swcrh_mean'].data),\n",
+    "        \"lwcrh_mean_nwpfr_dom04\" : (list_libradtran_nwpfr[3]['lwcrh_mean'].dims, list_libradtran_nwpfr[3]['lwcrh_mean'].data),\n",
+    "        \n",
+    "        \n",
+    "    },\n",
+    "    coords=list_libradtran_lem[0]['swcrh_mean'].coords)\n",
+    "ds_out = ds_out.assign(z_mc=list_libradtran_lem[0]['z_mc'])\n",
+    "\n",
+    "ds_out.attrs['description'] = 'Vertical profiles of CRH from twostr radiation calculations with LEM and NWP clouds for each LEM domain'\n",
+    "ds_out.to_netcdf('/work/bb1135/b381185/icon_output/data_for_crh_unc_paper/postprocessed_data_for_publication/for_publication/figure9.nc')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "69ba8135-1543-4231-a841-bef5032b5e4d",
+   "metadata": {},
+   "source": [
+    "## 4- Plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "39db69b1-fdd6-4775-83a2-b728ff561a9a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 8 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(nrows=2, ncols=4, figsize=(20, 10))\n",
+    "i = 0\n",
+    "\n",
+    "z = ds_out['z_mc']\n",
+    "\n",
+    "for ax in axes.flat:\n",
+    "    ax.tick_params(labelsize=14)\n",
+    "    ax.spines['top'].set_color('none')\n",
+    "    ax.spines['right'].set_color('none')\n",
+    "    ax.spines['left'].set_color('none')\n",
+    "    \n",
+    "    if i == 0:\n",
+    "        # SW diff\n",
+    "        difflw_1 = ds_out['lwcrh_mean_nwp_dom01'].values - ds_out['lwcrh_mean_lem_dom01'].values\n",
+    "        difflw_2 = ds_out['lwcrh_mean_nwpfr_dom01'].values - ds_out['lwcrh_mean_lem_dom01'].values\n",
+    "        # LW diff\n",
+    "        diffsw_1 = ds_out['swcrh_mean_nwp_dom01'].values - ds_out['swcrh_mean_lem_dom01'].values\n",
+    "        diffsw_2 = ds_out['swcrh_mean_nwpfr_dom01'].values - ds_out['swcrh_mean_lem_dom01'].values\n",
+    "        \n",
+    "        ax.plot(diffsw_1,z,color='#e6194B',lw=3)\n",
+    "        ax.plot(diffsw_2,z,color='#e6194B',linestyle='dotted',lw=3)\n",
+    "\n",
+    "        ax.plot(difflw_1,z,color='#4363d8',lw=3)\n",
+    "        ax.plot(difflw_2,z,color='#4363d8',linestyle='dotted',lw=3)\n",
+    "        \n",
+    "        ax.spines['bottom'].set_bounds(-3,3)\n",
+    "        ax.set_xticks(np.linspace(-3,3,5))\n",
+    "        ax.set_xlim([-3.2,3])\n",
+    "        ax.set_ylim([0,3])\n",
+    "        \n",
+    "        ax.axvline(x=0, ymin=0.0, ymax=1,c='black', lw=1)\n",
+    "        ax.set_title('Shallow cumulus', fontsize=15,pad=15)\n",
+    "        ax.set_ylabel('Height (km)', fontsize=14)\n",
+    "        ax.set_xlabel('CRH difference (K day$^{-1}$)', fontsize=14)#,labelpad=10)\n",
+    "        ax.text(0.0, 1.06, '(a)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "        lg=colorlegend.color_legend(ax,loc=1,fsize=14)\n",
+    "        \n",
+    "    if i == 1:\n",
+    "        \n",
+    "        # SW diff\n",
+    "        difflw_1 = ds_out['lwcrh_mean_nwp_dom02'].values - ds_out['lwcrh_mean_lem_dom02'].values\n",
+    "        difflw_2 = ds_out['lwcrh_mean_nwpfr_dom02'].values - ds_out['lwcrh_mean_lem_dom02'].values\n",
+    "        # LW diff\n",
+    "        diffsw_1 = ds_out['swcrh_mean_nwp_dom02'].values - ds_out['swcrh_mean_lem_dom02'].values\n",
+    "        diffsw_2 = ds_out['swcrh_mean_nwpfr_dom02'].values - ds_out['swcrh_mean_lem_dom02'].values\n",
+    "        \n",
+    "        ax.plot(diffsw_1,z,color='#e6194B',lw=3)\n",
+    "        ax.plot(diffsw_2,z,color='#e6194B',linestyle='dotted',lw=3)\n",
+    "\n",
+    "        ax.plot(difflw_1,z,color='#4363d8',lw=3)\n",
+    "        ax.plot(difflw_2,z,color='#4363d8',linestyle='dotted',lw=3)\n",
+    "        \n",
+    "        ax.spines['bottom'].set_bounds(-0.5,0.5)\n",
+    "        ax.set_xticks(np.linspace(-0.5,0.5,5))\n",
+    "        ax.set_xlim([-0.53,0.5])\n",
+    "        ax.set_ylim([0,12])\n",
+    "        \n",
+    "        ax.axvline(x=0, ymin=0.0, ymax=1,c='black', lw=1)\n",
+    "        ax.set_title('WCB ascent', fontsize=14,pad=15)\n",
+    "        ax.set_xlabel('CRH difference (K day$^{-1}$)', fontsize=14)#,labelpad=10)\n",
+    "        ax.text(0.0, 1.06, '(c)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 2:\n",
+    "        # SW diff\n",
+    "        difflw_1 = ds_out['lwcrh_mean_nwp_dom03'].values - ds_out['lwcrh_mean_lem_dom03'].values\n",
+    "        difflw_2 = ds_out['lwcrh_mean_nwpfr_dom03'].values - ds_out['lwcrh_mean_lem_dom03'].values\n",
+    "        # LW diff\n",
+    "        diffsw_1 = ds_out['swcrh_mean_nwp_dom03'].values - ds_out['swcrh_mean_lem_dom03'].values\n",
+    "        diffsw_2 = ds_out['swcrh_mean_nwpfr_dom03'].values - ds_out['swcrh_mean_lem_dom03'].values\n",
+    "        \n",
+    "        ax.plot(diffsw_1,z,color='#e6194B',lw=3)\n",
+    "        ax.plot(diffsw_2,z,color='#e6194B',linestyle='dotted',lw=3)\n",
+    "\n",
+    "        ax.plot(difflw_1,z,color='#4363d8',lw=3)\n",
+    "        ax.plot(difflw_2,z,color='#4363d8',linestyle='dotted',lw=3)\n",
+    "\n",
+    "        ax.spines['bottom'].set_bounds(-0.5,0.5)\n",
+    "        ax.set_xticks(np.linspace(-0.5,0.5,5))\n",
+    "        ax.set_xlim([-0.53,0.5])\n",
+    "        ax.set_ylim([0,9])\n",
+    "        ax.set_yticks(np.arange(0,9.5,1.5))\n",
+    "        \n",
+    "        ax.axvline(x=0, ymin=0.0, ymax=1,c='black', lw=1)\n",
+    "        ax.set_title('WCB cyclonic outflow', fontsize=14,pad=15)\n",
+    "        ax.set_yticklabels([])\n",
+    "        ax.set_xlabel('CRH difference (K day$^{-1}$)', fontsize=14)#,labelpad=10)\n",
+    "        ax.text(0.0, 1.06, '(e)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 3:\n",
+    "        # SW diff\n",
+    "        difflw_1 = ds_out['lwcrh_mean_nwp_dom04'].values - ds_out['lwcrh_mean_lem_dom04'].values\n",
+    "        difflw_2 = ds_out['lwcrh_mean_nwpfr_dom04'].values - ds_out['lwcrh_mean_lem_dom04'].values\n",
+    "        # LW diff\n",
+    "        diffsw_1 = ds_out['swcrh_mean_nwp_dom04'].values - ds_out['swcrh_mean_lem_dom04'].values\n",
+    "        diffsw_2 = ds_out['swcrh_mean_nwpfr_dom04'].values - ds_out['swcrh_mean_lem_dom04'].values\n",
+    "        \n",
+    "        ax.plot(diffsw_1,z,color='#e6194B',lw=3)\n",
+    "        ax.plot(diffsw_2,z,color='#e6194B',linestyle='dotted',lw=3)\n",
+    "\n",
+    "        ax.plot(difflw_1,z,color='#4363d8',lw=3)\n",
+    "        ax.plot(difflw_2,z,color='#4363d8',linestyle='dotted',lw=3)\n",
+    "        \n",
+    "        ax.spines['bottom'].set_bounds(-0.5,0.5)\n",
+    "        ax.set_xticks(np.linspace(-0.5,0.5,5))\n",
+    "        ax.set_xlim([-0.53,0.5])\n",
+    "        ax.set_ylim([0,12])\n",
+    "        \n",
+    "        ax.axvline(x=0, ymin=0.0, ymax=1,c='black', lw=1)\n",
+    "        ax.set_title('WCB anticyclonic outflow', fontsize=14,pad=15)\n",
+    "        ax.set_yticklabels([])\n",
+    "        ax.set_xlabel('CRH difference (K day$^{-1}$)', fontsize=14)#,labelpad=10)\n",
+    "        ax.text(0.0, 1.06, '(g)', transform=ax.transAxes, \n",
+    "            size=14)                \n",
+    "                           \n",
+    "    if i == 4:\n",
+    "        \n",
+    "        # SW diff\n",
+    "        difflw_1 = ds_out['lwcrh_mean_nwp_dom01'].values - ds_out['lwcrh_mean_lem_dom01'].values\n",
+    "        difflw_2 = ds_out['lwcrh_mean_nwpfr_dom01'].values - ds_out['lwcrh_mean_lem_dom01'].values\n",
+    "        # LW diff\n",
+    "        diffsw_1 = ds_out['swcrh_mean_nwp_dom01'].values - ds_out['swcrh_mean_lem_dom01'].values\n",
+    "        diffsw_2 = ds_out['swcrh_mean_nwpfr_dom01'].values - ds_out['swcrh_mean_lem_dom01'].values\n",
+    "        \n",
+    "        # net diff\n",
+    "        diffnt_1 = difflw_1 + diffsw_1\n",
+    "        diffnt_2 = difflw_2 + diffsw_2\n",
+    "        \n",
+    "        ax.plot(diffnt_1,z,color='#000000',label='net',lw=3)\n",
+    "        ax.plot(diffnt_2,z,color='#000000',linestyle='dotted',label='net',lw=3)\n",
+    "        \n",
+    "        ax.spines['bottom'].set_bounds(-3,3)\n",
+    "        ax.set_xticks(np.linspace(-3,3,5))\n",
+    "        ax.set_xlim([-3.2,3])\n",
+    "        ax.set_ylim([0,3])\n",
+    "        \n",
+    "        ax.axvline(x=0, ymin=0.0, ymax=1,c='black', lw=1)\n",
+    "        ax.set_ylabel('Height (km)', fontsize=14)\n",
+    "        ax.set_xlabel('CRH difference (K day$^{-1}$)', fontsize=14)#,labelpad=10)\n",
+    "        ax.text(0.0, 1.06, '(b)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 5:\n",
+    "        \n",
+    "        # SW diff\n",
+    "        difflw_1 = ds_out['lwcrh_mean_nwp_dom02'].values - ds_out['lwcrh_mean_lem_dom02'].values\n",
+    "        difflw_2 = ds_out['lwcrh_mean_nwpfr_dom02'].values - ds_out['lwcrh_mean_lem_dom02'].values\n",
+    "        # LW diff\n",
+    "        diffsw_1 = ds_out['swcrh_mean_nwp_dom02'].values - ds_out['swcrh_mean_lem_dom02'].values\n",
+    "        diffsw_2 = ds_out['swcrh_mean_nwpfr_dom02'].values - ds_out['swcrh_mean_lem_dom02'].values\n",
+    "        \n",
+    "        # net diff\n",
+    "        diffnt_1 = difflw_1 + diffsw_1\n",
+    "        diffnt_2 = difflw_2 + diffsw_2\n",
+    "        \n",
+    "        ax.plot(diffnt_1,z,color='#000000',label='net',lw=3)\n",
+    "        ax.plot(diffnt_2,z,color='#000000',linestyle='dotted',label='net',lw=3)\n",
+    "        \n",
+    "        ax.spines['bottom'].set_bounds(-0.5,0.5)\n",
+    "        ax.set_xticks(np.linspace(-0.5,0.5,5))\n",
+    "        ax.set_xlim([-0.53,0.5])\n",
+    "        ax.set_ylim([0,12])\n",
+    "        \n",
+    "        ax.axvline(x=0, ymin=0.0, ymax=1,c='black', lw=1)\n",
+    "        ax.set_xlabel('CRH difference (K day$^{-1}$)', fontsize=14)#,labelpad=10)\n",
+    "        ax.text(0.0, 1.06, '(d)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "        \n",
+    "        ax.text(0.04, 12, \"Net\", color=\"black\", size=14, va=\"top\")\n",
+    "        ax.text(0.04, 11, \"solid: without overlap\\nassumption\", color=\"gray\", size=14, va=\"top\")\n",
+    "        ax.text(0.04, 9, \"dotted: with overlap\\nassumption\", color=\"gray\", size=14, va=\"top\")\n",
+    "        \n",
+    "    if i == 6:\n",
+    "        \n",
+    "        # SW diff\n",
+    "        difflw_1 = ds_out['lwcrh_mean_nwp_dom03'].values - ds_out['lwcrh_mean_lem_dom03'].values\n",
+    "        difflw_2 = ds_out['lwcrh_mean_nwpfr_dom03'].values - ds_out['lwcrh_mean_lem_dom03'].values\n",
+    "        # LW diff\n",
+    "        diffsw_1 = ds_out['swcrh_mean_nwp_dom03'].values - ds_out['swcrh_mean_lem_dom03'].values\n",
+    "        diffsw_2 = ds_out['swcrh_mean_nwpfr_dom03'].values - ds_out['swcrh_mean_lem_dom03'].values\n",
+    "        \n",
+    "        # net diff\n",
+    "        diffnt_1 = difflw_1 + diffsw_1\n",
+    "        diffnt_2 = difflw_2 + diffsw_2\n",
+    "        \n",
+    "        ax.plot(diffnt_1,z,color='#000000',label='net',lw=3)\n",
+    "        ax.plot(diffnt_2,z,color='#000000',linestyle='dotted',label='net',lw=3)\n",
+    "\n",
+    "        ax.spines['bottom'].set_bounds(-0.5,0.5)\n",
+    "        ax.set_xticks(np.linspace(-0.5,0.5,5))\n",
+    "        ax.set_xlim([-0.53,0.5])\n",
+    "        ax.set_ylim([0,12])\n",
+    "        ax.set_yticklabels([])\n",
+    "        \n",
+    "        ax.axvline(x=0, ymin=0.0, ymax=1,c='black', lw=1)\n",
+    "        ax.set_xlabel('CRH difference (K day$^{-1}$)', fontsize=14)#,labelpad=10)\n",
+    "        ax.text(0.0, 1.06, '(f)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 7:\n",
+    "        \n",
+    "        # SW diff\n",
+    "        difflw_1 = ds_out['lwcrh_mean_nwp_dom04'].values - ds_out['lwcrh_mean_lem_dom04'].values\n",
+    "        difflw_2 = ds_out['lwcrh_mean_nwpfr_dom04'].values - ds_out['lwcrh_mean_lem_dom04'].values\n",
+    "        # LW diff\n",
+    "        diffsw_1 = ds_out['swcrh_mean_nwp_dom04'].values - ds_out['swcrh_mean_lem_dom04'].values\n",
+    "        diffsw_2 = ds_out['swcrh_mean_nwpfr_dom04'].values - ds_out['swcrh_mean_lem_dom04'].values\n",
+    "        \n",
+    "        # net diff\n",
+    "        diffnt_1 = difflw_1 + diffsw_1\n",
+    "        diffnt_2 = difflw_2 + diffsw_2\n",
+    "        \n",
+    "        ax.plot(diffnt_1,z,color='#000000',label='net',lw=3)\n",
+    "        ax.plot(diffnt_2,z,color='#000000',linestyle='dotted',label='net',lw=3)\n",
+    "        \n",
+    "        ax.spines['bottom'].set_bounds(-0.5,0.5)\n",
+    "        ax.set_xticks(np.linspace(-0.5,0.5,5))\n",
+    "        ax.set_xlim([-0.53,0.5])\n",
+    "        ax.set_ylim([0,12])\n",
+    "        ax.set_yticklabels([])\n",
+    "        \n",
+    "        ax.axvline(x=0, ymin=0.0, ymax=1,c='black', lw=1)\n",
+    "        ax.set_xlabel('CRH difference (K day$^{-1}$)', fontsize=14)#,labelpad=10)\n",
+    "        ax.text(0.0, 1.06, '(h)', transform=ax.transAxes, \n",
+    "            size=14)    \n",
+    "        \n",
+    "    i = i + 1\n",
+    "\n",
+    "    \n",
+    "fig.subplots_adjust(wspace=0.2,hspace=0.35)\n",
+    "\n",
+    "plt.savefig('figure9.pdf', bbox_inches = 'tight')\n",
+    "#plt.savefig('figure9.png', bbox_inches = 'tight',dpi=300)    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "48351b14-9765-4a3c-b9df-8f491d7d0231",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "client.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0128f0f7-bf65-4c3a-8844-fa1865597028",
+   "metadata": {},
+   "source": [
+    "## Plots for the review"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "ad5916bb-2eea-49d3-a05e-9ccf33d2f1a1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1296x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(18, 5))\n",
+    "i = 0\n",
+    "\n",
+    "for ax in axes.flat:\n",
+    "    ax.tick_params(labelsize=14)\n",
+    "    ax.spines['top'].set_color('none')\n",
+    "    ax.spines['right'].set_color('none')\n",
+    "    ax.spines['left'].set_color('none')\n",
+    "    \n",
+    "    if i == 0:\n",
+    "        ax.set_title('Shallow cumulus', fontsize=15,pad=15)\n",
+    "        \n",
+    "        ax.plot((ds_lib1[3]['ddt_radlw_mean'].values),z_fl2,color='#4363d8',\n",
+    "                linestyle='-',linewidth=3,label='LEM clouds')\n",
+    "        \n",
+    "        ax.plot((ds_lib1[4]['ddt_radlw_mean'].values),z_fl2,color='#e6194B',\n",
+    "                linestyle='-',linewidth=3,label='NWP grid-box clouds')\n",
+    "        \n",
+    "        ax.plot((ds_lib1[5]['ddt_radlw_mean'].values),z_fl2,color='k',\n",
+    "                linestyle='-',linewidth=3,label='NWP grid-box clouds with fraction')\n",
+    "        \n",
+    "        #ax.spines['bottom'].set_bounds(-3,3)\n",
+    "        #ax.set_xticks(np.linspace(-3,3,5))\n",
+    "        #ax.set_xlim([-3.2,3])\n",
+    "        ax.set_ylim([0,3])\n",
+    "        \n",
+    "        ax.axvline(x=0, ymin=0.0, ymax=1,c='black', lw=1)\n",
+    "        ax.set_ylabel('Height (km)', fontsize=14)\n",
+    "        ax.set_xlabel('CRH  (K day$^{-1}$)', fontsize=14)#,labelpad=10)\n",
+    "        ax.text(0.0, 1.06, '(a)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "        lg=colorlegend.color_legend(ax,loc=1,fsize=14)\n",
+    "        \n",
+    "    if i == 1:\n",
+    "        ax.set_title('Shallow cumulus', fontsize=15,pad=15)\n",
+    "\n",
+    "        ax.plot((ds_lib1[0]['ddt_radlw_mean'].values),z_fl2,color='#4363d8',\n",
+    "                linestyle='-',linewidth=3,label='LEM clouds')\n",
+    "\n",
+    "        ax.plot((ds_lib1[1]['ddt_radlw_mean'].values),z_fl2,color='#e6194B',\n",
+    "                linestyle='-',linewidth=3,label='NWP grid-box clouds')\n",
+    "\n",
+    "        ax.plot((ds_lib1[2]['ddt_radlw_mean'].values),z_fl2,color='k',\n",
+    "                linestyle='-',linewidth=3,label='NWP grid-box clouds with fraction')\n",
+    "\n",
+    "        #ax.spines['bottom'].set_bounds(-3,3)\n",
+    "        #ax.set_xticks(np.linspace(-3,3,5))\n",
+    "        #ax.set_xlim([-3.2,3])\n",
+    "        ax.set_ylim([0,3])\n",
+    "\n",
+    "        ax.axvline(x=0, ymin=0.0, ymax=1,c='black', lw=1)\n",
+    "\n",
+    "        ax.set_xlabel('CRH  (K day$^{-1}$)', fontsize=14)#,labelpad=10)\n",
+    "        ax.text(0.0, 1.06, '(b)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 2:\n",
+    "        # total effect\n",
+    "        net1 = ds_lib1[0]['ddt_radlw_mean'].values+ds_lib1[3]['ddt_radlw_mean'].values\n",
+    "        net2 = ds_lib1[1]['ddt_radlw_mean'].values+ds_lib1[4]['ddt_radlw_mean'].values\n",
+    "        net3 = ds_lib1[2]['ddt_radlw_mean'].values+ds_lib1[5]['ddt_radlw_mean'].values\n",
+    "        ax.set_title('Shallow cumulus', fontsize=15,pad=15)\n",
+    "\n",
+    "        ax.plot(net1,z_fl2,color='#4363d8',\n",
+    "                linestyle='-',linewidth=3,label='LEM clouds')\n",
+    "\n",
+    "        ax.plot(net2,z_fl2,color='#e6194B',\n",
+    "                linestyle='-',linewidth=3,label='NWP grid-box clouds')\n",
+    "\n",
+    "        ax.plot(net3,z_fl2,color='k',\n",
+    "                linestyle='-',linewidth=3,label='NWP grid-box clouds with fraction')\n",
+    "\n",
+    "        #ax.spines['bottom'].set_bounds(-3,3)\n",
+    "        #ax.set_xticks(np.linspace(-3,3,5))\n",
+    "        #ax.set_xlim([-3.2,3])\n",
+    "        ax.set_ylim([0,3])\n",
+    "\n",
+    "        ax.axvline(x=0, ymin=0.0, ymax=1,c='black', lw=1)\n",
+    "\n",
+    "        ax.set_xlabel('CRH  (K day$^{-1}$)', fontsize=14)#,labelpad=10)\n",
+    "        ax.text(0.0, 1.06, '(c)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    i = i + 1\n",
+    "\n",
+    "    \n",
+    "fig.subplots_adjust(wspace=0.2,hspace=0.35)\n",
+    "#plt.savefig('figure9.pdf', bbox_inches = 'tight')\n",
+    "#plt.savefig('figure9.png', bbox_inches = 'tight',dpi=300) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "2b06c82b-e0cc-4079-8d5e-5ad45d3e3f54",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Ym7S4skkJAw4G6bUjmidJSogq5WZlm58bbSVzK4QQouForcu3lHCU7htCCOtyKvaU+bmTrREbmfpYNFOSlBBVys0q01LCVlpKCCGEaDhFhWcHf7MB7I1yHxJCWJfkuCTzcyc7aQ0mmi9JSogq5WXnmJ8bJSkhhBCiARXl5Zuf22GDnb1UWYQQ1iU1KcX83NFekhKi+ZI7vKjSmaTEwOnLMHSYVu/v9+s/qVzz2NF6fx8hhBBNX7mkhFLYSo1FCGFl0pNTzc+dDE6NWBIhGpcMFNDIXvsyntNZxbx0b3Cl66957CiJKUUVll813otbr/QlIbmQax+PwkbBd6+2wd/7bIuGzOxipj90lMIizYdPt6JDaM0ysAW5Z1tK2MmUoEIIIRpQQW6e+bktNthKX2shhJU5nZZufu5odG68ggjRyOQvTQtw3SXeXDLco9wyR2P5n4y8Pe34Z91prp/iY162bFMGnm62JKVWTGpUR35urvm5ra1MCSqEEKLhFOUXmJ/bKoWtrSQlhBDWJfP0afNzR0dJSojmS5ISFsDRwQYv9/P/V40b6M6S9Rlcd4k3SpkqbovWnGbcIHe+m59y3n0Blqw7za9LUolNLGTIjLW89mU8+XlnkxJ2ZWbfSEwp5KOfk9i2zzQ7R69Oztx1tR++XqZWGt/MS2bN1kw+f6G1eZ9/1p3m/R8T+fvD9uZlvyxKYc6/aeTmlzC4pysBPuXHrYg6mc/HPydxMCYPrTUBvgb++x8/uodL8zYhhLB2BXlnW0rYKRskNy6EsDaZmRnm507Oro1YEiEal/TQtBJ9uzhTUFjC9gOmLheHj+cRl1TAsN4XDnB/r0rn7e8SGT/IndnPhbLjn9tpFWikIL9MhbC0Nqi1ZtaHsaRlFPH6wy1545EQkk8XMevDWLTWVb1FBSu3ZPDV3GRuuNSHT54JpaW/gd//TSu3zcufxeHlbssHT4XwybOhXD/FG4O9/FImhBDNQaF03xBCWLnMrEzzcycX90YsiRCNS1pKWICv/kzm27+Syy17+rZA+ndzMb+2tVWMGeDO4rWn6dnRmcVrTjO8jxsOxgvnnb7/O4XLR3ty5VgvADJT9nLVeC/+efpstw9lYzrOtn05RJ3I59tX2tCitGXDk7cGcMOT0Wzbn0OvTtVrevbH0jTGDHRn8jAPAK6Z7M2OgznEJZ1trpuUUsT0sV6EBJjmpg/yN1Tr2EIIISxfVvrZXxCNNraSlBZCWJ3TWWe7b3h4+5xnSyGsmyQlLMAVYzyZMLh89tTLo+J/3fjB7tzxQgypp4tYvimDl+6rfPDMstIyikhOK6Jnx4pdIkqKi83PbZQpKXE8vgBvDztzQgIg0NeAt4cdx+MKqp2UOB5fwIQhHuWWdWrrWC4pccUYT978NoElGzLoEe7EkF4u5gSFEEII63Y6+WzrOQcbAzbSUkIIYWXSc862lPAMCGjEkgjRuCQpYQHcXGyr1UqgZQsDYSEOvDQ7Dk83Ozq1dSQhufD8O52nx4UuKTE/P9NSQgNUVS8sXW5jU/GwRcXV79pxxg2X+jCqvxtbdmezZW82381P5r7rWlRI0AghhLA+p1PO/oJotJWWckII63M6P8v83LflhX9MFMJayZgSVmbCYHd2Hsxl/JDq/eHu6W6Hj6cd2/bnVFhXUnK2pcSZwTNbBRhISSsql+yIO1VASnoRrQJNlUZ3F1vSMorLjTFx9MTZ+eYBQgIM7I/KLbds/9HyrwGC/Q1cNtqTl+8LZvxgdxatSa/WeQkhhLBsGalnu284SFJCCGGF0gvP1n0D2oY0YkmEaFzSUqIJyMkt4cjxvHLLXJxszV0kcvNKSD1dflpPg73CxaniUORjBroxoJtzpeuqMmOiFx//cgpPN1v6d3XBxSuc3/5JpbhMS4kzzWZ7dnKiTUsjr3wWx11X+6OBD35MpF2IkR6ls2J06+BEZnYSPy5IZURfV3YezGF1ZGa597xslCevfZlAh1AHunVwYvXWTA5E5+HqbMqT5ReU8Olvpxjay5UWPvakZRSx50guHVs7Vvu8hBBCWK6MtLMtJQx2kpQQQliXkpIS0ovP/mgXFB7aeIURopFJUqIJ2H04lzteOFZu2ZBeLsy6MwiA7+anVJjWc1Q/V564NbDCsWxtFO6uNftvnTLCE3s7xW9L0vj891N0H/cZMXH5lbaUUErx/F1BfPhTEg+9fhyAnp2cuftqv7OtKQKN3HetPz8uSOGnhSn07+bCjEnefPnnKfPxRvR1Iz65kK/+TCavoIQB3Vy4YownS9abKqE2NorM7GJe+zKetIxi3Jxt6NfNhdun+dbo3IQQQlimrIyzyWyjnUMjlkQIIepeZuIpCjD9AOiobGnRUga6FM2Xqsk0jk2c1ZxIY1NKobXmqg5j+PXQUgDuGTqN91b92sglE8IiNafR+SQOizrz/BV38twfnwAwIagPC09uLrf+zL1KiGqSWCyalENrN9FhSH8AAmydiMnNxGBfvme9xDlhZaqMwzKmhKhS2ZYSNrbV7w4ihBBC1FZ25tkB4IwGaSkhhLAusYeOm5+72TlVSEgI0ZzIt19UqaTMmBK2kpQQQgjRgLKzs83PjcaK01YLIYQlO3HkbFLC1eDciCURovFJUkJUydb27NgURUVF59lSCCGEqFspqcnm5y5uno1YEiGEqHtH9h40P/d09mrEkgjR+CQpIapkNJ5tLpubU3HKUCGEEKK+JKUmmZ/7B4c2XkGEEKIeHDlyyPw8MKB1I5ZEiMYnSQlRJaPD2eayuXm559lSCCGEqFtJWWnm54HtwhqxJEIIUfdOJJ40P2/ZrmMjlkSIxidJCVElo4Oj+XmeJCWEEEI0oKT8DPPzVl3CG7EkQghR945nnm0N1r5vz0YsiRCNT5ISokpOrm7m50nppxqxJEIIIZqTHYtWc6o4DwBbFO36dGrkEgkhRN3Z9McSThRkAqY/xrqPHtC4BRKikUlSQlSpz4Rx5udbT0WRl5V9nq2FEEKIuvHyA0+an/dxb0NYmEfjFUYIIerY8/c9gi593tejLZ26tGjU8gjR2CQpIao08ebxBNu5AJCti5j/wXeNXCIhhBDWLulIDPMPbTS/vuKae7C1UY1YIiGEqDvbF67kn5O7zK+vv+dZiXGi2ZOkhKiSi5MdPQO7ml/P+3HOBfdJv/9tUq9//rzbpF7/POn3v33ebeIDJ5P799rqFbSBZL7xA6dG/Lfe36ck5TTxgZPJX7/rwhsLIYSVeeXWR8nTxQC0N3pz+2v1H3eFEKKhzPrvQ5SUPu/jFsotz17XqOURoimQpEQjS7//beIDJ5P5zs/lluev30V84GRKUk4DkNj9OjLf/aXcNplv/2T6433BunLL0+56nZTLHit3nDOPxIgZpF4zi8K9UdUqX//Rl5ifL9u/gfT4pPNsDW4v3IbH+w9V69hCCCFEWevnLOar1XPNryeNuAZXZ/vGK5AQQtShzx58mYXHtptfX3vHU9jbSSsJISQp0RQ4GMj+6HeKSxMQlTEM7ErBuvK/nBds2I1tkG+lyw0Du5Zb5rPyI/x2fIfnd7MoOZ1F6jWzKMm48BgR0x+9GQ8bAwAJRTlcP3BipdvpomK01ti4OWPj7nLB4wohhBBlbf17OVP+M5XTJYUAtLB14t4Pn2vcQgkhRB35/MGX+e/bT1NcOppEL9dW3PF/NzVyqYRoGiQp0QQYB3bBtqU/WW//XPU2g7pQEHkAnW+qrOn8QgoiD+J8zzQK1u82b1d0NJaShFQMg8snJWy93bH188TQowNuz95MSVIahdsOVPl+OT8tIbH3jThNuIP57S7lBqdWxAVMYn7MVt69/WlzV4acX5aSNOAWEkIvQ+fkVei+cWZZQtiVJHa9lqz3fq3251KSlEbqdc8R3+YKkvrcSM7vK8qtL9wfQ8r0p4hvczkJnf5D+v1vmxMt+Rt2Ex9yabkuENnfLiSh/TSKjiVU+Z5Fh0+QesMLJHSYTkLYlSRf8hCF+2Mq3VaXlJD59k8k9ppJfOhUTo28i7zFZ/tBF51IJD5wMgU7D5fb79yuKQU7DnFq3H3Et76MU2PupWD7ofLvU1jE6ac/JbHH9cSHTiWx10wyXvr6vJ+dEEJYkl3/rmXC1MmkFOcD4KLseO6Jjwht49nIJRNCiNr74qGXufPtpykqTUi0tHflzR/nYLCXP8WEAElKNA02Nrg+eQM53y2iKCa+0k0MA7tCXj4F2w8CULDtADaerjhdOZKimDiKT6UBpu4aOBgx9Kx6TnflYGr5oAuLK13fy96D0w+/j/PMSfj++x5dn7yRJ9w6mtc//dn/OHUsluLjieT+uQrPTx/HZ+l7KKOhwrEyXvyS/NXb8fz8Cbx+/T8K9xylYOOean0smW/+iHFsP3z/fQ/Ha8dz+t63zH/g65w8Uq95FuXsiM+Ct/D84ikKIvdz+sF3ATAO6ILznZeTfs9blKRlUnT4BJnPf4Hb/92BXavKRzguTkgheepjoBReP7+Izz/v4jRzMhSXVLp99ud/kf3xH7g9NRPfZR/iMH4Aabe8TOGe6nWNASjJySPtuuexa9UCn0Vv4/bUDWS88EX59/niL/IWbcDj40fxWzsbz08ew65tULXfQwghmrIDa7YwfuI4ThXnAuCkbPm/hz7g9hdvaOSSCSFE7X3x8Mvc+Vb5hMQ3c5YzbHLvRi6ZEE2HJCWaCIdRfTD06Ujmq99Wut6udSA2AT7mrhoF63ZhHNgF5eSAfZcwc2uJgnW7MPQORxkr74NbkppB5ls/oVwcse/RvtJtbnYOxTisBy53X4ld2yDcrp+AcfxQ8/osXcRvP/6MLijC4/0Hse8ahn14KMrOtvx7ZeeS89MS3J6+EePwXtiHh+L+9v1gU72vncOEAThfNwG7tkG43ncVhkFdyf5sHgC5f65EZ+eZ3r9jKMYBXXB/7W7yFq6nKDoOANeHr8HWz5P0h98j7a43MI7ui9P0UVW+X/bXC7BxMuI5+3EMPTpg1zYIpytGYB/RpvLtP/kD5zsux/Hy4aYyPnothn6dyPrkj2qdH0DeHyvRhUW4v30/9uGhGIf3wuW+q8ptU3zyFHZtAzH064xtsB+GPh1x+s+Yar+HEEI0VWt++pvRo0YSX5QDgIOyZdZ/3+a+129v5JIJIUTtvXXDw9z55tMUlklIfP3bMkZMkYSEEGVJUqIJcX36RvL+Xlehuf8ZxkFdKCjtjlCwfjeGAV0AMAwss3zDboyDu1XYN6nfzaYuFBEzKDpyAs/Zj2Pr41Hp+4TZuWDfvXzCwm9MLwDsMA3Gc6ogi/iCbE7EVd0VojgmHgqKsO91tpWFjbMjduGtzK+z3vuVhLArzY/ik2cH0jT0Kt/aw9A7nKJDJwBTNwu7jqHYuDiVWd8RbGzM2yh7Ozw+fJj8pVsoSUnH/bW7qiwrQNGeo9j37YQyXHhQtZLMHFM3mT4dyy039O1sfv/qKDp8AvuOodg4O549xjnn7TR9FIV7ozk1+DZOP/ExeUu3oEsqb70hhBCWoKSkhFmX3caYGZcSW5gFgBEbnrzxVR794J5GLp0QQtROdkoqV4b146Fv3yyXkPji52WMvLRPI5dOiKanQZMSSqmhSqm/lFKxSimtlJp5znqllHpOKRWnlMpVSq1USnVuyDI2JkP39jhMGkjmS19Vvn5QNwq2HaQkPYuC7QcxDDQlJYwDIshfv4fCwycoOZVuXl6W15yX8Vn6Pv4Hf8Vv7WyMw3tVWQ4FoCofCfim/tean58uKWRwn75sX7ym8gPpKt/CzOm6Cfj8+575YdPC+8I7lR5bVVFGyiwu2HYQSkrQp7PNM5mc75g1VlkZ1JlVpU/02QPrwqLyb6kv/Kb2XcPw2/QFro/fALqE9PveJvU/T0tiQlwUicOisSUdiWF0YCdemPsZ+aUT4zkqWx679gWe/lxmbxLNg8Ri67Vz0Sp6Bbfn96ObzctCDe589tNSxlwuCQkhKtPQLSVcgD3AfUBuJesfBR4C7gH6AEnAv0op1wYrYSNzffx6CjbtI3/FtgrrDIO6Qn4h2Z/+iY2XO3ahAQDY9+1M8fEE8v5YiXJ2rNDKAcCupT92oQHYuDpVWHeuw0VZFO4oP9jimdcfrfuGe0fegSr9yzu2MItRk8aw9IvfKhzHtnUA2NtRuPXsgJolOXkUHTxmfm3j6Ypd60Dzo2wXkIJtB8sdr2DrQezaBZvOp31LCvdFU5KVc3Z95H4oKcGuXUvANNBkxlOf4PbynRiGdif97jfQRZWPowFg16UthZv3oQsKz/8BATauTti08KJg877yZdy8F/v2IaZtvN1N55yYZl5/7lSs9u1DKDwQQ0lO3tltKhmA1MbFCcdLBuP+6l14fTeLgrW7KI6ufPwRIS5A4rBoNAve+5ruHTuzIvFsfG9n9OLrD+bz/HdPVZ1sFsL6SCy2Qp/e9QyDJ43mYF6Kedkw3wgWbdnDuCv6NmLJhGjaGjQpobVeqLV+Ums9Byj3M68y1UTuB17VWv+utd4D3AC4AjMaspyNya51IE7XjCP7i78qrgv2wzbEn+wv/sJYpjWEjZMD9l3akv3FXxj6daowtkNNfZEdQ/6q7WR99DtFUbHk/LiEvEUbALC1Ubyz9COGdhlmbpCQVpLPlFuv5pun3ix3HBtnR5yuHkPGS1+Tv2o7hQePcfqBd6scOPJceQs3kPPDYoqiYsl6/1cK1u7E+dZLAXC8bDjKyYH0e9+icH8M+Rv3cPrRD3GYOBC71oHo4mLS734TQ/8InK+bgMcb91Icn0LWWz9W+X7ON0w0DTx5+/8o2HGIoug4cv9cVeXAlS53XkH2J3+Q++cqio7Gkvna9xRs2ofz7ZcBoByN2PfqQNaHcyg8eIyCLfvJfOHLcsdwuGwYytaW0w+8S+HBY6bP/d3yM5RkffqnqRyHT5SWaSXK1QnbgGq2KhGiDInDojHkZWRx75DLmXLfjebxIwAmhQzin72HmP7fCY1YOiEansRi65Kdksa1nYZyx0f/R5Y2tYo1YMNtQ29h4bHthHcNbuQSCtG0NaUxJVoDLYAlZxZorXOB1cDAxipUY3B58GqUbeWJBcPAruisXPN4EublA7qYlg+qOJ5ETW0tTMf99bvJ/mI+p0bfQ97iDTjfdQWUztqhlGL45cNo4ROIp41pWa4uZubLD7N8zt/k55xN+Ls+ezPGQV1Iu/klUq98ErvwEAz9I6pVDteHriZvwXpOjb6H7G8W4f72/RhKW4EoJwe8fnwBnZVD8qQHSbvxRQy9w3F/6z7ANFZFcUyc+bWNlxse7zxA1gdzKNi0t9L3sw3wwfuPV6GwkNQrnyR57H1kfzkf7Cq/TJxuvgTnOy8n4/++Kp0OdAOenz1RbmBM97fuByB5woOcfuwDXB+7ttwxbJwd8fx2FkXRcSSPu4+MF7/A9amZ5bdxcSTr4z9ImfggyePuo2hvNF7fP49ycqjW5yhEDUgcFnWqpKSET+99nvY+Aby/9k/zX14eNgaenD6LuUfX0LqtJFiFOIfEYguhteb7x18jPCCEH/af7c4cbOfKuy/8wCcrZ+PkaNeIJRTCMqjq9GmvlzdWKgu4W2v9denrgcA6oJXW+niZ7b4EgrTW4y5wyMY5ESuklKow1kHGrM/IX7MD3+Ufllv+y/sLePDBq4gryjYv87Y18tztj/Lf95/DppozbQhhxZpse3SJw6I+LZv9M488/CDbM8t3NevqHMgrX/zOxKv61+r4ld2rhDgPicWiTh1es4U7rryW5UnluzwP9OzA+4sW07NfaK3fQ+KcsDJVxuGm+BfjuVeeqmSZaYVStymlIpVSkbNnz67/kjUjWR/9TuGeKIqi48j+diHZ3y3CsZJpKK+6ZxK//LWOoV5nZ4xIKc7nno9eZHiLDhzatKMBSy2EqCMSh8VFO7RmMxNbdmX07VeXS0i429hzU98ZLD56qNYJCSGaCYnFTVB+ZjaPjbqKbsMGlEtIuNsYuHPkHSw8trtOEhJCNCdNqT3RmbklWwBl51T0AxIr20FrPRs4E3kljViHCnceIfuTPynJzMa2pT9uT9yA0y1TKt128IRu/Juwl5euf4FPf3uNxGJT9401p47QY0BvHpxyA7N++wQ7+wtPtSmEaFQSh8VFSzsey6OX3cR325aaZ9UAsEcxIXQoT3w9m/7DKg7ELISoQGJxEzX/tU954NnHOZqfXm756IDePPfdtwwa1bHyHYUQ59WUWkpEYwrC5p/jlVIOwBBgfWMVqrny/PQx/Hd9T0D0n/it/gTnWy8976joBnsbnv/pOZZs3Mek4P7mtjk5upj/m/clbV18eHjstcQfim6YExBCXAyJw6JGSoqLWfDOl1zWpg8hoaF8vm1JuYTEYK9wvv/0X+ZGrZCEhBDVJ7G4CSnKy+erB/+P/h4hTHnsjnIJiTCjF68/+BmLjm+WhIQQtdCgY0oopVyAsNKX64FXgb+AVK31caXUY8BTwEzgEPA0MBTooLXOvMDhJStcR2rbf624RPP+Q7N5/6PHiSpIL7fOgA2jWnblv4/cz8S7rpMxJ0Rz0KT6MUscFnXh0OrNvP/EK8zbspQThVkV1ndy9OfG25/n3tduxWBfP3Fe+lqLGpJYLGok6VAUb/z3SX5c9TexZcZOA3BWdlze6ypemvsJLYNc6q0MEueElakyDjd0UmI4sKKSVd9orWeWToE0C7gd8AQ2AXeVToV0IXLF1pG6CoAxR1N46tKZzN/3D5m6sML6tkZPrh53Ofe//zzeIUG1fj8hmqimVhEejsRhcREyEpL44vH/8dPcX9ly+mSl24TauzN17C08/d3LeHsa6rU8UlkXNSSxWFTLxh/n8cbT/8ff0dvKtfwCsEUx2LcLD77/OZdM733eVsR1QeKcsDJNIylRz6zmRBpbXQfAo/vj+fD+/+Of1XPYl5dUYb2DsmV8657c++yjDL/+inoP8EI0sOb0hZY4bGWKi4pY+M4XfPHhbP49tpMcXVxhG1dlz+Cgnky87g6uf/Ia3FwaZvwgqayLGpJYLKpUmJ3Ld4+/wsfffkFkRlyF9R42BkaGDefG555n4lX9sLFpmK+TxDlhZSQpIaqvvgJgQWEJ3736C79/+jar4rZVWrkNd/RhWLdBTL1+GqNuuhJ7o7HOyyFEA5OKsLAoKdHHmffBdyxd+A+rj24jtjC7wjY2QC+31owYOZ2bXnmIDuG+DV5OqayLGpJYLMopzMll2ac/8uvXP7Fo33oSinIrbNPewYcxQ67m7veeJTzcp8HLKHFOWBlJSojqa4gAuH1zDJ89/AJLN8/jcH5qpdu4Knt6+7Vl2KDhTL93Jh2H9avXMglRT6QiLJq0jIQk/vn0RxbNXcCmQzvZn3Oqyv/I1gYPBncey1WPP8K4y3thZ9d4X2+prIsaklgsSIs+zu9vzOav+fNZE3uA9JKCCtvYoejv3YlLbniA22Zdj4db480eJ3FOWBlJSojqa8gAmJFVyNcvfMv8bz9gTeKuCn33ymppcKV/aFfGT5nIZffdiGdwQIOUUYhakoqwaFJy006z4utfWfDrX2zYu5XdmQkUnee/zt3GnoGBfRh33R1c++hVeHvU71gR1SWVdVFDEoubIa01h5av4+d3PmPx2hVEpp+sMt552RgZ3m4U18x6jinTejdq0vUMiXPCykhSQlRfbQLgzJkzSU5O5u+//65ym8mTJ+Pj48PXX39tXqa1Zsvaw8x7+wsiNy5lSfy2876PLYouri0Y1LUfU2dOZ9h1l9d7V4/nnnuOOXPmsGdPdcaYunjJycn4+vqyYsUKhg8fXq/vBTB8+HAiIiL44IMPqtzm66+/5u677yYrq+Io++eTkJDA9ddfz7p168jJyWnwG+vFlruONX6tpuFIHG6CCnPzWP/jn8z/4U/Wbd/E9vST500A2wAdHPzoFNKNXsPHMfX+GwgP925y4/1IZV3UUNP6AtevZn1hFOXmsfqLn/jtq59YuncLR8pM4XkuX1tHerToSv9Rl3LVE7fRKdy74QpaDRLnhJWpMg7bNWQpREUzZ87km2++4cUXX+Tpp582L1+5ciUjRozg1KlT+Pj4EBAQwN13381TTz1l3ubFF1/k2WefZc6cOVxxxRXm5ddccw0nTpxg9erV5uOc4ePjQ+/evXn11Vfp1q1bnZ/Pu+++e1HBUylF3yHt6Tvkf+bX99/4ODn7T7Br/wZ2Zhwjt8wYFMVodmTGs2PdXD5cNxfX2+zp6x/GkAFDGPufyfS+dKyMR1FNf/zxB/b2Z5smhoaGcvfdd/Pwww/X+thvvPEGcXFx7NixA1dX11of73yUUvz2229ceeWV5mVXXXUVEydOrNf3FaKpyUlJY+NvC1g27x/Wbd3A1uRjZOmi8+4TZvSiY0AE3QeOZvzt19BnYGvsm8CvhEIIUR1x2/ew+Iuf+Pvvhaw6sY/USrplnNHBwZcubfoxdNq1XPHfqQT6SX1RiMYmSYkmwMHBgddee43bb78dX9/KBwsbMWIEK1asKJeUWLlyJSEhIaxYsaJcUmLlypXccsst5fbfu3cvXl5eHD9+nHvvvZfx48dz4MAB3N3d6+QcioqKsLW1rbPjAQya2Isrv3wFrTVHD6cy7/0f2bTkb/Yc386BvPJ9njN1IcsS9rPsz/089+dsnJQtndwC6d4ugmFjRzDm+svx79C2zspmTby8vOrt2EeOHKFXr160a9euym0KCgowGOqnObijoyOOjo71cmwhmoLiwkL2LlnFqt8Xsn79JnadOMShnJTzdscACLF3o6NfR7r0GcGomTMYMrYTzo62DVRqIYSoneSDUSz98gcWL1jC+iN7OHye1hBGbOjq3oauXUcy5tabGX9ZL9xdJN4J0ZTYNHYBhCnhEBoayosvvnjebdavX09+fj4A+fn5bNiwgSeeeIIVK85Oc33o0CHi4uIYOXJkuf39/Pxo0aIFffv25c033yQhIYGNGzdW+X5ffvklISEhODk5cckll/DRRx+Va7r73HPPERERwddff03btm0xGo1kZ2czc+ZMJk+ebN4uJyeHmTNn4uLigr+/Py+//HK1P5eEhAQmTZqEs7Mzo8b0xL+fJ78e/IcdGQks/Xs3Nw6/AS9D5b++5+hiIk+f4PPIRVz38qO0CA/Dy84Bextb7ht/NRt+mUdBbl6F/Q4cOMCUKVNwd3fHxcWFAQMGsHv37krfo6SkhBdffJGWLVtiNBrp0qUL8+bNM6+PiYlBKUVkZGS5/ZRSzJkzx/x6y5Yt9OrVCwcHB3r06MGmTZvKbV9YWMi9995LYGAgRqORli1b8vjjj1f5ubVo0YJffvnF/HrQoEG4urpSVGT6pfTw4cMopYiNjQVM3Tfuvvtu8/Njx47xyCOPoJSq0Fx72bJlRERE4OzszIgRI4iOjq6yHKGhocybN49vv/0WpRQzZ840n/+HH37I5ZdfjrOzM08++STFxcXcfPPNtG7dGkdHR9q1a8drr71GSUn5JubffPMNXbp0wWg04u/vbz5maGgoANOmTUMpZX799ddf4+LiUu4Yn376KWFhYRgMBsLCwvjss8/KrVdKMXv2bKZNm4azszNt2rTh+++/r/I8hWhIsTv28uvT/+Ou/hMZ4NUKT6MT3SaP4d6v3ubng+vZl5NcaUKiha0TI/x6cNeY+/jl6/VsTUph8cmNvP7nK4y/tIskJIQQTdrpmJPMfe4N7uw9mghnX/zC23L1a8/yzd61lSYkfG0dGdmiLw9c+QILVx9ndeJBPl/9KVdd11cSEkI0RVpra3lYpBtuuEFPmjRJL1iwQNvb2+sjR45orbVesWKFBvSpU6e01lofPnxYA3rVqlVaa61Xrlypg4ODdXZ2tjYYDDohIUFrrfUnn3yiHR0ddV5eXqXH0VrrrVu3akDPnz+/0jIBWimlX331VX3w4EE9e/Zs7ePjo01fF5NZs2ZpJycnPWbMGL1161a9e/duXVhYaD6fM+68804dGBioFy9erHfv3q2vvPJK7erqqm+44Ybzfi6A9vLy0p988ok+ePCg/r//+z+tlNJbtmzRWmudnZ2tAwMD9aWXXqpXrNmqn7jzZe3m4Kq97V20v62TxtSf8rwPB2Wre7kH61t7j9ffPfGK3rpitfb29tZTpkzRmzZt0gcPHtTfffed3r59u/mcO3fubC7jW2+9pV1dXfUPP/ygDx48qJ955hltY2Nj3j46OloD5jKXPbfffvtNa611VlaW9vX11VdeeaXevXu3Xrx4sQ4PD9eAXrFihdZa6zfeeEMHBwfrVatW6WPHjul169bpL7/8ssrPbvr06fr22283f04Gg0F7e3vrDRs2aK21nj17tg4LCzNvP2zYMH3XXXdprbVOSUnRwcHB+tlnn9Xx8fE6Pj5ea631V199pe3s7PSoUaP0pk2b9M6dO3X37t312LFjqyxHUlKSHj16tJ4+fbqOj4/X6enp5vP39fXVn332mT569KiOiorSBQUF+plnntGbN2/W0dHR+pdfftHu7u76888/Nx/vk08+0UajUb/55pv6wIEDOjIyUr/22mvm9wL0Z599puPj43VSUpK53M7OzuZj/PHHH9rOzk6///77+uDBg/q9997TdnZ2+q+//ir3/xMUFKS/++47ffjwYf34449re3t7HRMTU+W5XkBjx0aJwxYqM+GUXvrBl3rWpGv0hKBOOtCuerEN0KH27nqEX3d965Bb9RfvLNExsbm6pKSksU+pTpW9JwlRDY0dHyUW10BWfJJe9OoH+oGBE3RPF39tizpvzLNH6QinID21w2T94oPf6R37MnRRseXHPIlzwspUGbcaO2g2+wBc9o/44cOH66uuukprXXkyITg4WD/33HNaa9MfyNdee63WWusBAwbon3/+WWut9VVXXaVHjRpl3ufc4yQnJ+spU6ZoV1dXnZiYWGmZAD1u3Lhyy2699dYKSQk7OztzMqSy88nMzNQGg0F///335vWZmZna3d29WkmJW265pdyyUaNG6WuuuUZrbfrD2s3NTWdkZFQ418ht+/RfP6zXj1z6iPYwuGo3G6NW1azI26L0+OAI/fxlt+i138/RuRmZ5c65bFIiMDBQP//88+XKOGzYMHMZq5OU+PTTT7W7u7vOzDz7Pt999125pMQ999yjR44cWe0/KD766CPdvn17rbXWS5Ys0eHh4fr666/XL7/8stZa6xkzZpT7bMsmJbTWulWrVvr1118vd8yvvvpKA/rAgQPmZd9//722t7fXxcXFVZZl0qRJFf6vAX333Xdf8Dwee+yxct/loKAg/dhjj1W5fdnPtWy5yyYlBg4cqG+88cZy29xwww160KBB5Y7z+OOPm18XFhZqR0dH/d13312wzFUVrRk9xEUqzM3T235fqD+88T59dXh/3dHR+4KV8DMPLxuj7usWpqd3vlQ/c+MbeuGCQ/pUWmFjn1K9k8q6qKHGjo8Si88jLz1DL3vvc/3kiKm6v0eQNmJz3rhnAzrc0V9PbD1GP3zNG3rBv8d1arr1xT2Jc8LKVBm3ZEyJJuS1116jf//+VQ4weGZciVmzZrFixQquv/56wNTkfsWKFVx11VWsXLmSe++9t8K+Z5qzZ2dn065dO3777Tf8/PyqLEvfvn3Lve7Xr1+FZu7BwcH4+/tXeYyjR49SUFDAgAEDzMtcXFzo0qWL+fXLL79crkvHvn37CAkJASi335nXCxYsAGD//v107dq13OCJAwcOxMbGhtgTh5kyYwqXzBjArYdvpXPnzvj6BPDAVU9yeOMGjhzdweHTMcQX51QoczGaxSf3sPjkHmb9+Tn2KMKcvOkc2JY0YzE56Rnkns6kUGni4uIYNGhQuf0HDx7MwoULq/xMznXmPMp2MTj3vGfOnMmYMWNo3749Y8eOZeLEiUyYMAEbm8p7Xw0fPpz//ve/xMXFmQc67dOnDz///DNPPPEEq1at4n//+1+1y3iG0WikQ4cO5teBgYEUFhaSnp5e43EpevfuXWHZJ598wueff86xY8fIzc2lsLCQVq1aAZCUlERsbCyjRo2qcbnL2r9/PzfddFO5ZYMHD+avv/4qt6xr167m53Z2dvj6+pKUlFSr9xbiDK01JyJ3sf6P+axbuZbIg/vYnR5P9gUGowRwULaEOfrT0qcdbTv3o8ekSQwY35uwVo4yMKUQwmJorYlZvYkFn33L4hXLWR1/lMwLDsjrTahPBGG9RjP42qsYPCiEkABDk5sZSAhRc5KUaEL69OnDFVdcwWOPPcYzzzxTYf3IkSO54447SEtLY9OmTXz55ZcADBs2jPvuu4/9+/eTmJhYbraNM1asWIGXlxe+vr64ubldsCzVCfDOzs7nXa/1hWfhuOOOO5g+fbr5dWBg4AX3OXPsqspYdvnGjRspKSkhOzuDK+4ZR7v3TGMnJKUWsn7hVjb8MZ+D29cTlXiAPbkJFXpiF6LZn5PM/iPJ5mVuHu60dvAA4KcX38H+ZDK9p4zHydO93PufSRqU/RwKCwsrnMeF9OzZk5iYGBYvXszy5cu54YYb6NatG//++2+liYmOHTvi7+/PypUrWblyJffffz99+vThnnvuYd++fcTGxl7UVKN2duXDxZnzPHfch+o497vzyy+/cP/99/PGG28wcOBA3Nzc+PDDD/nzzz+B6n1O1VXZ9+bcZWVnIzmz/mLOUwiAjJMJbJzzFxuXrmTDju3sSDxGQlHuBfdTQKjBgxD3NoS07kHHoSPpf9lYunbywNtDbt9CCMuSm5TCys9/YOGf81i6ZxsH8tLPu30rgwetvTrSqvMIek+bxpDh7enUVhKwQlgjqdU0MS+//DKdOnVi8eLFFdaNGDGC/Px83nzzTXx9fWnb1jSbxODBg4mKiuKHH37AxcWFPn36VNi3devW+Pj4VLscmzdvPu/r6ggLC8Pe3p6NGzfSpk0bwNRSY8+ePeaye3l5Vfkr+8aNG8v9qr1x40Y6duwIQKdOnfjyyy/JzMw0t5ZYv349JSUl5m1iYmK4++67+fDDD1m8eDHXXHMN69evx87ODj8ve6Ze25+p1/YHIC+/hP/+9yHm/vYdk9uN4VjMbqIyjnGyKKtCuYrQHM5LA+CLVX/zxaq/sUXR1tGTVF1AkF8LVn7xIxHjhgMQHx9v3nfHjh3ljtWpUye++eYbsrOzzX+oVzYAqaurK9OmTWPatGnMnDmT/v37c+TIEdq3b1/pZzds2DAWLFhAZGQkw4YNw8/PDx8fH1577TXCwsIICgqqdD8Ag8FAcXFxlevrw9q1a+nXr595wE0wtbQ5w9/fn6CgIJYtW8aYMWMqPYa9vf0Fy92xY0fWrl1b7nu1du1aOnXqVMszEMKkMDuXXX//y4aFS9iwaTPbjh/hUG4a1Ulp+dg60MYlhMCAzrTpOYTeUyfRo3dL2rZ0kEq4EMLi6JISjvyzmgVffc8/a1axOjGaHF31fdrfzpl2nh1p0WYw3aZOY8joLnQPd5KBKYVoBiQp0cSEhYVx22238e6771ZY16pVK1q3bs17773HpZdeal7u7OxMz549ee+99xgyZEiFX7QvxpIlS3j99deZOnUqq1evNv9iXRMuLi7cfPPNPPbYY/j6+hIYGMgLL7xQ7T94//jjD/r06cPw4cOZM2cOy5YtM89Mcc011zBr1iyuv/56XnjhBdLS0rj99tu5/PLLCQsLo7i4mGuvvZZhw4Zx++23c+WVV9KlSxeef/75Smc5cTDa8OILDzNv7rdktMzl9Y+/RNu48uevC8g4HE9m9HFWH1xJfF4qhZX8eVGM5lBuKgDJx48w4pZrsMHU1PrG6TO4ZuhY2vWM4JfVS8vtN2PGDJ566iluuukmnn32WeLi4njppZfKbfPWW28REBBA9+7dsbe358cff8TNzY3g4OAqP7vhw4dzzz33EB4ebu6mM2zYML7//ntuvPHG837uoaGhrFmzhmuvvRaj0VijZNbFat++PV9//TWLFi0iLCyMn3/+mVWrVuHp6Wne5qmnnuKBBx7A39+fSZMmkZOTw7Jly3jooYfM5V62bBnDhg3DaDSW2/eMRx55hGnTptGrVy/Gjh3L4sWL+eGHH/jjjz/q/RyF9SkpKeHY+q2sn7uATWvXs/ngPnafTjhvpfsMR2VLmGMLArzaExzej85jJ9BnZHc6tXWSVhBCCIuVHZvIsk++YfHff7Nk306OFmRUua09NnRyDaFFwABaDr2CoVOGMaCbC21bGqVLhhDNjNR8mqBnn32Wb775ptJ1I0aM4Msvv6zQ/H748OFs2rSpwlSgF+uzzz5j1qxZPPvss4wePZrHHnuMp59+usbHeeONN8jOzuayyy7DycmJe+65h+zs7Grt+9xzz/H7779z77334uvry1dffWVuBeLk5MQ///zD/fffT9++fXFwcODSSy81J3Nefvlljhw5Yp7O09vbm2+++YaJEycybtw4Bg8eXOH9goKCWL16NY888ggjR45EKUWXLl2YPXs2ERERzJo1i19+ncM7r//J5rkLObRlHWsPr+JkbjLFlUzBVwLk6WLy8rN5/98/4d+ziZ2Xbn+QLZ/+Rp8hfZn92lu8+P7b9OzZk/DwcP73v/8xZcoU87aurq68/vrr5qk8e/TowaJFi3BycqrysxsxYgTFxcXlvicjRozg22+/vWDXjRdeeIHbb7+dtm3bkp+fX6ddJ6py++23s2PHDmbMmIHWmiuuuIKHHnrI3EUJ4M4778RgMPDmm2/y2GOP4eXlxcSJE83r33zzTR588EFatmxJUFAQMTExFd5n6tSpvP/++7zxxhvcf//9tGrVio8++ohLLrmk3s9RWL7M2EQ2zfmLDf8uZ+OO7UQmHCOpuOLUwueyAVobvQh2a41fyx60HTiKXpNG0CXcnbbBRuykFYQQwoJln4hn3qvv8fOff/BvwhHydNVtwwLt3Wjn0xW3tmPoPGU6Q/u3oF9XF7zc5U8SIZoz1RB/cDQQqzmRxqaUqvCH6AMPPMDSpUvNf+QLk5ISTfypQnZuOUbk3MUcjlzH8RN7OZ55gmOFp6v9pXSzsaeDmz+dQtrSs3d3+owaStdxI3D2rvhrv7A4zekvTquJwyWFRexfsor1fy1i44YNbI46yP7s1EoTkOfys3WitWsIPr6dCeo6mK4TJ9K9RwAd2zhKxbsOVXavEuI8JBbXofykVBa98TE///oLC47vJ6uKQSqNypZOrq3xCxyAW+8rGDi2HwO7u9A93AmDfeUDdouzJM4JK1NlHJakhKhAKcVrr73GmDFjcHFxYenSpTzwwAO8/PLLPPDAA41dvCavpERzMrGA3VtPEDnvH45EruPEiT2mREVBerX6loPp19VQozsd/FrSrVMneg/qR8/xI2nVq2uVM2+IJkkqwhYg9cgx1v/yJxuWr2LT3l1sPXWS9JKCC+7npOxo5xSAn2d7vNv0o92IcfQc2oVObRxpI60g6pVU1kUNNaeLsV4ujKKMLFa8+zk/fv8Dfx3ZRWoVMbKlwYM2vt1xaD2WViOmMqSPL/2lW8ZFkTgnrIwkJUT1KaWYPn06K1eu5PTp07Ru3Zrbb7+d++67T24mF6m42JSo2L8nnu0LlnN0ywZij+0lLv0Y0bmnyNSFFz5IqTOtKjqHhtGrdw96jRhM1/EjcfbyqL8TELXRnC4ai4jDRTl57Jq/hPUL/mHj5s1sOXaEw3np1Sp8qMGTEPc2uPt3J7DPKLqNHUaX9q50aiutIBqaVNZFDUksvgglOXmsn/0DP335NX/uiyS+ii5rgQZ32gcNwabz9QR37cXYAe6M6u9GCx/7SrcX1SNxTlgZSUqI6pMA2HBSTxdx5FguO5dtYf/KVZw4uJ34xMOczI7jeGFGtb/UZ1pVhPu3pHunzvQc1Jee46RVRRMhFeFGVpyXT+TP81j8y+8s37KRramxZFdjMEp3GwNhLi3x8e6Ee5sBhI2aSLeeLYkIcyQsxChNjxuZ3KtEDUksroHi01l8MuNO3vx3LtGFFWciA/C1cyY8YCD2na/BNWwAI/u7M2aAG+GtHeRHrDoicU5YGUlKiOqTANi48gtKiI7N58DueHYtXkH0to3EHd9DwunjF9WqItzdn4jQMHr27kHP4dKqohE0p5pZkwgcWmvituxi0WffsGT5clYcO0Bycf5597FF0cbBhyCPMFz8e+HXazRdhvcjIsyRTmGOBPjYSyW7iZF7laih5nQB1+rCSFy6gVunz2B+WkyFde42Rjr798Gh09XYthnBgG5ujBngRt8uzpKorQcS54SVkaSEqD4JgE1PSYkmIbmQI8dy2bUikoOrVxF7cDuJyUeIvZhWFQ7udPIPoXvnTvQY2JceY6VVRT2SinADyD2VyqrPf+CfefNZunsre3JSz7u9j60jbVxb4e0TgUPoINqMGEfXCF86t3UkvI0Dzo62DVRycbHkXiVqSGLxhXYqKmbpfS9w46evE1uca17urOyJ8O2Oc4cr0WET6djGlbED3RjexxV3V+m2Vp8kzgkrI0kJUX0SAC1HRlYxUSfzObAnnj3/LOP4js0knNxLYsbFt6ro2qYdPXp2p4e0qqgrUhGuByVFReyd/y+Lvv2ZpRvWsjbpGLnn6ZLhaWMk3KMDnsGDKWk3kY59I+jXxYVenZ1pHWzE1qY5/TdZB7lXiRpqThd5jS+MvKiTzJpwFW8cWl9uQO4eLUfjPvx/2Bqc8XC15fFbAujd2bkOiyrOR+KcsDKSlBDVJwHQshUWaY7H53PkWC57V27lyNrVxB7eRkrq0Ysbq8LBnc4BIfTq2pU+I4bS77JJeIcE1ecpWBupCNfVwbVm8+c/8ek77/Hv4d2cLMypcls7FOHOQQT698am7STcuw6lX1dTE+Pu4U7SEsIKyL1K1JDE4irE/rKYK268jk25yeZlrrZGIgY+h1P7ywDo2t6RJ28LxMdDWkY0JIlzwspIUkJUnwRA66O15lRaEVEn8jmwJ4EDS5dxYudmkuL2kZRZ81YVIUZXugeG0rt7d3qPGErfqRPwbimJiipIRbiWirNzmTfrdd75YjZr0mOr3C7I3o0w7wicWo2kOPwSunUJoG8XZ/pEOBMSYJAxIayM3KtEDTWnAFDtC0Pn5HFrQARfZBw1L+vg1Q7f0Z9gdDXd12dM8uKGKT7Y2janj7BpkDgnrIwkJUT1SQBsPnLySog+mc/hmGz2rd5KzIY1JBzdQWpazVtVtDK60SO4tTlR0WfqBLyCAuq1/BaiOdXi6jRwZB6L5csHn+GDv3/nSEFGhfVOyo5O7m3xCRxIcbsp+IV3pW8XZ/pGONOtgxOODjJGijWTe5WoIYnFlW1YXMy9vhF8kHYAgLa+AYz87xqiThYB8J8JXtxyhW/9lFJckMQ5YWUkKSGqTwJg81ZcoolNLODI8Xz2747n4NLlxO3ZSFrSXuIyjnE0P4XCal5uoQ5u9AhuQ+/u3ekzajh9p07AvYVfPZ9BkyMV4Ro6sXYL7z38NF9sWUlaSUG5dbYoenl3wSviBvJbj8HD3ZHJw9wZO8CdIH+ZIaM5kXuVqKHmFBxqdGHsefh1erz5GEWlu7309iKW72kNQFiIkY+faSWxtZFInBNWRpISovokAIpzFZdoTiYUcDAmj/37TnF46TLid60jPXkvJzOOEZWfaq7MXEgbR3d6tGxDn569GDxlAr2nTsDo6FjPZ9ComlNNrlaB4+iilTxz1/38Hr2bgnJDrYGLsqdn8HDs+tyP8mxLyxYGrhjjyej+bjgYpUVEcyT3KlFDEourUJyYyoy23fk1+wQAo3oPwK7n5xQVm8beee+JEDq1ter7dJMlcU5YGUlKiOqTACiqo6hIcyw+n4PReRzYl8SRpctI2LuO08n7OJl5jKj8tGolKozKhu5egQzo0p1Bo0YweMbltGgTWv8n0HCkIlwN0X+vYNDUScSXmYYOIMDelU5hl1HS8y5sHNzp0dGJK8d40ifCGRuZMaNZk3uVqKHmFDBqfGFsvulp+n/1knnHFg7utAmbhn3323B0dmf8YHemj/MkwNdQx0UV5yNxTlgZSUqI6pMAKC5WQWEJUSfzORiTx4G9SUQvW0rS/g2cTtnHiYxjRBWkUVyNS7W1ozv92nZg4MBBDL78ErqMGoKdncWO+C0V4QtI232IIX36sTc/3bysg3MQIRE3UNTxagwGe0b2c+OKMZ60belQV2UVFk7uVaKGJBafR1F0HDO6DuC3rOPlljsqe7qEjMDQ+34cvdowvI8r/5ngTZtgY50VVlRN4pywMpKUENUnAVDUpdz8Eo4ez+NAdB57dyUQtWwpGVEbSD+1h6OZMZwoyrrgMVxt7Okd0Ir+3XsyaMJYBk27FA8/nwYofZ2QivB55CelMqFDN1aknwRMU3kOH/oSut3lAIQGGXj1/mB8PO3rtqTC4sm9StSQxOILyNyylxduvJMv9m0k7ZwZuRQQ4d0Vt153YQweSv9uLlw9wYuIdk51UV5RBYlzwspIUkJUnwRAUZ+01iSmFLH3SC57DudwcN1ukrcsIjs+kpPphziYe4r8c8YTOJcCOrr60C88gqFjRzP5zpvwabozfUhFuAol+QVc36EPPxzbZV42ut9D6IhbAQjwteedx0Lw9rDYVjKiHsm9StSQxOJqyth1iK8fmcWHKxZwqDCzwvpQpwBat7+SkrDJdO8dztSRHvTs6Iybi21t3lZUQuKcsDKSlBDVJwFQNLSsnGL2R+Wx90gu+3YlcnLlInKPrSc5eQ8Hs05wqiTvvPvbAH39WzFxxCguvfNmugwZ0JRGCm8yBWkANQoc8+5/jqnvPm9+PabLNEr6vgiAt7stbz8eQqD0XxZVkHuVqCGJxTVUnJzO/Gde4/0fvmF5Zlyl27Swd6OVZwdcgwYSOPASBg3rQs9OzkSEOcogxHVA4pywMpKUENUnAVA0tuJizdET+ew9ksveQ1lEr9pA5r5lZCRu59jpoxwqSD1vW4oQRzfG9+zPlGv+w8iZ/8GxcWf3kIpwFX64+SGu/fIt8+upl31OptdgAKaP8+S2ac1u+lhRA3KvEjUksfhiD1ZQyPaPf+DdN97k15P7yDvPHdjf3pVQjw64txxIq2FTGDK8Kz07udAh1AE7u+b0X1A3JM4JKyNJCVF9EgBFU5SYUsjeI7nsOJjDjrVHKdi1hPzYDUSd2sHu/FNVBgAnGztGtOnE5EsmM+WuWwhs27pBy41UhKtUkJLOoFbticw+BUCwgxthVyzB1sEDg73i8xdCpaWEqJLcq0QNSSyu7UG1JuHfdXzx6lss276FzafjydHF593H186F1p7t8Ww5kHajL2XoyB706uRMaJBRZlCqBolzwspIUkJUnwRA0dRprTlyPJ8NO7PYuCubpMhd2B/6g7jYtWzPjCFTF1W5b3fvQCYOHcH1zzxChx7dGqK4zanWVePAceivpfSZOoGM0v+zQYG9MI7/DqVs6NvFmZfuDWpKXXFEEyL3KlFDzSmQNMiFkRebxKYf/2Dlwn9YtXMrm9PjyL5AksLHzpnWHu3xC+lLu4HDGXLJMLqEexIaaMReWlJUIHFOWBlJSojqkwAoLE1yehGbd2WxYVc2e7fG47RvLukxS9mTupdj55ndY1y7Ljz43DOMufrK+vzDtznVsi4qcHx74wPc8PU75tdtjN6Ed7mFvC7X0ynMmRsv86FnR+e6KqOwEnKvEjUksbie5Scks+nHP1j192JW7dzKprQ4ss7zIwGALYqWRi8C3FoR0LIzrXsPoteE0fToGkiblkYM9s17XAqJc8LKWEZSQillCzwHXAsEAPHAD8BzWl8gqklSos5IABSWLL+ghB0Hcti4K5vNO05TsnMVRUf+Ijoxkh25iRRXEio6evpz7x13cv3Tj+DkVOfTm1lcRbgWsfiiAocuLua6sF78ELOz3PI2Ri86RdxETtcb6dHJlZsu86FT20YdH0Q0IXKvEjVkUbHYGurE+YkpbPl5LivnL2LV9kg2p8WaW8VdSAs7F4JdWxIYEE6rrn3pNn40/QZ2om2IA47NaABNiXPCylhMUuJJ4GHgBmA30BX4BnhLa/3iBXZvOidi4SQACmuhtSbqZD5rt2WxcHU6RceO4nDgd45HL2ZtzokK23vZO3DLlCu4982XCWoVUlfFsKiKMNQqFl904MhLTuOJSdP5dMsKcs9p/tva4ElE5xvI6XYz/Xt4MHOqD2EhDhf7VsJKyL1K1JBFxWJrrBMXnkoj8ud5LJ+/kE17drM3+STRhVnVLqy7jZFWzgEE+rUjtGMvOo8cwaDxA2gf6oSzo3VORypxTlgZi0lK/A2kaK1vKLPsG8Bbaz35Ars3nROxcBIAhTUqLNKs2pLB7/+mcfhYHsExy0nb+RlLU3dXGKjLDsUVvQfx1Efv0KVPr9q+tUVVhKFWsbjWgSNh5z7+d8cDzN60rML/S6jBg24d/kNel+sYNCSUaeM86RDqIGNONFNyrxI1ZFGBojnUibXWpO8/yvaFS9myaj3bdu9hX+IxDuWlU3DeObbOclC2tHL0I8irNaFhXWnfpy89JwyjW7eW+HraWfz9QeKcsDIWk5R4HPgvMFZrfUAp1Qn4B3hFa/3RBXZvOidi4SQACmumtWbP4Vx+/zeNdTuy8Ek+jM3WD1gVt5rY4txy2xptbPnlhx+59D/Ta/OWFlcjqkUsrrPAkbjnIK/dcT+frl9K9jnNfQ3Y0N21NYEhw3EbcC39R3diaC9XOrZxkNHcmxG5V4kasqjg0JzrxPkJyexevJxNS1cTuXU7+05GcyD7VLW7fgB42zoQ5OhHoHcrgluH065Xb3qOG0b33m3w9rCcZIXEOWFlLCYpoYD/A54AigE74CWt9dNVbH8bcBvAp59+2uu2225rqKJaNQmAormIO1XA3GXpLFqTjk16Gj7bP2Nn1F9szT9l3sZWKb786FOuv+PWi30by6j5lFGTWFzfcTjp4FFev+0+PlnzT6UDptmi6OoURKugwbj2m0Gf8X0Z0suVLu0dsZUEhVWTe5WoIYsKCFInLq8kJ49Dy9ayefEKtmyMZE/MIfafTiCxOK9Gx/G0MRLs6EugdwghoeGE9ehF93FD6dm/Q5NMVkicE1bGYpIS/wFeBx4B9gLdgXeBR7TWX1xg96ZzIhZOAqBobrJyivnqz2TmrUjHrriIjru/588d73GsOMe8zTsv/4/7nnj0Yg7ftGo41VCLWFxvgSP5SAzv3/cYv69ext6slCq3i3DwIyygP+7dp9P10pEM7e1G9w5O2MlUc1ZH7lWihiwqCEid+MJ0cTHxW3axecFSNq7ayN6jh4hKjSMqL528anb/OMPNxkCIgw+B3i0JCelAWPee9Bg7hN5Du+Dp3njJColzwspYTFLiBPCG1vrdMsueBmZqrcMusHvTORELJwFQNEdaa76am8yPC1IB6HlkMX+ve4b9RZnmbZ595DGef+3Vmh7aoirCUKtY3CCB4+Dqjcx57xPmLv+XyLS4Krdrb/Ag3K8XHu3HEnHZVPoNCKBbByfcXaxzQLTmRu5VooYsKhZLnfjiFWVmc3jNRiKXbmBn5A4ORx8hKiWWo3lpFQZSvhAXZU+IozfBnsEEh7QnrEs3ugwfQJ+xffHzMtR7skLinLAyFpOUSAFmaa0/KLPsCeBWrXWbC+zedE7EwkkAFM3ZD3+n8NXcZAB6Rq9i+ZrH2VqYZl6/ZMkSxowZU5NDWlRFGGoVixs8cJzYs58/3vqIPxctZG1CdKVTvgI4K1u6OAUR5NudwO5j6HzpJXTr6U2Xdo5WO2q7tZN7laghi4rFUieue8V5+Rxdt4XIJWvZsXkHh6IOEZ0cS1RuaqXdA8/HiA0hRk8C3VoQHNCG1uERdBrQlz4TB9M6zLvOug9KnBNWxmKSEl8Do4HbMTVV6wHMBr7VWj90gd2bzolYOAmAorn7+Jckfv/XlIi4KXslL815lH1FGQB88cUX3HTTTTU5nEVVhKFWsbhRA8ep4yeZ+/bH/Dl3LsuPHSBfV91810PZ08WlJUF+PWjZexwdL51At+6edG7riNFg04ClFhdL7lWihiwqFkuduOGUFBYRs3E7mxavZufmbRw6cojoUyc5mpNCpi6s0bFsgCA7V4KcfQnybUWrtuF06NWT3hOH0rlPWI3vLxLnhJWpfVJCKdUaCAUcgVPAbq11zUaXufB7uAIvApcBfkA88DPwQjXeS67YOiIBUDR3n/ySxJzSpMQzxk08/vkDbCwwdev4999/GT16dE0OZ1EVYahVLG4ygSMzNY2/3pvNorl/sfLALmLzs867va+Nka4urQho0ZNW/SfS8dJRdO3iQXhrR+xlPIomSe5VooYs6kKWOnHjKyku5sTW3WxcuJodGyI5fPQwMUkniMlOJqUkv8bH87Yx0tLRhyDPIEJC2tGua1e6jhhI73F9cHc3VrqPxDlhZS4uKaGUCgXuBK4Ggs45UAGwBlPW9netz/OTVMOQK7aOSAAUzd2jb55g237TIJcfZf/B6N9e5ETpdKEHDx6kffv2NTmcRVWEa6lJBg6tNUe372LJNz+x7N9lrD6yl+TC3PPuE2zrRGfXUPz9uxHaZxSdpoymYzdfOoQ6SEuKJkLuVaKGJBaLOnPqcDSbF65m+7pIDu3fT3R8DMcykjhZmFnjD99J2RJi8CDYPYDggDaEdepK12H96TdlKP6BbhLnhDWpeVJCKfUuMBNYAvwFbAbigFzAC4gAhmBKWBQBN2qtt9RlqWtIrtg6IhU90VzlF5Tw3fwUfv0nFVVUxJjDSzi880NmZx41b5OTk4Ojo2NNDisV4SZGa82eDZv599ufWbZsBWuj95NRXHDefTyVPRFOLQjy6kBwhwGEjx1Pu+Gd6dzOGW8PuwYquShL7lWihiQWi3qXm3aabUvWErl8I/t37ib6RBTHUuOIyU87b5fCyhixIZ8SRvl0omVAG9p2jKDr0AH0nTwE/xCPJjd9qRDVcFFJideB/2mtky94dKUmAk5a6zkXXcTakwBcR6SiJ5qj3YdyePObBE4mFtI6/Th9N7/Pq3HLiSrONm/To1t3tu3YXtNDN6dag0UGjuLiYratXMPS739l6YpVbDxxiJyS8w96Zoeik8GDNq6hBAb3IGzgKNpPGkZ4V29Cg4x1NsiZqJrcq0QNNaeLUi6MJqaosJDDG7ezcdE69kRu5+jRQ8ScOsGx7GTSS86fFD+XARtaGdxo6dqCVi1a0yY8gohB/eg7ZSgBbXwkWSGaMssY6LKWrOZEGptU9ERzkpNXwue/n+KvFekYi/K5ZP98duz9ktnZUeWCyphBQ/jql58ICgqq6Vs0p9qBVQSOwsJCNi5eyvKff2ftho1EnjhCetGF+w8H2jjQ2SmAIJ+OhEYMJHz8OMKGdaBjmDNODtLlo67JvUrUkMRi0STFHY5m04LVbF+zmUMH9hMdH01MZiJJRefvZnguexSt7N1o5eJPyxahtOkQQcTAvvS7ZCgBHVpIskI0BZKUENUnFT3RHBQUlrBySyZfzU0mNTmPPnHb8d7xOR8kR5ZrHeHq4Mib777DLbfeerE39OZUC7DKwFFSUsKBnbtZM2cea5auZOP+3RzNvGAjQhywoavRizbubWnVug8dRk6k1bhedI7wxM/LTiqItST3KlFDzemCkwvDCqTExuMTHMisq+7j4N49RMVFE5ORQFJRTo2OY4ci1N6VVi7+tA1sT0Svvgy4dBxdxvfA6GSop9ILUanaJSWUUp7Ac8AITCMAl/vJR2vtV7vy1QkJwHVEKnrCmp1KLWT+qnQWrj6NOpVK/6jlJB34hZ8yDpoHszxj9JChfPH9d4SEhNTmLaUibIVSUlJY/9ciVs9fzLrNm9kRH03uBbp82KLobO9GR5dWBAZ2p+3AsbQcM4D2vQMIa+mAnczyUSNyrxI11JwuMLkwrERlcS41MYkNf69m64qN7N+zi+iTUcScTiCxKLuKo1Qu1NaZ9i4BtA3qQKcefek3eTSdJ/TEyd2hLk9BiLJqnZSYD3QGvgESOSfYaa0/rWUB64IE4DoiFT1hbbTW7DqUy9zlaazblkm7lCg6H5jPluOL+S3nBNm6uNz2ro5OvPHO29x68a0jypKKcDNQVFTEjk1bWPv7X6xavprNh/YSl3v6gvt1sHOhi1Mgwb4RtOw6nOBRQwgZ1IZOYc64udg2QMktl9yrRA1JLBYWpyZxLj0llQ0LVhG5bAP7d+0iOjaK6PR4EgvPPyW2+b2AtnYutHduQZvADoR360OfSaMJH9cdN1/nWpyFEGa1TkpkAsO01tvqslR1TAJwHZGKnrAWufklLNuYwbzlacQdy2RA7Fbc9//GX8lbWZafVCFoeDq5cNttt3HvIw8RGBhYV8WQinAzdfLkSdbMXcCK+YtYE7mFg6lxF/yAQmwd6eXgR4hnR1q0H0jAkGEEDOtIp47uBPnbS5ePMuReJWqoOV08cmFYibqIc+lpaWxcvJYNi1ezI3ILB44fJCorkaJqfE1sgHZ2rrR38qdNQHvCu/alx4QRtBvTDa9g91qVSzRLtU5K7ABu0VpH1mGh6poE4DoiFT1hyQoKS9hxIIc127LYsDmNkBMHCYxZQeyxZSzMOcahooq/GHRsGcr9Tz7Gtddfj5OTU10XSSrCAoC0tDTW/PMvK//4m5Xr1rE7PoaiC0wR52djZIDRh3DPcHzbDsdlwCBajOxMtwh3WgUamnWSQu5Vooaa08UiF4aVqK84l5eXx8bl61gx5x+2bd7EgWMHic5KorgaXx1bFB3tXOnrGkLXDgPpPmkyba/oR1C4b7O+J4lqqXVSYhjwNPAwsEfrc9o6Nw0SgOuIVPSEpcnNL2HLnmzWbstk27ZU2h7bR4tjy4g+uYYlOSc4WEkiAmDisJE88PQTjBo1qj5vpM3pDi2Boways7PZuGYdK/+Yx8oVq4mMPkBe8fnHpQi3c2WQ0Y82Pl1wDRuOff9eBA4Pp3tnV1q2aF5JCrlXiRpqPheHxGKr0ZBxLicnh82rNrDs98Vs27iJfccOciyrYqvSc7kpO4Yafejh2Z5OPUbRavwwWk/tQYuWbg1SbmFRap2UCAJ+AQZUtl5r3RQ6vkoAriNS0ROWICunmI07s1i7LYu920/R8eQevKKXcThuPYtzY8vNoFGWs9GBmdffwL0PP0j79u0boqhSERbVUlBQQGRkJKvnLmDlkqWs37+LzIK8Krc3YENfgycDHVsQ7NcTm7BB2PTrTsiwdnTv6EKgn3V395B7lagh670YKpILw0o0dpzLyspiy7pNLP99MZEbNrIv5iDHs06dd5/Odm4Md/Cjk383WvcfheeY3rSf1AUvHxlAU9Q+KbEa8AQ+ofKBLn+vZQHrggTgOtLYAVCIqqSdLmLdjizWbsskakcCXeP24Bb9L/sTN7MoN55jxZVPk+VgZ8/4wcOYdstMJl9yCW5uDZq9l4qwuCjFxcXs2LGDf+b+xZJ5f7Nh704KSqpuqOhtY2CowYe+TkH4BfQju20vbAd0pfWgNnQPdyLA17qmfpN7laghicXC4jTFOHf69GmW/fMvc778ieXrVpKYlVrltu7KjqFGXwY7BtC2VX+c+g3Ae2wPOozpgLurXQOWWjQRtU5K5AB9tdZ76rJUdaxpXbEWrCkGQNF8pWUUsWZrJqsiM4nefYre8bsIilnB9oSN/Jl7kuPnTON5hrPByIQRo5h20w1MnDgRFxeXBi65mVSERZ3Izs5m9erVLP5jHv8u/of9J2POu/04oz9Pu4Xj4NGWxaFDSeg/kAmjfRnZ1w1HB5vz7msJ5F4lakhisbA4TT3Oaa3Zt28ff/85j7k//U7kgZ0UVZE8twFmuXXiVufWRLm35MB/7+I/t3TE2bEpNLgXDaTWSYlI4F6t9fq6LFUda7pXrIVp6gFQWL/TmUWs3Z7Fyi2ZHN6dTM/43XQ4sZ5Dsev4M/ck2wvTK93P1ejI5HHjmTbzOsaNG1cfg1ZeDKkIi3oRGxvLkiVLWDJvPkuXLyc5s+IUpHYoZjq14gHXdtg4eLG01SDWdRjMwGGBTBrmQZtgYyOUvG7IvUrUkMRiYXEsLc5lZmaybNky/p7zJwsWLCQhPbncehtgsc9gIuzdyTC48PWwm5l8f38G9XBtnAKLhlbrpMR44DlMg13uBgrLrtdaV91up+FYzhXbxFlaABTWITO7mLXbs1i1JYN9u1PpHr+X7rFbiD+5hrk5J1iRf6rSUaHdHZ25dPJkpl1/LaNHj8bBocn1WZSKsKh3JSUl7Ny5k3/++Ycl8+azYmP53xA8lT0PuLbjBqdWlNgaWRvcm8Whw/DuHsIlw9wZ2tsVg71ltZ6Qe5WoIYnFwuJYcpzTWrN3714WLlzIF59+xqGoIwD0MXgx16s/SikKbWz5ost0bKYM5+6r/fH2kC4dVq7WSYmyc5aV3UEBWga6tC6WHACFZcnLL2H11kxWbslk554MusXtoX/cdnJPruGP7GMszEsgS1ecjcDe1o6JY8dx3c03MmnSpKaYiChLKsKiwW3dupUHHniANWvWlFvextaZWW4dGePgTwmK7f6dmN9mFEnBrRk/yJ2pozzx97ZvpFLXjNyrRA1JLBYWx1ri3IEDB+jSpQtFRaY63bsBA5iGl3n9X21HsajbRG6Z5s+koe5WPUhzM1cnU4JWSWu96iIKVdcs/4ptIqwlAIqmS2vNis2ZfDbnFKfSimiTfpybd/9KYEYsT2fs5duc45XuN7BPX6676UamT5+Ol5dXpds0Qc3pziqBownRWvPHH3/w6KOPEhUVVW7dF569mODQAoBiZcP/+t7Ofu8w3Fxs+ey5UIv4tUruVaKGJBYLi2PpcS4xMZHVq1ezatUqvv/+e06fNnUzDPLxY7OxL6rk7Ln90HEKi1sP47//8ePy0Z6NVWRRv6qMw9WtdWzWWlc6mpxSKviiiiSEaJYOROfy8c9J7D2ah7Eon2sOLWZszBpySgqZmb6N5fnlp5pq37oN1944k2uuuYY2bdo0UqmFsDxKKQYPHsyoUaMqJCUO2+YzofS5rS6hRfYp9nuHkZFVTEJyoUUkJYQQQjQtcXFxrFq1yvw4cOBApdulJCeT26IIJ3W2sX1gVhIAx+PzG6Ssommpbq3jD6XUJVqXb0etlGoJLAfa1XnJhBBWJSW9iC//OMU/6zMA6HLqADfumYNvbhrxxXlcl7qFfUUZ5u0vv/xyHn/8cXr37i3N+ISooYyMDF5//XXefvttsrOzy62b3DqCW3KCzK9Vr06s8+kFgK0tFj34pRBCiIZz/PjxckmII0eOnHd7OxTd7N35r0tbc0KiwMaO7X6d+b39OLq2d+SGKT4NUXTRxFQ3KeEGfAdcfWZBaUJiJdCUZ+QQQjQyrTW//5vGN/OSyc3XGIoLuGn3bwyK2wbAwcJMZqRuJr4kz7zP008/zQsvvCDJCCFqKCMjg88++4xXXnmFlJSUcusG9+3Hk67hdN+XbBoCHTBOGMCumbdT8I1p2/DWjlYxXagQQoi6l5+fz6JFi5g3bx4rV64kJibmvNsbsKGHwYMBBi/6G7zpbe+Bk40d2XYOrPPrRGSLLuz26UDLNm48frkvvTs7Sd2vmapuUmIysEop9YHW+m6lVAiwAlNC4vp6K50QwuJF7s3hk1/PdsmYfHS5OSEB8HD2XnNCwtbWltmzZ3PTTTc1eDmFsGTHjh3jvffe4/PPPycjI6Pcuog2YTzVqi9DD5xGcXZ6NsOM8fw9eDq//nx2Aq0e4U1iGl0hhBBNRHFxMatXr+bHH39kzpw5pKenV7mtg7Khl70n/Q1eDDB408PggWNpi4jTRlc2+EWwtUUE+7zDKLaxI8jfnkem+jC0lys2NpKMaM6qlZTQWqcppcYBa5VS7wMTgXXADdqSR18RQtQ7p3N+de3awwvKtO7zbN0S9pl+pS0pKTGPzCyEuLBNmzbx1ltv8fvvv1NcXFxuXYi3H496d+bSDEdsD5wuty5++pX8r2QQKX+nmZcpBYN6uDRIuYUQQjRdWmu2b9/Ojz/+yM8//0xsbGyl2znZ2tPLzp0B9l4MMHrT3d4dY5lxItLcvFnhHUGkfxeOeLZCK1Od0NvDjuuneDNuoDt2dpKMENWcfcO8sVJtgbXAP1rrmfVVqIskyZE6Yukj/YqmZ9aHsazbngVAa18bXjnyNYXrdgKQSAEzDEfYfyzavP0bb7zBQw891ChlrQfN6W4rgaMBFBUVMXfuXN566y02bNhQYX07b39utgnkKlv/cpVDgLze3fjFbwhLaV1ueesgA//9jx89OjrXa9nrktyrRA1JLBYWp6Hj3NGjR/nxxx/58ccfqxykspWTO5cqH0Y7+NPV3h2DKv/j0+mAINZ6dGa9dwTHXQNNGe9SoUEGLhnuwfhB7hgN0lWwGar5lKBKqUwqD2pGoBAoObNAa+1WywLWBQnAdUQqeqIuFRdrNuzM4rmP4szL7hhYzOA3/w+dZmpmftK+mCm5W0k4dbabxzvvvMN9993X4OWtB1IRFnXmwIEDXHLJJZUOJtbJJ4DHioMYZfTDpmyfXAcDjleP5TvnvsyLKp90MBoUt0/zZdIwD2wtrOms3KtEDVnWF7x25MKwEg0V5zIyMpgxYwYLFiyodL2zrT3TjIFc7hhEL3uPCuM+2PXswAbvzvxS0J5EZ9/y62xhaG9XLhnuQUSYo4wZ0bxdVFLihuoeXWv9zUUUqq5JAK4jUtETtVFcook6kc+OAznsOJjD7sO5OKSmEJ4aRfvUaMLTotCnY4gsSCOyII0tBansK8qk+JxLODg4mBMnTjTSWdSp5nT3lcBRz+6//37efffdKtc7K1tCbZ1pY+dMa1snWts5mx7uvqQZgkhw8SfOxY84Fz/inf1JcvJC2dsS7G8gNNBI62AjrYMMtA4y0sLHvkn38ZV7laihpvtlrntyYViJhopzf/75J5dffvl5t3FStrS0dSTE1sn0sHMyvbZ3ISgklKhCPxKcfEh09iHRyYcEZ19SHD3QygZvd1tCAoyEBBgICTTQKsBIqwADHm62kqRoXmqelLBAVnMijU0qeqImSko00bFnkhC57D6YjcupRDqkRRGeGkWblCMkZx5nc0EqW0oTEWVn2qjK448/ziuvvNIAZ1DvmtPdVgJHPVu+fDlTp04lMzOzxvu6KjtTgsLWmdZ2TrSxdSbE3hVnt1bkuLUsTVb4E+/sR7yLLzg50irQSGhpkqJ1kJHQICNe7k2jEin3KlFDjf+lbThyYViJhopzSUlJ9OvX74KzaZyPp7InxM7pbNLC1pFAexecnAOxcQ8l1cWfRGdfEp28zQkLF2c7QgJNyYpWAQZCAk3JCl9PuyadFBcX7aJaSrhqratd66np9vVAAnAdkYqeuJATCQVs3ZfNjgM57DqYg1tCLJ1SjtAhNYpWyYfYm3WcLYWmBMS2wnRydfF5j6eUIiIigkGDBjFw4EAGDx5M69atz7uPBWlOd1UJHA0gMzOT/fv3c/jw4QqP842Kfj6eyv5sq4rSpEVrl5bYebQtbVXhx0nXAA55huLiZiA00FDaqsJIu1YOtG9lbPBEhdyrRA1JLBYWpyHjXGFhIYcOHSI6OrrSx7kzO9WEAlrYOBBi50QrW1MLiyB7F3ydg/Hw6kCiix9JTj7EuvhxyLM1Bkc7QloYTK0rAk0Ji24dnHBxsr3ge4km7aKSEgnAB8DXWuuTVWxjA4wDHgSWaa1frX1ZL5oE4DoiFT1RmVOphazYksmyjRkcPZGPd24qA2O3MzBuGwGZ8awtSObP3DgW5iWQpc8/g4azszP9+/c3JyH69++Pu7t7A51Jg5OKsGgQWmtSUlLKJSkOHTpken7oEFnZ2TU+5pWOQTzpGk4LWwcATrq04JvOl3PAu2257doEG7l8tCcj+7lisG+YwcvkXiVqSGKxsDhNJc5prUlLSyMqKqpiwuJoFDHHYigoLLyoYz/k0o6HXNubX2/168w7vStODe/qZMNDM1swuKfrRZ+HaHQXlZRoB7wETAV2AZFAPJAHeAKdgP5ALvAy8JnWuqTSgzWMxr9irURTCYCi8WVkFbNmmykRsftwLo4FOfSL38nAuG10SDnKzsLT/JEby1958SSV5Fd5nJCQEHMCYtCgQXTp0gU7u2rNSGwNpCIsGp3WmsTExIqtKw4e5MiRo+Tk5Va5r7Oy5V6XMG5zbm2ezWN9QA9+6ngJ6Q7lk4kerrZMGeHBJcM98HSr32tc7lWihiQWC4tjKXGupKSE+Ph4oqOjyycujhwl+uhRTiYmVHkejsqWLX4j8bIxmJc9NfghjrsFVrr9pSM8uH26b4MlwEWduvgxJZRSLYHpwBCgFeAIJAPbgX+AhY2cjDij6V+xFsJSAqCoH3n5JWzclcWyTZls2Z2FKiikW9J+BsZto/upfZwoyOCP3Fjm5sYRVVz5L6/t2rVjwoQJ5kREcHBwA59FkyIVYdGkaa2Ji4srl6zYGhnJ8hUrym0XaufMLNeOjDX6oZSiyOjA1qGT+cqxH9lF5SuH9naKUf3duHy0J22CjfVSbrlXiRqSWCwsjrXEuYKCAo4fP25OVkRFRfHzTz9x7PhxAJ6eOJ17C1tQtOcoALZXjyfh9ps4FlfA8fh81m7P4lTq2Va4bYKNPH17ACEB9XN/EfVGBroU1WctAVBUX2GRZuu+bFZuyWTdtkwKcovomHKEfvE76JOwi6S8FBbnJfBXXjw7C09XeowWLVrwn//8hxkzZtC7d+8mMRBeE9GcPggJHFZk2bJl3Hfffezdu7fc8mEGH15z70JLOycAVFhLtl51I9/FeJOcVrHrVs+OTtw2zZewEIc6LZ/cq0QNSSwWFsea49z333/PddddB4Cfnx8Hf/mb3BnPA6CcHfHb/g02Lqb7TGZ2MW9+ncDa7Vnm/R0MikduCmBYb+nOYUEkKSGqz5oDoDjrTCJidWQm67ZnkZddQETyYfok7KRnwm6icxNZnJfA4rxEDhRVPoatq6srV1xxBTNmzGDkyJHY2soARJWQirCwWEVFRXz88cc8++yz5QbR9LQz8qFbN4YbTfPRK2dH3H94gQ06iN//TeNgTPkZdowGxaN1XHmUe5WoIYnFwuJYc5wrLCwkODiYpKQkAH79/geGPPcnOtvUndDjsydwnDTIvL3Wmvkr0/n4l1MUFpk+EweDYs7bYTgYpSuHhZCkhKg+aw6AzV3ZRMT6HVnkZ+YTkXyIvgk7iUjYzZ6cOBblJfBPXiJxVUzbaTAYmDhxItdccw2TJk3C0dGxgc/C4khFWFi85ORknnnmGWbPnk1JianHplKKxzw7cbehFTZKodyc8f71Jey6tGXvkVz+WJrG2m1ZlJT5Vlw/xZvrLvGuk5ZUcq8SNSSxWFgca45zmzdvpl+/fubXi0ZcR7cDqebX3gvfwtC9fYX9vpmXzHfzUwBwc7Hl97fbSutcyyFJCVF91hwAm6PCIs22My0iShMRXU8doG/CTjok7GJT9kkW5yeyNC+JdF35yMkODg6MHTuWqVOnMnXqVDw9PRv4LCxac7pTSuCwchs2bODKK68kLi7OvGy8azDvOHXCzcYe5emK928vY9/JNKXvsbh8nv0wltjEs7FlWG9XHrmxRa1/2ZJ7laghicXC4lhrnCspKWHAgAFs3rwZgAnBHfiiOMy83u3/bsf5pksq7JebV8J1T0SRnmmaav72ab5MG+fVMIUWdUGSEqL6rDUANhdaa2KTCtm6N5vIfTns3nuawMRjdEw5SstTe0hN3MHW/FNsKkhlV+FpCqu4dDw9PZk8eTKXXXYZY8eOxdnZuYHPxGpIRVhYlYSEBK666ipWr15tXuZpa2CIvTcjjL4M92xJqwnDcBjfH+OIXmTbGHnxkzi27c8xb9/Cx57xg9wYM9Adf2/7iyqH3KtEDUksFhbHWuNc2fEkAP7w6k9/ozcArk/cgMs90yrd76eFKXzxRzIAvp52fPNya5mFw7JYTlJCKRUAvApMBFyBKOBOrfWqC+zatE7EgllrALRmWTnFbN+fw9Z92WzbnYljVDQdU47gl7CDlKQdbM1LYnNBKgeKMs97obRs2dLcGmLIkCHY21/cHwuiHIusCF9kLJbA0UwUFhby2GOP8fbbb1e6vqOdK8ONvgx3bsGQEcNxGTeIn3Pb8Mu28peDUtAj3Ilxg90Z3MMFo6H6lUu5V4kasrhYLHViYa1x7rbbbuOzzz4zv7ZFMdbox0znUEYMGoLbw9dgGNKtQreMWR/Gsq50sEtHo+LhG2WgSwtTu6SEUqoYCNBaJ52z3BtI0lrXyeh2SikPYBuwFvgAOAW0AeK01vsvsLv1XbGNxFoDoDUpLtYcjMkjcq8pCZG9K4rwpEO4xUWSfGo72/OS2FSQyvHi3AseKyIiwpyI6Nmzp/TLq3sW94HWIhZL4Ghmfv75Zx5++GFiY2Or3MZR2TLQ4MVwoy992/XiiGd/NnpHEOfsZ8pKlHJ2tGFEX1fGDXInvLXDBWOR3KtEDVlULJY6sQDrjXPbtm1j6tSpnDhxosK6trbO3ODcihmDRhH8+E3lkhM7DuTw5LsnKSg8+5lcMcaTW6/wxc7Ooi7x5qrWSYkSoEUlSYlA4KjWuk5GulNKvQwM01oPuuDGFVnfFdtIrDUAWiqtNclpRcTEFXDsZC7Htp4kZdtROLkH21M7yUs9zP78ZDYVpJJcUnDeY9na2NCjS1eGjhrJkCFDGDRoEL6+vg10Js2Wxd0laxGLJXA0Q1prdu3axT///MPixYtZu2YthUWVj08D0NLWkWFGX0LcAsj26EiJVwcKvDuQ4hZEutEVrWwICTAwtJcrIQEGAv3sCfY34OJU/vcPuVeJGrKoWCx1YgHWHecKCwv566+/+Oijj1i+fHmF9Y7KlksdAugd1pEOowcT1rs7of17EmvjyfOfJhJ36ux9pnOYI+MHuxPSwkBwCwPuLjIbXBN1cUkJpdSDpU9fB54HssqstgWGAC211j3qoJAopfYBi4EgYAQQB3wOfKgvfEVa5xXbCKw5ADZlWmvSMoqJicsn5kQeyXtOkrXnKCn7t5GTeIDMjBgSchKIKswgqiibfEoueEwHewP9evdm6OhRDBkyhAEDBuDi4tIAZyPKsKiKMNQqFkvgEGRlZbFy5UpTkuLvBRyJia7Wfi1sjATbueBn9MTNyR9H12BsPdpQ7NWeLO92lPj4EhhgJMjPQJC/gesu8eFQTC6BfvY4O0oFVFyQRcViqRMLaD518v379/Pxxx/zzddfk5FZ+TT0AHYoWto5EerqhaejH7aGIGw92lDiE06ObwS5Tj6AaVaOli0MFR4BPvbSoqJxXXRS4kxNohVwEigus7oAiAGe1Vpvqn0ZQSl1Zg7Ct4Ffge7A+8DjWusPKtn+NuA2gE8//bTXbbfdVhfFaPaaSwBsTOmZRcTEFnDsRA7Je2JJ37mf5P3byUraT3rGcRJyEokqzCSmOLsaqYezPJxcGNS/P0PGjmbo0KH06tULg8FQb+chqsXi7n41icUSh8WFREVFmRMUy5cvJyvvwt3KzuWADcF2TvgbPPB09MXFJYifj/3NtcPfIc+7EyoohIAAR4L97AnyNyUtgvzsCfIz4Oggg6AJwMJisdSJBTS/OnlWVhY//PADH77zLrsPXKiXUkX+to60MHri6eiHk2swth5tKfHuSJ5fBDj5YGsLgb5nkxTBLQzSuqJh1br7xgrgcq11Wl2WqpL3KQAitdYDyyx7GbhMa93xArs3nyu2njW3AFifMrNLWz4czyVl9wmSt+8hcd9WMhMPkp55nPjcJA4XZhJfknfhg53D39mN8Fat6dSjO10G9WPw4MF07twZGxupgDcxFlURhlrFYgkc4rwKCgrYsGED61ev5cjGSKIOHSY6KZ6TmWkU1+K+42NjoIW9Gz6OPrg6t8DoGoLyDKPQuyOGtp0JDHA+m6jwNxDsZ0+gn6HW05IKi2JRsVjqxAKab51ca8369ev598+/OLRuM1HHjxGdkkRSfvZFH9PNxoC/0RNPJz+cXUNMLfF8win0icDo7F++dYW/PS0DjLRsYSDQ1x5bW4sKH02ZZcy+oZQ6Bvyrtb6lzLLrgE+01heaj7DpnIiFa64BsC4kpRay+1Aue/emk75uPyV71pMdt4FjaQfZkp9MTHHOhQ9ShgJaunvTMbQ1nSIi6NS3F5379KJjx454eHjUyzmIOmdxd7JaxGIJHOKiFBYWcuLECY7s3MORbTuJ3neAqKgoouNiiUk7RVphzRO3Z4TYOTM64laO97wNrc4mIeztFOMHu3P9Jd54utvVxWmIps2iYrHUiQVInfxcWVlZHNm1l8MbIzmyczdHDx3m6MkTRKUkEpubSfFFfvV9gofQdezsSgdY9vOy461HQ2jhIzPS1YHaJyWUUlcBowA/oNxPC1rrKbUpXZn3+BHTGBVDyix7EbhCa93pArvLFVtHJABWj9aa2KRCdh/KYf+uVLI27KV4/1oy4zYRdfoIm/NTqt0Cwl7Z0NrLn45t2piSDwP6ENGnF+3bt8fJyamez0TUM4uqCEOtYrEEDlEv0tPTObpnH0e37ODo7r1EHT7M52v/pY2zJydyTlOoL9zRbZJ7B7yGvkqcX/kfmR2MimljvZg2zgsn6ephzSwqFkudWIDUyWuiID+f6MidHN4YyaEdezh6+BBRsSeITkniWF4GeRe4TwycvhRH1+BK1917jR9TRnjWR7Gbm1p333gduB9YgWmgnXI7aa1vrF35zO/TB1gPPAf8AvTANKjPk1rrDy+wu1yxdUQCYOVKSjQxsfnsPpzLoe1J5G7cRcmhtaQnRHLwdBSbC1NJvcDsF0YbO9r7BtI5LIxO3brQaUBfOvfuSdu2bbG3lwyslbKoijDUKhZL4BAN5sy9qri4mBNHjnJ00zaObt/N0QMHiT52jOjEOPanJ5FdUmTep4WNA3f2nMGJoQ8Sfbp86wgPV1uuu8SbSUM9ZCA062RR/6lSJxYgdfK6Upybz4nInRzZEMmhdZv5cOGf7CvKMK8fM3oM3/70NycTCzmRUMDuw7msjjw74Obnz4cSGmRsjKJbm1onJRKBu7TWc+qyVFW81yTgZaADcBzT3Mzvy0jDDUcC4FkJyYVs2JnFrh2pFK3fgcOhFSQlbGZvZgybC9LI1EXn3d/F4MDAzl0ZNm4Mwy+ZSO/evWXgyebHoirCZ1xkLJbAIRpMde5VCcdPcueUK5m7s/x43NM823D3o//ju5yeRMeVn740yM+e26b5MqiHa52XWTQqi4vFUicWUievW7qgkA/7Xco9OxaZlw3o1ZslK1eUm53uszmn+GVxKgA9Ojrx+kMtG7ysVqrWSYlTwACt9ZG6LFUdkyu2jjTnAFhSojl0LI8NO7PZv+YYtptWUHBsKUdT9rIuP4nkC7SE8HJ0ZnD3XgybOJ7hE8fRrVs3bG1lNN9mzuIqwrXQPAOHaBQ1uVf98uFs7n7oAZLzz47r46nseWbARLo8/jZfrCnhVGr5JPP4we7c9R8/mb3DekgsFhanOdfJ68OJ93+i/b3Xklc6t13v8E4s3bged3d38zZ5+SVc/ehRMrNN27xwdxADu7tUejxRY7VOSrwEFGqtn6vDQtU1uWLrSHMLgPkFJWw/kMPGrac5viSSwp3zORW/kW1ZxzhclHXefVu4ejCkd1+GTZ7A8HFj6Nixo8x+Ic4lFWEh6kFN71WnTp3iriuu5rc1y8ot7+HgxTuzXiKh+zR+WphKZs7ZfseBvvY8cWsAHds41lm5RaORWCwsTnOrk9e3H/texjVb5gLQ2tufrYf34+lZfqyIhavTeevbRABa+NjzzcutsbVpTuGjXtU8KaGUeq/MSxvgGmAfsAso19ZRa31v7ctYa3LF1pHmEADTMorYtCubyPVxHJv/B1lRSzmasocd+SkUnuer5O3kwsgBgxl7xaWMGDuGNm3aVDpSrxBlNKcviHUHDtGkXOy96q9f53DPbbdz/HTq2WMBM9v34dmfvufHrS4s33S2L7GNDVx3iTczJnrLtHCWrTn950ksthLNoU7eUIpOJPJIp8G8k2Vq+H/v7Xfw7icfl9tGa81tz8UQHWtqGX3HdF+uHOvV4GW1YheVlFhRzYNrrfXIiylVHZMrto5YYwAsLNLsPZLLtu1pHJy3hNjIBcQmbWd71glO68Iq9zPa2DKgQwRjL53MuGmX0717d2kJIWpKKsJC1IPa3Ktyc3N58c77ePPbLygoMyK7i7Jjetf+DL36fn4/2ZWc3LPrWgUauHayN0N7u8qvZpapOf2nSSy2EtZYJ28sWR/O4ZKH/svK/FMA/PDDD8yYMaPcNrsP5/DA/04A4GBQ/PxGW1ycpBt2Har9lKAWwGpOpLFZSwCMTSwgcncmBxZs4uiyucTHbmT36SiOFWefd7/OvoGMHjqc8ddfzdDRI2VKTlFbUhEWoh7Uxb3q0K493HXZVSyN2ldhXV/PIDp3ncaJVjeg7c923wgJMHDdJZKcsEDN6T9LYrGVsJY6eVNw+omPGPbmk+wsPA3AlZdfzq9z5pRr8bx47Wne+DoBgF6dnfjfAzLAZR2TpISoPksNgNm5xezYn82epQc4NH8usYdXcSDtALsLUjnfzMT+BmdGdu3J2MsuZdyNMwgICGiwMotmQSrCQtSDurpXaa359dV3eOb/XuRwTlqF9d62RnoHDYEed1Hk09G8XJITFqc5/SdJLLYSllonb4oK90Xz5bDp3JEcaV72zjvvcN9995lfR8fmc+usGACcHGyY83ZbDPbSQroO1XqgyxVUHuA0kAccAb7RWm+72BLWAbli64ilBMCSEs3h4/nsXH2cg38uIGrbEqJP7SIyL5FsXVzlfo42dgwM7cC40aMZf+M1RPTrLeNCiPrUnL5cTT9wCKtR1/eqkpISln36LR++8RYLovZQdM7XWQG9PNrjPeg5ilr0NC8PCTBw2zRf+neV0dmbOInFwuJYSp3cUmTNnss9D9zP1znHALCztWPturX069cPMCWpr38ymvhTpq7dr9wfTJ8I50YrrxWqdVLiI2AGEA9sKV3cB2gBzAW6lj7Ga62XVXaMBiBXbB1pygEwOb2I7ZuTOPT7Sg6sms+xuK1EZp8gviSvyn0U0NW3JWMHD2HcNdMZPHk8RqOx4QotmjupCAtRD+rzXnVy1z4+fWwWXy1bSGxhTrl1diiGtRyGGvIS2tHbvHxgdxf++x8/WvjY10uZRK1JLBYWpynXyS2RLikh/j9PMWnux+wo7cZx1VVX8fPPP5u3eff7ROavTAdMgxzfcKlPYxTVWlUZh+2qeYA84Gut9f3ljqrUm5gGuuyllHoX+D+gsZISwgoVFJawe+9pDs3dwp6F84g5up5dGVHsKco4737BTh6M6tWX8VdOZfSMafj4SEARQghRPcFdO/Hiot94NieXuS+9w8efz2ZFUgwARWiWnViJ9y+jGNx1Jpnd70HZ2LJ+RxZb92UzY6I308Z5SpNfIYRoYpSNDc5tgmlv52pOSrRsWX7ciMPHz/7Q6edV3T+VRW1Vt6VECtBfa334nOXtgQ1aa2+lVGdgvdbavX6KekGSRqwjjZ2VzcopZuXCYyT8MJfITfPYl7qfLfkp5J1nZAgXOwNDOnRl/CUTmXDDDMI6tJcuGaKpaE5fRInDosE09L1q6/e/c++997E+Lbbc8giXFrQa+BJ5LQeZlwX52/PAdS3oHi4DJTchEouFxWnsOrm1KUnL5NjgW+ix/w9O6yIANm/eTJ8+fYDyY0rY2sJvb4bh5iKzb9ShWnffSAVu0lrPPWf5VOBLrbVXaYJik9bas3ZlvWhyxdaRxgqAWTnFLPzxIDEffETk4b+YlxNLQRWJCFsUPYLaMn7saCZc/x/6Dh6EnZ1kM0WTJBVhIepBY9yrSoqK+P6BZ3jik/eIKzrbrcPH1oFLH91CTNLZrhv2doqvX2qNv7d052giJBYLiyNJidrRxcUU7jpC3vKtrP/zb37asZ55ObHmhERoaCgLlu5l8+4cNu7K4sjxfPO+fbs48/J9wY1VdGtV6+4b3wBfKKXaYRpTQgN9gceAr0u3GQbsufgyiuYqM7uYxV/tYMd7r7Py2FLWF6RUul1rdz9GDxrCxGumMXLyBNzc3Bq4pEIIIZozGzs7rn//FSZffhl3TrqCX3NPApBWnM+L/w3knjdTSc80DbRcVKwpKpI/JoQQoiEVJ6WRv3Ib+Su3cXTpWubEH+C33FiiirMrbOvgP4l7XzlR6XHGDpS/MxpSdZMSDwOJwAOYBrcESABeB94off0PsKhOSyesWkZWMX+/v4p1H7/C4rgNlQaLXm3Cue3O2xg//QpCQkIaoZRCCCHEWQWb9lJw55uklhSYl10xYjzPf5lhTkgA3DHdlyB/Q2MUUQghmg1dUEjB1gPkr9hK/optpO8+xMK8BObknmRdQUqlzYa8Hbzx6ng1LcJvLbfc1ha6tnNiZD9XhvV2bZgTEEA1u2+U20EpNwCt9flHGmx48nNEHWmIpmJrlscy79a7+SZmIcllKnYAtkoxdcQYHvq/5xgwYEC9lkOIBiBNhoWoBw3drFnn5JH18R+kffArn6cc5MXMA+ZyTL55MTm6lXnbe6/xY8qIxurNKqogsVhYHOm+UZHOyaNg20EKNu6hYNNeTm3ezebMBDYWpLKxIJVdhacpruQSMNg60qL1WHw6TMPDvydKmQYj9nC1pV8XZ/p1c6FXJyecHWUMiXpU6+4bZk0wGSEsSH5BCbPv+4Zfv36StXkJ5da52hu55epruf/FZ6VVhBBCiCZBl5SQN3cVGS99w5qYAzyVsZdDRVnm9T6tRpsTEkrBgze0YMLgxhrzWwghrEvJ6SwKtuw3tVLbuIeEbXvYlHOKjQWpbChIZV9RRpVZOIUNnkEDCGg3Fd9Wo7G1cwSgfSsj/bq60K+rM+1bOWBj05xylk1TlUkJpdQuYJjWOk0ptZvzZF211l3ro3DCuhzak8grU67j5+hl5WbS8Hd04/GHHuKWxx7E5f/bu+/wqKr0gePfM72lJySEDqFKR1YUULFX7GVdG3YXXcta1r6r66pr7/5sa+8VCxasgArSOwKhJYGE9DJ95vz+mDAQSCBAkplJ3s/z5Enm3jN33jsk7z28c885LlcMIxRCCCG28c9dQfVdL7Dx94XcXb2cT7xFDfY7U/vSb8ztACQ5DFxzXjaHjpZxyEIIsbdCpZXRAoT/t6UULl7Ob74yfvOX8au/vEFRuCmu9AFk9z6enLyJ2JzZOO0Ghg9wMGaokz8NcZGRKpPjx5td/Yt8CGydgvSDNohFtGPfvTWHqycdzXJ/eXSbAi498QwefONFmbRSCCFEzOlAkMCi1fh/Wcy6b6bz/fSfmeEv5UvvZur0tvkijCYHvUZeRbf9zsNsNnPShDTOPSFDlo4TQohm0FoTLi4nmF9IKL+IwOoCipevYuWyZazcuJ41wTpWB2tZFaxlfci964MpA0kZA0nLGU1qzmhSc0aRkZHB0L52hvSzM7S/g95drRjlboi4tsdzSsSxdnMisdYa49fO6TGBtzf8GH3cPyWH/73/JgceeViLvo4QcagjXQUlD4s20xLXKh0MRYsQW376nR9nTOfn6iJm+EpZ3cjkywDZvY8n7083YXNmc9BwF5ednkXXHJnQMgFILhYJJ9HnlAhX1RJcW0Qov5DgmiI8qzaweuky/sjPZ1VdOWuCtawO1pIfrKNCB5p1TKWMJGcNqS9AjCY1eySdslIY0tfB0P52hvVz0L2zRYZkxKeWmVNCKbU/0Af4XGtdp5RyAj6t6xd7FaIRWmuWlCyPPr78sFN48qt3MZtl7XYhhBBtRwdDBJaswT9zEdUz5jPz5+lMrypihr+MhYHK7QYW7syZmke/A28nPXcMfbpZueLMLEYMdLZZ7EIIEY+0109w/aZo4SGYX0jJitWsXLmSVWXFrA7WRu982BByE9zDmpnBYCG50zDSOkeKECmdhpPTKYlh/RwM7WdnaD8HXbLNKCVFiETWrDsllFLZwBRgNJHqa1+tdb5S6v8Ar9b6mtYNs1kSt4wYZ1q6KltQUE6vbpnRJLR5VT7Zeb1a7PhCxLmOdJWUPCzazJ5cq3wzFlL3fx/jn7WUqaVrebluHXP8FQ3mN9qR0WAmI2soSd0OJrv7QYw7aDQj90ti5EAHed2t0gFOPB3pH0xycTsRj3dKaH+Auhen4Ju+gFB+EaGCEgqCbp6rzWdxoIrVwdpm3/WwPbPRSnJSdyxpedhS++BM7U1Obh8GDRpAr27J9OhspVtnCz06W8hKlw82E9Q+3ynxKLAZyAA2bLf9feDJvY9LdARPXH9rtCDRz5IqBQkhhBBtJlxZS8WF96DdXtYG67i0Yl6jy8WBIj19AEldx5HR5SD+dMCBjB6azsiBTgb1sWExG9o8diGEiCehsioqL/kP/llLo9umeIq4uWoxVc28cd5lz8Ke2htrah6O1N64UnvRq3c/BvTrTvfO1vovC91yLCQ5ZZ6ejqK5RYnDgcPrV+LYfvsaQNZuFE365r2PePj956OPx/XcP4bRCCGE6Gi8n89Au70AvFi3tkFBIimpOyldx5GWeyBDho1jzKjOjBzoYFh/By6HdIaFEGKrwPJ1VFxwN6GCEgDqwkFur17Ku56CndqajFZcyd2xpvbFkdoLR0pv0jJ7069/P/r0SKV7joXu9Xc+dOlklqKvaHZRwg74G9meBXhbLhzRnqzLz+fP551LuL4DONyaydWvvBzjqIQQQnQkng9/AKAqHOC94Obo9mFHPktm9wn8abCT687PltuBhRCiCd6vZ1F51UPoOg8AiwLVTFarWOPZllNtri70PeAfJGfuh9WZg9FgYNxIF0eNTaFXFytZaSaZfFI0qblFiZ+BC4Fb6x9rpZQRuBn4rhXiEgnu8w8/4pLzLqDcH0lenQxWjj79SYaN6RrjyIQQQnQUoS0V0duMp3iKqPNHVjq32LPI6HYIx4xL4dpzszGZpKMshBCNCRWXU3Hlf8EbyZ8eu5kLfcvZXF4abZPd5wQGHHQXJksSNovimHEpnHpkGrlZsjKRaJ7mFiVuAn5SSo0GrMDDwH5ACjC2lWITCai2tpbrL7uSF95+I7rNhOLcwZdz6BXHysRgQggh2k5Yg9kEgYbzvfs9Wyhd+k+uee4NKUgIIcSuhMPRggRA8plHUvbwF9HHXQedR/8DbyMjxchJh6dxwsGpJLtk+JvYM80awKO1XgYMAX4BvgFsRCa5HKG1XtN64YlE8ssvvzCs/8AGBYksg5WrB04i5YobOeqg5BhGJ4QQoqMxZqeT8t+rwGjgbEc3jrVmR/ctmvUeZ5xxJl6vjEIVQoimGDtn4rzspOjjwKtfcvkJp0Ufl274jlDQx3N39eSc4zKkICH2SrOWBE0Q7eZEYm1Plx9au3Ytt990M2998H6D7cfaOrPfgXcSPvY47ru2q0xiIzqqjvQxrORh0Wb25FrlnfY7FZfdT9Dj4aaqxbyz3cRs48eP5+OPPyYjI6O1QhXxQXKxSDjxsiSoDoYoP+cO/DMWAVDlMDG2bDrlleUA7Df2JpbMeCCWIYrE0GQe3mVRQinVrJU1tNYbdt+q1cX+L7adaG4CLCkp4Z677+b/nvs/AqFtywAlKRO3ZIxmzcH/xDCkH4/c1E1mMRcdmXSEhWgFe9pZ989dQfn5dxMur+KemhU8V5cf3de3b1++/PJL8vLyWiNUER8kF4uEEy9FCYBwWRWlx14XXX3j5T42bp/xIQDJ6b2pLF0tw7TF7ux1USLMrhObArTWOh7+xxkff7HtwO4SYE1NDY888ggPPfBfaj3uBvuOsmZzdu+T+XzUxXQZ1Im7ruxCRmpzpy4Rol3qSFdoycOizexNZz24uoAt5/4LNmzi2do13FOzIrovIyODTz/9lLFjZaqsdkpysUg48VSUAPD9vIDys28HwK3DDCj9kWAwMqn93LlzGTlyZCzDE/Fvr4sSo3Y4yE/AOUCDBWm11nP3McCWED9/sQluVwlw06ZNjBs3jvz8/AbbR5vTuKLTGJaMvIwl2QM55/gMzjsxA6OxI/UBhGhUR/ojkDws2szedtbDFTVsOPderPOX8IVnE1dXLsBLGACr1crixYvp27dvS4crYk9ysUg48VaUACg78zb8Mxbi0SGOq17ESncRANdf/3cefvihGEcn4lyTeXiXH2HvWGyov3NisdY6v4mniHYsEAhw1llnNShI9De5uCF1CFWDz+PdXgeTnmnjoUs6M7SfI4aRCiGEEI1TqS4WX/t3grc+wfGFkR7SJZXzAPD5fEyfPl2KEkIIsQPtD7DxP+8Q/GUR79Wt5/HaVWwOb1uVo6CgYBfPFmLX5L560Ww333wz06dPByLLttyfMoSzxx/DtSlnUGVLxmpRPHdnT1Jk1l0hhBBxyOML8/jrxUz7rZobfXX4dZhntptbIicnh4kTJ8YwQiGEiC+BoOb3DxZjufcpZmycxSO1q9gY8jRok9mpK3feeUeMIhTtgRQlRLO89tprPProo9HHNyf154IBo/ll0lVUfRlJTCMHOqQgIYQQIi7VukP8/cGNrNnoY2DZaoaW/sF/av5gXqASAKPRyHvvvUdmZmZsAxVCiDigtebX7woouPctBq74gfPKfmNhoKpBm7T0bG6//TYm//UyrFZrjCIV7cHeFCXia2CTaHXvv/8+kyZNij4+2prN5IxBpL10G9O/3NbuwGGuGEQnhBBC7FoopLnn/4pYs9FHsq+GKxa8CcAn3qJomwceeIDx48fHKkQhhIgbm/Ir+P2mtxg66zu6Br38pXxOg4JEamo6t976DyZPnozDIUO2xb7bZVFCKTVlh0024AWlVIMlF7TWcq9jOzVlyhTOOeccwuHIJGADTEk8njqMtPuuxNuzB4tXrY62HSNFCSGEEHHo2XdLmLvUjSEcYvKCN0j3VQNQRyja5vzzz49VeEIIERf8tV5m3fo+OZ9+xuiAG601N1QtYYa/LNrmjjvu4IYbbiA5OTmGkYr2xrCb/WU7fL0BbGxke6tQSt2qlNJKqada6zVE02bOnMkZZ5xBMBgEIM/o5N30A+h0wiHYzzqC2UvqqK9VMKCXjfQUGQ0kRHsjeVgksuKyAA+/uplPvq8E4PQ/vmJQWaSY7kfjN28bcmixWGIRohDNIrlYtJZQWLN4lZuP7/ia1cMvIe+Dd3H665jtL+eyynm87dkYbXv33Xdz9913S0FCtLjdrb4xaVf7W5NSagxwKbAoVjF0dDfddBN+vx+AnkYH72WMIbtzDikPTEYpxbrCbTPu7pdnj1WYQohWInlYJKpNW/y8PbWcb2ZWEay/GWJk8RJOzP8egLKQj8udG6nzRuZEMhgM2Gy2WIUrxC5JLhYtLRDULFzpZsa8Ghb+uomJsz5kzKYFuHWINz2F/K9uPcuC1Q2ec95553H77bfHKGLR3sXlR9tKqRTgTeBi4M4Yh9MhzZw5k19++QUAszLwdvoB5BhtuK7/M4aMFAC6d972qdLqDd6YxCmEaB2Sh0UiKirx89aX5Xz7axWhbSMz6FRXyl8Xvw3A8kA1F3oWsbFk2/joyZMnyyRtIi5JLhYtxesLM3dZHdPn1fLbwlpq60KMLZrLLcs+pcyzhX+51/OOeyNVOrjTc0877TReeOEFlFIxiFx0BHFZlACeBz7QWn+vlJIEHAO33XZb9OdTbbn0MDnAaMB2/Njo9pEDndGfl63x4vGFsVt3NyJICJEgJA+LhPLlz5U89kZxdFjhVoPzrFz/xVuY/V6WBao5qfxX6sKRTrdSigceeIAbbrghBhEL0SySi8U+W7DCzT+fKaTWvS1BTlryAYdt/I0na1dzf83KnVYysNvtnHvuuUyePJlhw4a1bcCiw4m7ooRS6lIgDzgv1rF0RHPmzAHgp59+im670tkbAMuBQzDW3yUBsHGzH6VA68htYJu2BOjdVT5pEiLRSR4WieizHysbFCT2y7Mz6eRM9jOWUfrEegA+8RRFCxIA48aN44orrpBP/0RcklwsWsrXM6saFCQM4RCHbfwNgJfr1jUoSOTm5nLDDTdw4YUXkpaW1saRio4qrj7WVkr1B/4D/EVr7W9G+8uUUnOUUnOef/751g+wHQuHwzz44IMceOCBDbZf6exNP3MSymEj6ZZtM5MXFvv51zOF6Pos1q+HlR65MkmYEIlO8rBIVMeMT2nwOBTW9OlmxdQrF+sRowE41d6FVGWOtpk+fTpDBg9m2rRpbRqrELsjuVi0pCMOTMawXe01JdWCOuc4AC5w9GjQtry4BHtZLampqW0YoejolNY73qwTO0qpC4H/wXZrdIER0EAYcGqtfY08lfo2Yi+Ew2HOOOMMPvroo+g2lzJxf8pgTrV3wTSoJ2nP/QNTXlcAgkHNZf9ax4ZNkWtkRoqRp27rQVa6udHjC9HBJdRHsJKHRaJQSrFjH+bdr8p54YMt0cf9etp44h/dMRrBN+13ah54ncLFy7m5agnf+IobPPfBBx+UYRztm+RikXAay3N766c5Ndz7fFH0jrLcLDPPnOTD/+z7vPnpR9xcvQSP3vbr9o9xx3Pf9M9b5LWFqNdkHo63okQq0HWHzf8DVhGpFi/VTQccPyeSYF555RUmTWq40MqvWRPoYXLgOP9Yku+6BGXfNixj7rI6bn6kAACLWfHIjd0Y0FtW3xCiCYnWEU5F8rBIAE111j/9oYIn3yyJPn7+rh707hZZWUOHw3i/+IXq/77OB0tmc0fVUip0AIA+ffqwevXqtglexILkYpFwWrIoAfDz3BrufrYo+vihG7oxfICDwJJ85t79FH/56HnyQ3UA5BrtFPhqUEZjU4cTYk81mYfjaviG1rpSa71k+y+gDiivfyxJtoVVVlZy0003RR+f7eoOQA+Tg5QHryLl/skNChIQmUtiqwl/SpKChBDtiORhkeiOOjAlepuyUpCTtW1ooTIYsJ84jk4/PsOfTz6N19JHR/dZLDIEUcQPycWiNYRC235tHHYDed0jfXzz4N4M/r87CZi2/Z/xyL6DpSAh2kxcFSVE28rPz+ess85iy5bIra659iT+7RwIgHlEP+znHL3Tc8JhzeI/PNHHXbOlEyeEECJ+rFznJVzf7zabFO9/Xc6SVW6CwW2dcWU0QihEQG+b+K1w7XquPe8ipnzyCVVVVTseVgghEt47U8ujPzvtBj74poKZ82soKQ/wn3MvY6OvNrrfFwzy8hU3sujDqYRq3bEIV3QgcTV8Yx+1mxNpbVVVVfz73//miSeewO/fdtfD/6WO5ER7Z3I3fYH3x3lYDxnR4HnVtSH++/ImfltUF9327791YcxQV5vFLkQCSqhbhveR5GHRZpq6rXnm/Bruerpop+0Om4Fh/e2MHORk5EAHrpv+w8yvvuWksl93amtQiuHdenPooYdy+BknM/6QQ0hKSmqV8xBtRnKxSDgtOXzDHwhz/F9X0dThyt47igU1Gxrd51RGhiRlMbxnHqOGj2D0YYcw8NgJWDqlt0hsosNIjDkl9lG7OZHWEgwGeeGFF7jzzjspLS1tsO8iR0/uSR6EUorcTV8QDocbLJG2cp2Xu58tpLhs21JqowY5uO/arhgMHek6L8Qe60h/IJKHRZtpqrNe6w7xwMub+XVBbSPP2uYA/zr+Mvt17tzwI594igjt4tfXqBQje+Qx4dBDOfysUzn40EOx2Wz7fA6iTUkuFgmnpeeUePPzMt6ZWobHt/MxHcve4/ff72dzsHl3RTiUkSGODIZ368XIocM5YMLBDDruMMzdc1osXtHuSFGiI/N6vbzyyis8+OCD5OfnN9g3ypzKP5MHMcqShmlAD5L/cyW2A4dEE2BpZZA3Py/jy+mVhLab//nMo9O46JQsTKaOdI0XYq90pD8SycOizeyus15ZE2T+cjfzlrmZu6yOkvLgzo20JqduC71KlmDdMoMNRfOZVVPE4kDVLn+ZnWYLR44Zx2kXnc/xJ00kLS1t309ItDbJxSLhtHRRAiLLJRcW+1m1wceqdV5WbfCyaoMPtyeM1hpLZT7mgpn4ihdQVvEHa+sKKQ95dn9goIvRzhFZvTjm0AkceeE5pE8YjbLI6nwiSooSHVFVVRXPPvssjz32GMXFDZc+62K0c1vSAE6ydcaQ5MB1w19wTjoBZTahlKKyOsA7X5Xz6feV+AMNJ8W5+aIcxo6Q21iFaCbpCAvRCvaks661prAkwNxldcxb5mbBCjd1nnBjDenkLmNg5RLMJT+zesN8fq0sYFmwpsljmwwGDh4yklPOP4eTzzyDrl13XDBBxAnJxSLhtEZRojHhsGbTlgB/rPeyan2kSLF6vZcad6RQQfVGTAW/ECieR0X5SjbUbqQ8tOs7KiwYGGPP5Kghozj+jNMY/JeTMHXObPVzEXFNihIdSXFxMY8++ijPPvss1dXVDfalKTNXuHpzibMXdqMZ+6mHkHTrhRhzMgCo84RwOUycOPkP3N6GHbah/ezccGEOuZ1kcksh9oB0hIVoBfvSWQ+FNCvXeZm33M3cpXUsXeMh3EiNAqCTp5z9g0sJbfiRZavm8HNlQXTJvMaM7N2PU87/C5MuuZguXbrsVXyiVUguFgmnrYoSjdFas7k0wKr1PlZt8PLHei/L87246wu6/tpNGAt/JbRpDpXly1lTtQZ32N/k8bob7RzROY/Tjj2BI6+/DPOAnm10JiKOSFGiI9Ba89prr3HNNdfsNHN4Z4ONK1y9OcfeDafZgv20w3D97QxMvSMdpnBY880v1bz00RY+eLQvh1+8Ivrcfj2sTDoli/33czSYZ0II0Swd6Y+mw+dh0XZasrNe6w4xb5mbWYtrmb24jorqUKPtrCbN86cHKJoyhU8+/YSpm1YzP1DZaNusrCxWrFhBerpMBBcnJBeLhBPLokRjQmFN/kYfC1e6WbzKw6I/3NTURYoU4XCAuk2/E179GZuLfqXAvbnJ4zyUNoyrv3kby/4D2yp0ER+kKNHeFRUVcfnll/P555832J5ndDLZ1YdT7F2wWCw4zjoC51WnY9puEpqlqz08/XYxf6z3AfDdSwM4/OIVdO9sYdLJmYwb6ZJihBB7ryP98XToPCzaVmt11sNhzZqNPmYtrmP24lqW53sbzFZ/6elZnHVMOlprgivWs+atz/j0/Q/4cu0yfvGXEdzuz2DBggUMGzasxWMUe0VysUg48VaU2FE4rFm/yc+ilW4W/REpUmwt6nprNxFY8wVrljxLmbfh3WVXOnvz0Ksv4jhtQizCFrEjRYn2SmvN66+/zrXXXktFRUV0ew+jgzuTB3K0NRuDUtjPPpKkv5+DsUtWtE1xWYCXPtrC97MajpX97qUBfD2jksMPTMYoK2sIsa860h9Rh8zDIjbaqrNeVRPkna/Kef/ryDV2zFAn//7bzvNGlJ11O4u/n86E0p8BsNlsVFZWYrVaWz1G0SySi0XCifeixPYCQc29/1fEjPmRlY/sm+ezccaNLKwqaNBunDWTN66+le73/Q1lMsYiVBE7TeZhQ1tGIVrWjBkzGDNmDBdccEGDgsQkRw++yxzPsbYczN1zSH/336Q+ck20ILG5NMAjr23m/FvzGxQkLGbFuSdE5pY4amyKFCSEEEJ0eClJJo4/ODX6ePbiOh57fTMV1dtW8whX1eKfuYhVwYbLkD700EM7DacUQoj26NPvK6IFCYCl065oUJDoZLDy0rHn8/3KRfR48DopSIgGpCiRgFavXs1pp53G+PHjmT17dnR7d6OdD9LHcG/KYBxGM46LTyTz+6ewjh8OQNEWPw+/spkLbsvny5+rGizxefD+Sbx8Ty8uPFlmxRVCCCG216WTmS7ZkWXtwho+/6mKC25by3tfl+MPRMZTK4eVdMO2iaC9Xi+33347PXv25K677qK8vDwmsQshRFsIb39Dh9aEww3v8KgyapYOzKTKKUuEip3J8I0EsmXLFu69916eeeYZAoFAdLsVA5c4e3GtKw+nwYR5aB7J91yOZXRk8pj8jV4+/LaCb3+r3ml28aH97Fx4ciZD+zmi2xLpVjEhEkBHuuVIEodoM219rSos9vPEm8XMXdZwGbzcLDN/Pj6DcaF1uK9/lA/yF/JAzUo2hjwN2rlcLi6//HIuv/xy+vbt22ZxiyjJxSLhJFKfPBDUPPzKZqb9Fln5L2PDTIqm38IMb0mDdklJSfztb3/j0ksvpUePHrEIVcSOzCmRyKqqqnj44Yd59NFHqa1teGvoybZcbknqTzeTA0OnNJL+cT72Mw8njOK3hbV8NK2ChSs9Ox1zaD8750/MZPgAx077EikBCpEApCMsRCuIxbVKa82sxXU8924JBcWBBvtcDgPHjTBz/OyPCX3yLR95Cnmydk2jy4dOmDCByy67jFNOOUXmnGg7kotFwkm0PrnWmnemlvPyx6VoDXkV6xg7/d88UrmYBYGGQ9mUUhx11FFccsklTJw4EYvF0sRRRTsiRYlE5Ha7eeqpp7j//vsbzBkBMNqcxl3JAxlpSQOrGedlJ+O6+gzqlJWvZlTx6Q+VbC4N7HTM4QMcnHdiBsP671yM2CrREqAQcU46wkK0glheq4JBzZQfK3ltSim17vBO+0+25DNx+tsYSsuY4i3iidrVrNxhvgmAjIwMLrzwQi699FL69+/fFqF3ZJKLRcJJ1D75zPk13PfiJrw+TV7FOm6c/X/8VLeB+2tW7jT3DkCnTp244IILuOSSS+jXr18MIhZtRIoSicTv9/Piiy9yzz33sHlzwzV++5tc3JTUn2Os2SiDAfvpE3Dd8BcKjal88n0l3/5Shdff8K0wGGD8yCROOTyVwX2bLkZslagJUIg4JR1hIVpBPFyrqmtDTJ1RxRc/VVK0peEHAbaAlzMKf+SIVT+C38/3vhLecG9kmq+YncsYcPTRR/Pf//6XoUOHtknsHZDkYpFw4iHP7a3isgDvTC3nqxlVZFSWcOWCN+hetYGvvcW86dnIT74tjf6iTpw4kRdeeIFOnTq1ecyi1UlRIhGEQiHefPNN7rrrLtatW9dgXw+jgxuT+nGSLRejUliPHoPrxnOZF8zk4+8qmLvUvdPxkl1Gjj84hRMPTaVTevMnlUnkBChEHJKOsBCtIJ6uVeGwZt5yN5/9WMmvC2obTPiW5qnk/IJvGLV6NkprNoW8vOveyFuBIgp8DT8xNBgMXHzxxdxzzz1kZ2e38Vm0e5KLRcKJpzy3t0org3zwdTlTfyjluKVfc8Ka7zGg2Rh081a4hA/M5RRu3tTgObm5ubzzzjuMHz8+RlGLViJFiXimtebjjz/m9ttvZ/ny5Q325RisXOfqy9mObpiVAcuBgzFdfx7fe3L55PsKCkt2HqLRq4uFU49I47ADkrFa9nyBlfaQAIWII9IRFqIVxOu1akt5gC+nV/Hlz5WUVW1b5qpbdRGXbPiS3hsi1/mQ1vwcKOOdnCBfLJ9PeLuZqF0uF7feeivXXXcdNputzc+hnZJcLBJOvOa5vVFZE+SjaRUsensOV8x+lRR/pCjr69GFxdcfzUtvvsann34abW80GvnPf/7DDTfcgMEgC0a2E1KUiFdz587lmmuuYebMmQ22pykzV7vyuMDZA7syYhrcB/+V5/CJpwdf/1KD29vw5k+DggOHuzjl8DSG9bej1N5fe9tTAhQiDkhHWIhWEO/XKn8gzMffVfLWF2XUeeqv2VozvvB3LljxKVa/N9p2VY6Ne9jAtPm/NzhGXl4eL7zwAoceemgbRt5uSS4WCSfe89zeWLbGw4t3zuDan57CHI4Ubiu69CD7qWv4vSKfc889l7Kysmj7M844g9dee00KtO2DFCXiTXFxMbfddhsvv/xyg2TjUiYud/biMmcvkgxmjD07s+UvZ/JWsD+zl3jY8Z/LaTdw3PgUJk5IpXNWy8xa2x4ToBAxJB1hIVpBolyrqmpDvP1FGZ/+UEkgGIk3013OZYveYWD5mgZtf85W/KtiKcvXr22w/ZJLLuHBBx8kNTW1rcJujyQXi4STKHluT60r9PHe3z7mvF/fiG4LKQMbjj6WzOuPYNLlF/LLL79E9x1yyCF88sknkgMTnxQl4oXWmscff5y77rqL6urq6HYziknOnlztyiPDYMGQnc76iSfzfHAo64pDOx2ne46Fkw9P5cgDU7DbWvaWpvaaAIWIEekIC9EKEu1atWmLn9emlPHD7GqCIVA6zFHrZnDKqm9wBrct3R3UYd7K9HHfut+pqq2Jbu/cuTPPPfccEydOjEX47YHkYpFwEi3P7YnisgDfXP02B//8EZZwMLq9xJXJlmsv4ZNl7/D0009Htw8ZMoSpU6fSpUuXWIQrWoYUJeKB1pqrr766wR8YwBHWTtyVPJA+JhcqxUl40qk8FhjFvLU7FyMOGOrklMPTGDXIsU9DNHalPSdAIWJAOsJCtIJEvVaVVQb57MdKPv+pksqaEI6Am+Pyf+SYddOxhvzRdptVgH/m1jFlTsPhnY888gjXXXddW4fdHkguFgknUfNcc2mtmfvlKvx3PEOvzauj2wMGI9+ffhmFnZZw+23/iG7v06cPP/74I127do1FuGLfSVEi1rTWXH/99Tz22GPRbX2MTv6VPIjDbJ3AZsV5yURmDD2CZ6bW4fVtOx2HzcDRY5M56bA0uma3zBCNXWnvCVCINiYdYSFaQaJfq/yBMD/MruGjaRWs2egj2VfDxDXfcdiGX6LjrAG+G9+dG3/+uMES4TfddBP3339/q3040U51pDcrcf8wRAOJnueaKxgMMf/eKWS8/Bb2QOTOsaAy8OERkzAeUMn111xGMBi5myIvL48ff/xR7phITFKUiLXbb7+de++9N/p4oq0zT6QOx6IMmAb3wfzMrdz/mZ852y3taVBw1rHpnH1sOk67sc1i7SgJUIg2Ih1hIVpBe7lWaR1ZTvSJN4opLAmQ5S7jmrmv0KOmKNrGfehQLtzwMzO2mxR78uTJPPXUU7EIOVFJLhYJp73kueZy52+m8KRbcZWVAJF5Jp4fexHdjg0z+fKzCQQiqw727duXWbNmkZaWFstwxZ6TokQsTZkyhZNOOin6+HhbDs+mjsCkDFgPHYnn33/ntpfKKCjetrxn984WbroohwG97G0eb0dLgEK0MukIC9EK2tu1yusL879PSvloWgWWgI8rF77JqOKl0f2mv/+ZS35+jylTpkS3LV26lEGDBsUi3EQkuVgknPaW55ojtKmUDSf8A9umyN1hxY4M3v7r3RzUZwGnn3569I6Jl156iYsuuiiWoYo912QelkVfW1lBQQGTJk2KPj7MmsUz9QUJ+2kTKLz9Bq56vKRBQeLMo9N47s4eMSlICCGEEKLt2awGrjyrE4/e1I3UbCePj7yQr3uOj+4PPvk+79z7MEcccUR02zvvvBOLUIUQotUYO2fSfeqDhF1OALLdZXh+Xszog47lrLPOinF0orVIUaIV1dbWcvrpp1NeXg5AZ6ONJ1OHY1YGLIeO5MeJF3Dj45uoqYusX24xK+64IpfLzuiExSz/NEIIIURHM7ivg/uu7YrDYeKtASeyJqVbZIc/SN0tz3LFFVdE27777rsxilIIIVqPsVMarjMPiz4eVziHr2ZUsWnTpug2l8sVi9BEK5H/+bYSr9fLySefzKxZs4DIG/1M6gjSDBYM/XvwwgEX8NS7ZQTr57JKTTLy8I3dOGT/pNgFLYQQQoiY697Zyp1X5oLJyPPD/ozfYAIgMHclh/UcEG23Zs2aDndrtxCiYzD17x79Odlfy/tvPsr3338f3ZaXlxeLsEQrkaJEKwiFQvz5z3/mu+++i267O3k/DrCko3t24d5hFzFt8bb1ePt2t/LUbT0Y2FuGawghhBACRg2KLAFe5MpmXvZ+0e2Vn/4Y/TkpKUlW4BBCtDuBPzZQ88Y3aK35I1DDg5VL+OrD+6L7zznnHEaOHBnDCEVLM8U6gPbopZde4pNPPok+vtnVj4ucPWFwHnf2n8S6Wlt03wmHpPDXs2W4hhBCCCEaGtjbRpa7jE7uyDDQWf5y/vHYXdH9ycnJsQpNCCFaVHBjMXUf/UT5Oz+wctVivvRu5kvvZtaE6hq0mzBhAi+//HKMohStRYoSLaympoY777wz+vhiR0/+5srD8KfB3NX/AtaVRYoPNoviugtyOPwA6VAIIYQQoqEvfqpg3n2fce/ST/EFarmxejlvejY2aHPCCSfEKDohhNh3oZIKPFN+puydH5i/YHa0EFEY8jTafsSIEXz88cdYrdY2jlS0NilKtLBHH32U4uJiADobbNySPABDWjJPDfsLqwsjBQmTEf51VRdGDXLGMlQhhBBCxJlwWPPaq2vIeOJFLt68mC+8m7mteilbwr5oG6fTyb///W+uuuqqGEYqhBB7Tnv9eL/+jZp3prHwu5/42F3AJ54iNoW9jba32Zwcd/yxnH7aqZx66qlSkGinpCjRwt57773ozzcl9cOhjBRdcgG/LrMAoBTccmmuFCSEEEII0YDXF+bNW77hoI9exevZwsVVS/jKV9ygzUknncSTTz5Jt27dYhSlEELsGa01gd+X437/O/744Cs+KVvDR55CVgZrG23vcCZz3PETOfec0znqqKOw22XevfZOihItqLS0lKVLlwJgQnGiPRdjr1yeKusHBAA4/cg0WWFDCCGEEA2UFNUy64KnOXbJz7zl2cg91cup1tsmxc7NzeWpp57ilFNOiWGUQgjRfOHKWtxvfkXZa1/wycr5vO3ZyCx/eaNtnc5Ujj3hVC6ZdCaHHXYYZrO5jaMVsSRFiRY0e/bs6M/DzCk4lJGKgw5kQ3GkIOGwGzj7uIxYhSeEEEKIODT3i5XoGx4io2w1Z1YtZqa/rMH+yy+/nAceeICUlJQYRSiEEM0XXFtE3YtTWPj6R7xRvpr33QVU6sBO7UwmK2PGn8A1V53PSSceK4WIDkyKEi3I59s23jPTEBnvVJDZHSoi2yaMTiLFZYxFaEIIIYSIM8Gg5ptbP6XvW6/wWvVKHqr5Ay/h6P68vDxefPFFDjnkkBhGKYQQzeP/fTlVT7/PB1M+4fW69fzayF0RCgM9B4zngvPP47rJZ5KcLHeQCylKtKjtxzt5dQiAzXXblvrskSsTswghhBAiYuotU9j/zRe5v3oFT9StiW43Go38/e9/55///KeMpRZCJATPZzOovPx+rqlcwPuewp32O5O7cuTxF3LnLZcyYkj3GEQo4pkUJVrQ9h0HX/0nHZuqt+3vmmNp65CEEEIIEYfCYY3+aQ5hrXnNvSG6ffjw4bz00kuMHDkyhtEJIcSe8Xz0IyUhLx95iqLblDLQo/+RTLr4Mv4+eSJOu/zXUzTOsPsmbUcpdYtS6nelVLVSaotS6jOl1OBYx9Vcjd0pUVilotu6Zss4KSFE/Ev0XCxEIlhX5CettoyVwZroWOusrCxmz54tBQkheVgklNDmMvyzl/Jy3TpCaACSMvfj82kryV82lTtvOFUKEmKX4qooARwKPAMcBBwGBIFpSqn0WAbVXNsXJWrCkRmzy92RooTBANkZUpQQQiSEQ0ngXCxEvCuv8PP7Zc+RUraKyZULotvHjh0rE72JrQ5F8rCIc+E6DzUPvsnvf/oLZ6+e1mAY2pHHTeK4w/JQSu3iCEJExFXJSmt99PaPlVLnAVXAWOCzmAS1B7p33zY+akPITVCHcQbcAGgNBvmbFEIkgETPxULEsy0lHuaeej99V83k1LLfWBOqA0ApxeTJk2McnYgXkodFPNNa43nnW6rve5Xn1s3ngZqVDSbpTc7szwP/uiiGEYpEE293SuwoiUiMFbEOpDlSUlLo0qULAAE060JuMkO1QKQo4fXpWIYnhBB7K6FysRDxbMaV/2NY/lyur1wYLUgYjUbeeOMNjjjiiBhHJ+KY5GERN+qe+oCqvz/Bc+vm86+a5dGChMLAuCMvZcmC38jrmRbjKEUiifeixOPAAuDXxnYqpS5TSs1RSs15/vnn2zSwpmRlZUV/dusQ4dC2qqFRVgMVQiSmJnNxPOZhIeLVhj/KGfr796wL1vGTvxQAg8HAu+++yznnnBPj6EScS7g+sWifvFN/oea+V8kP1vFAzcro9vROA3jzgx+Z/s3zdOuSGrsARUKKq+Eb21NKPQKMA8ZpXT9r5A601s8DWzNvXNyG4PV6oz/bMFBtcgKQlmzEYo73GpAQQjS0u1wcj3lYiHi08usVlN75IlvcRfynekV0+/HHH89pp50Ww8hEvEvUPrFoP8LVdXi//o2yD77nh+++Y6q7kK+9xdE7JLK77MfaVXOx260xjlQkqrgsSiilHgXOBiZorfNjHc+e2H6CqsKQB1vIB0SGb2itZbIXIUTCSORcLES8WDV9LQV3vcrmhV/xRO0qfvOXN9h/0UUy7lo0TfKwiJWw24vvm1lsfvdbvv72a6bWFfKdr4S6HepiShl5791XpCAh9kncFSWUUo8TSb6Haq1X7K59vDniiCNYvHgxAJ95N3Fy5R/Myx5MZU2I/AIffbrZYhyhEELsXqLnYiFiLX9OAfl3vM7G2R/zZM0q5gcqG+w3GAxcd911nHTSSbEJUMQ9ycOirWmPD+8Pc9nw1lS+/OpLptYW8LOvFN92k1huz5mUzQMPPsLBY/dv40hFexNXRQml1NPAecDJQIVSKqd+V63WujZmge2Bs846i0cffRSAr7zF3LZlKfQ7BZRizpI6KUoIIeJee8jFQsTK+sXFLLv9ddbNeJenqv9gWbC6wX6T0cQFF17AP/7xD/Ly8mIUpYh3kodFW9H+AL6f5rPqtSl8OvULvq7ZyK/+ckJNjAJKTe3KuMNP4dKLzuCEY8ZiMMjwdLHvlNbxM+xMKdVUMP/SWv9zN0+PixPRWtOzZ082bNgAwOtpo/nmyAcpSsph+AAHD93QLcYR7p5Sinj6vRAiwSXcmK19yMWSOESbibdr1eZiL3Ov/R/rv3mFR6uWsrp+ZY2trGYLl1x2KTfeeCM9evSIUZQdWkLl4vbQJxb7rjXzXGhTKZvu+x+vvvMOH1fmM3eHu7m2l5WZx/ijTuWKS8/iiENGyHB0sbea/MWJqzsltNYJ/xuulOLMM8/koYceAuAjTyFjt6ygKCmHJavc1NSFSHLKMhxCiPjVHnKxEG3F5w/z3tRS6u68jw9XvsU0X0mD/Xarjb9eNZm///3vdO7cOUZRikQjeVi0poUvTeH5G//BW5WrqdSBRtt0ydmPg489nSsvO5PxYwa1cYSio4mrOyX2UdycyIIFCxgxYgQAFgx8OOwSHtv/egCuOqcTJx8W3+v2xtunT0IkuI7UsZTEIdpMPFyrZi2q5ck3izG/ey3vb5jaYAI4l93J3667huuuu47MzMwYRinqSS4WCael89zUrxfy0KWXMH3jXAI7/JoYMNC963AOOeFMJl9+JqOH92qx1xWiXpN5WIoSrWTM/qOZNXcOAHekDGL2Ke8RNJro39PG07fH922b8dDRE6IdkY6wEK0g1teqtYU+rrh7Hc45z/PxvEca7Bs/ajSffvs1aWnx/SFEByO5WCSclsxzawvcDO3TmVp/9U77xg85gsdfepYRo2WeG9GqmszDMjNJK9BaE6p1Rx970YTqJ4EJBOU6IYQQQiQ6nz9MKATaX7PTvulzf+eArn2454wLKdlQEIPohBCiodo6H8GQv9F90xdP4+ADBnJwem9uOvQsFn08tY2jEx2dFCVawS+//MKclcuAyBvco9/JaBV5q086LDV2gQkhhBCiRQzoZeemSTmEDr6eE/qcglM1nKZrlbuCOz94leH9B+J2u5s4ihBCtI3B/VJ55LnP+FPuKCxq5/8C1uog0yvW8uBP7zHq1OP58q6HYxCl6KjiaqLL9qKoqCj6cxj4avm7ZPQ8irLkznw8rYL993OSnWGOXYBCCCGE2COhkGbTlgAr8t3M/WUpK77/jqJlMykuW8HquiLqdLDR523y1lJcUEivfn3bOGIhREcUCmu2lPtYPH8VS2bOZ/WceWxcs5zNJevZVLuZTf7qJpf73CqIZsnsORzXRjELIXNKtIJAIMDxY8bx7bzZ0W25RjujMoeT1P90qgccw4H7p3L4AcmMGuTEZIqvYY6xHqcrRDsTX3/grUsSh2gzrXGtCoc1ZZVBNhTUseLXxayeNZeNK5dSUpRPaWUhWzxbKPRX4dluQsumGIBByVlcet4F/O2pB1s0TrFXJBeLhLNjnguHNZs3lrN81jLWLFzGxtX5bN64ni0lmyir2kKZu5wyfw2lQTfhvXg9Kwa6W1MY22cQj3z6Bml5PVvsXIRAJrpse4FAgMsmnsYrX322076epiSG5vwJy8Bz0IPGc8joZA4fk8zA3ra4WPdXihJCtKjY/1G3HUkcos3s7bVKa01lTYgNq8tZOWMBq36fx8ZVyynetI6SykKKPWUUBKrx7WGXPstoY2ROTw4cOZKxxx7BAadPJCkrY4/jE61GcrGIa/5qN4Ur1vPHvBWsXbKKwrVr+feXzzGx22hKa8oo81RREqihItz4vBB7Itdop7szg+6ZXejdqy+DRg5l1BEH0m/8/pjsthY4GyEaJUWJWNBa8+B1N/PA009SHvQ22qa3OYX+2aMw552EY9QxjB2ZwtgRLob0dcTsDgopSgjRoqQjLEQr2PFaFQppKqqDbNhQRv785RQuW03x2rWUFRVQXrqJyqpSqtwVVPqqKQu6qQj79voXNsVgpo8zndF5Axl7yDjGnnYiPQ/aH4NBpuqKY5KLRZuoqfWw8Y8Civ7YQPH6ArZs3ERFcQmVpaVUVZZTXV1BrbuGGk8NtX43NUEP1SEfVWH/Xt3d0JQsg5Uci4seKdn06tKTvoMGMnzc/gw/4iBcvbvFxQehosORokQs+f1+vnrhNd5+/iU+WzKHunDj404zjTYGZQzF1ec4TMNO4cBRWRw0wsXo/ZzYbW3X0ZGihBAtqiNd9SVxiBbn84cprQywek0Ra5fmU7j8D0rWreOJj/7LcXkTqKreVmwoD7pxNzG3w55KM1jo5kynR3oOvbv3YNCQQQwaNZR+B40ms18vKUAkHsnFYo9sKatm/YbNFK3aSPHaAkoLNlOxeTMVpaXUbC0u1FVT462hzu+mJhApLrRUDmqKEUWWyU4nazJZzjSy0rLonJ1L15496Nm/FwNGDKT3iEFYszOk8CDijRQl4oW7tpZPH36Wt19/nW/zl+LVjddEncrMsJwDsI64Alv30Ywb6eLosSmMGODAYGjdBCNFCSFaVEfqEUjiEHtMa01ldYiiLQH+WF3MklnzWL9gHpvXLKekZC2l1UVs9pbhaYWOfobRRldXBj0yO9O7Z28GDh3IkDEj6Df2T2R06dzirydiSnKxAMDjDbN2YxnLF61i3cJlFPyxmuKN69hSUkhZZQlldeWU+qpwhwNtHptDmUg3O8i0p5KVlMG3BfP5+7GX0K13L3oP7sug0fvRY2hfTGaZMF8kJClKxKPasgqmPvkin77/AVNXLqQ85Gu0XR9HZ7r2OwOGnE9uTipHHZTCUWOTyc2ytEpcUpQQokVJR1h0eKGQZlNpgE1bAqwvrGHprPkUzJ9Dcf4ySkvyKa8pothbSmWo8aGOe8KCgSyTnQyriwxHCpmpmWRlZdO5axe65fWg93559Bw6gOx+vbFYWuc6KuKS5OJ2zucPs6UiyMbCSlbOWcTGJSvYlL+a0k0bKSvdRHl1CeWeCsoCNdS1YsHBiCLFYCbZaCXJ7CDJ4sBld5HkTCE5OZWUtAxSMzPJ6JxNVtfO5PTqSpd+3emS1wW7zdrgWNInF+2MFCXiXdDr4+cX3uDT19/ms4WzWOuv3alNssnOwBPfxZXeD4DJZ3filCPSWjwWSYBCtCjpCIsObXNpgOse2MCWiiBVJQtZ+dXF1AR2vsbtTpIy0cloJ9PqItOZyhcly7lx3Gl07to1WmzoPmQA6X17YrDIp4hiJ5KL27Gf59bw35c2kb/4PdbM/Bf+FrizyoqBdIOFFKOVJLONJIuTJFukuJCUnEJqWgZp9cWFzK6d6dyrC1369SC7Ty5GR8tMFil9ctHONJmHTW0ZhWiayWblsKsv5rCrL+ZRr5/vHnyG5//veT4rXBmdAbw66KFy/bRoUeKrmVWtUpQQQgghWsrsxbVsqYj8B2HL+mm7LEjYMNDT7KJHUia9s7vSL68Pg0cOZdCB+9Np+CCMndKiY6SVUvx3+gdtcg5CiPj2/W/VeP2a4j8+alZBwoaBXKOdLKuLTs50OqVk0jk7l9zu3enVvw99hw2k29A+mDtnouzW3R5PCLFvpCgRh5TVzNjjj6Fm1Xoq3ytjmq8kui+56/jozzmZ8kmQEEKI+NY1Z9sQiZw+J1K05FUCTSxp18PkIFWZCPu8lJRswltTzYbVa/j1q+9Jz0wjIzOD9E5ZpOZ0AqB8+lySOmdjTEtCpbhQJmObnJMQIr50yoj0iXMHX0D59/N32XZrQcKhjBAMUF1bTsBbS3nZJvJXL2HuTDsOmx2H3YHT6cDpcpGUlERSShJJaSmkpCfjSkvFmZ5KUloarsw0rGkpGFx2lNOOsltlgkkh9pAUJeJIIL+Qnx59kbc/fJ9Pi1dRtkOnLStrKMlZQ6KPh/S1t3WIQgghxB4Z2MuO2aQIBDWu9H7sd+TTLPz2SnQjK1GtDNbfReEH6nZ/7IyD98eEItlgJkWZSDZZSTLbSbE5SHa4yExLIT01lbT0NFIzMkjrlMV++w2i26D+mAf3kSKGEO1E/16R4RLZvY7BcPhTLPnxBsJNzFHjJUx+qBkJZg8YUTiUEYcyYldGHAYzdqMZm8mC3WzFbrbisNqw1xc7XE4nGRlJuJKScCYl4UpJwpmSwqhhw0jpkoN5WF+UrPAjOhCZUyIOhMqqmHPjf7nxreeZ7itttE2Gswt9jnqGpPT+AIzaz8Edl+ficrR8h0rGrwnRojrSxyWSOESjps6o4p0vy6ioDuH2hikrmM6aOY/iqSkg6K+hLX91DMB76WOYcOJxpP3vdvlEs+PoSP/QHS4XB4Oah17ZzPwVbry+MJsLFrJq1kPUVeYTCroJBT3QxIp38SRFmfgx6xC6Hz2e9Nfukj65aG9kost4pANBip5+h3/efTf/q1hNaIdTSDMn0bvbBMz7nYsjawjdO1s5fEwyhx2Q1Gorb4AUJYRoYdIRFmI7oZCm1h2ixh2mti5EZW2QkpIKNm+pYEtJOeVbSqnaXExtaQl15aV4q8tw11Tg81Tj9dbgC9TiDbop8JZiUUb8OrTHMZzn6M5/kgeTeusF2M88AsN2c1WIdqsj/QN3+FystSYQ1Hh9Go8vjMcboqrGQ0VFLZVVtVRU1lBdU0d1RTW1FRW4KyvxVFXhqanGV1uDr64Gn6cOn7cOv8+N3+/B7/fiD3rxB334wz58IT/ecABvOLBTH35ffJg+hgOtGXRa+DqmTunSJxftiRQl4k1oUynfn3UdF//yIRtDnuh2A4r9uhyMffD5uHLHYDYbOeGQyDKgfXu0zRg1KUoI0aKkIyxECwkGNbWeELV1Ybp1tjJrcS3l5XVs3lJOSXE5lQVFVJcUs2xRAXjL0d5KQr4qgv5qft38a4NjWTDQ3Winl8lJb2caed170G/AAPqNHkHPA0dhGdgLQ6orRmcqWoHkYtEiQmGN1xeOFjy8vjA1NV7KKyMFj8qKampKK6grr8BdUYm7uor8NaXUVVWj/XXoYB1Bfx3TN37b6PEzDRayDTayjVa+923hb7nD6dypE7ldcsnt2Z3cvn3IHdQPV+9uGLtkoWyytLFIGFKUiCf+WUt4/OhzuKlqcYPtXTMGk3vwvdEhGkP72bnm3Gx65LbtrL9SlBCiRUlHWIhW0NS1KhzWvPDBFt7/pgIArcOEg15mvnckAW/ZHr/OA8mDufzeO0i+5GTp/Cc2ycWizYXCmkBA88XPlTzzTjHhUAAdDhDwVfHLe4e36Gs9lTqc8/9zO65JJ0quEvFKlgSNF1prFlx8F7dULdlpX3I4hHHZO9R2G4ejy2gyUpOYt8yN2xumTzcrFrNMeCOEECIxhcMaf0DjC2h8/jBuT5DK6jqqqmqprnZTVVNHXa2b6uo66ioit1K7q2rw1dbgra3BW1eL31OHz+Mm4HMDMKbL/vj9HgJBH/6gn0DIhy8UwB/24wsH8engXg3v2N7N1UvIvPO/nGW347z4xJZ4K4QQLSgc1gRD9fnFH8Lt9lHn9uL21H93+/B6fdTVf/d4vHhr3Xjr6vDXefC53fg9HvxuD36vh4DXR8DrIeD3EfD5CAZ8BPw+ggE/gYCfUNBPIOgnGAwSCvkJhAIEQ0GC4YZfAR0iGA4R0CECOkyQ1p3T4qrKBXS942GOcNhxXnBcq76WEC1NihJtTClFamYGFmXAs0NHaVnFcqhYDivfBmDhp3n0Ofi+6Iobowc7uWlSDmkp8s8mhBAifm0pD/D4G8X8tigyw3154S/M/+qiFn+dWUVzW/yYjUk3WDCkJbXJawkhdpZf4OPOpwrZXBqgesti5n91MUF/dazDijupyiy5SiQk+d9tDOR9+gi/PTGctz76gFlrVrCgtpRKHdipXXnlakyLXmTI4Y8D8PuSOr6bVc3pR6W3dchCCCFEs309sypakABYM/fxGEYDdmXEhgG7MmI3mLAZzVgNZmwmM1aTBYvJitloxWyy0inDicFmYFNdNUGPl2sHHMS42yZjGTM4pucgREf24TflbC6N9JXXLXo+YQsSZhQWZcCCIfJdGTEpAxaDEZMyYjGYMBmMWIwmTEYTP1Ws5djs/TCbTJiMZgwGMwaDhTBGjPYwYZOPipoqLEHNbQPHs/+/rsb8p/1ifZpC7DGZUyLGtNYEC0qYd9EdjJn2QqNtDu1+FObk7pDWh877DWXCUcPolptEdrqJnEwz6SkmDIaWGyopc0oI0aJkHLNIaOFwZBZ7n1/j9YeoqXZTVeumttZDTY2bmjoPddV1uKuqcVfV4KmpY93acvLXVkLQDUEPs1e8jS/obvHYehod3JU8KFJsUAbSrv4zyV2ycaalRL7SU7GnpWBw2jE4bGBvmwmjRVzqSP/wcZWLI/MqhPF4A7g9PjweH263D4/Xj9vjw+v14/H68Lh9eN0efLVufG4vfo8Hr9tDwOPljzVVbCquhaCf5Wu/pKx6bUzP6Upnb0Zb0rDWFxfMyoD1sDHYRg/B7nJgd9lxJruwuRzYk51YHQ4syS4MVgvKakZZzGA1owy7HpotfXLRzsicEvFKKYW5WzZ9unXDggF/I+PNftzwzbYH0+G15yDT6CBNGSkxGOg15AIGHHIJyujAZjXw5+PSOeyAZIwtWKgQQgiRuCprgjz//ha++aWa2vKVLPnhBuoqV+3yOYrIilAGwIgihCYQX//XwalMHG3Ljj7OOPZwLH8aFMOIhEhshcV+7n5mFV9/8jjrFzX+YZmALIOVY2w5Dba59h9B0o3nxSgiIRKbFCXiRMq1f+aBeTN5askM1oTqdtu+NOSmtP7nBXOfoNxXQ98DbgbggZc2YzYpDh2d3IoRCyGESBQPv1rMrwtqCQW9zP3ifIL+qt0+RwMhNCGIu2LEVrcnD4j+7LzsJMz7D9hFayHEroTDmn88VsDPnz/EhiWvxDqcuDXR1pnT7V0abLMevj+OvxwTo4iESHxSlIgT5qF5XLvgK67RmlB5NRvnLSJ/3mLyl61gzep81q38g3fLmv5Uy2BsuPSPrNQhhBBiK4spcuecUgaUwRjjaJrWx+jkxbRR2JURqzKQdurhpF95KrbkZAwOG8pmiSx1ZzZhMBg4u2xWrEMWot1QCsxGhTKYYx1KmzrR1pn/pAzGgsKsDJgxYFSK1GduxHrwiMgwC7MJzCYZ/iVEK5E5JeJc2OOjckMh6+94mpHvP9Jom/32/zudhkzCYIjUmP7511zGjdz7mXdl/JoQLaoj9WAkccQptzfM61NK+WJ6FXWVG1n0451UlixE63Ak3+swWodBhyLfY+jbzPHsZ47c6WfITid7/muNtpNrldhDkouboawyyGOvbeDj91/gj9+fBXT935muzxE02KZ0GIMOgdbo+hfWbPs5USzsdARZRmuDbbaJ40l77uYYRRQheU60M03mYSlKtBEdDlO3aQtb1m6gdEMBm9YXUbxxE1s2l1C6pZSKigoqa6qodFdT7a2j0u+mOuijMuwnuItTG33ShyRnRmbZnTghlb/9JbvJts0lCVCIFiUdYZGQtNb4A5oLb1/LlvIgi7+/jpK1U1v9dYebU7ArY3R2ehxJmI1mzCYzFlP9d7OFV1ZNZ/Lw47BaLFisVsxWK1abDZvNhtVuw+awR76cdhwuJ3ZnZPI5R4oLV7IDR5ILa7ILm8uJxeXAYLU0a+I5kbAkF8dAKKTxBzX3PLOBh2/7E37PlliH1KhjrdmYlAEzqsH3QFYOJqMJkynyZTaZMZnNmExmzGYLZkvku8lixmKxYLZaIrnIasVis2KxWbDYrFhtVqx2GxaHFbvDgdVpxWa3Y0+yY3VYsTrsmO12zHYbyrL1zgwjBpNJ+uSiPZGJLlubDoepWL2eNbPnsWbhUlYu/YP89esoKNlMQU0Zhf4a6nSoxV83HPJt+zksSUsIIUTLUEphMYPFHOlDhEP+NnndBYEd5rvwlTTZ9ukFX7bY6/4zaSCXuXpjOXgE6a/eibJ2rFvYhWgNRqPCblQYjfF978RUX3HjO9avb9tAgGtdedyU1D/6OLBsLeZBvdo8DiHakhQl9kA4GKRo/jJWzPyNpb8vZNnylawrKqCgupRCXzVV4UCrvK5dmUgxWnGZ7DhNTowGA/bUzmQceCbDxh1MKKzZL8/OiYektsrrCyGE6JiUUtx7dVceeW0zPvc/WW1xsXn1p9H9RpMVi8lKUiiMK6yxKSNGpfCj8aHx6TD++q+ADhHQIUJx+h+TZ+vyuczVG//P8/HPWY517NBYhyREu3H9Bd2oqXiXFx+7otGVf5TBhMFgxmQw4gyFsGqNWSlMGDApRRgI6MgKQAHCBHXke0CHCepw3OaVvfFcbT7XufpiVpG7tjwf/yRFCdHuyfCNRgTdXhZ/8S3zf/yFxQuXsnr9WtaVb2adp5JaHdzr45oxkGq0kmSy4zI5cFpdOG3JOJ0puJLTSUrLJDmzE+mdc0jv2oWsXt3J7d2FzHQHSU4jSU4jVotq9Ul2ZPiGEC1KbhkWHZ7WmmAIAkGN1xugptZLnduL2+Ojrs6Lu6YOd40bb00tnuo6PHVufHVuvHVufG43fo8Hv9cb/Qr4vLw6/Q1OHz6RQMCHz+dj2prpBPfhjsTbkwbwV1cfLGOHkv7aXSi7dfdPEolEcnE7FQprgsEwXl8Qj8eP1+vH7fXh9UV+9nqD+DwevHUe/HVevB4PPo+PgHtrTvER8PlYsOR3Ppz2TqxPh6udfbilfmWh3E1f4F+8BvPg3jGOSogWIXNKNPkkrVn/2zx++ehzZvw4k3mrlrGkevNeDbWwKgM55mQyrOmkJuWQkdWNrO59yRkwkF77j6BP/67kZFrIzjDjsMXvmFUpSgjRoqQjLEQr2P5aFQppJpz5P6Z/dPEeHePU4WO54oyzOGDcWBxdszF0SsPgcrRGuCL2JBeLXVqxYgVDhgwhGNz9B5AGwKVMOJQRh8GE3WDGZjRjN1qwm6zYzFbsFht2qw2HzYHD7sDpdOJ0OUlKTiYpJYmk1CRS01NJy0olKT0VV3oarow0zMkuDHYrym7F4LRLn1y0J1KUaEzB7AVMOvFUppWsbfZzXMpMF2samc4cMrN60Kl7P7oOHETv0SPoPzSPzllWOmWYEnpJTilKCNGipCMsRCvY8VpVUR3kroe+4q0XbydUsYGQv5a6cIDmrCViVgbSTA5SbEmkOlJITUonLS2TjE7ZZHXtQude3eg2oA89+3WnV48cHA57652YaC2Si8VuLVmyhD//5UKWLJq727ZOgxm70YrDZMNutuOwOnBYndgdLhyOJJxJybiSU0hKTyclPZ2UrExSczLJzM0iKzuDTpmpZGUm47A3vdSo9MlFOyNFiR0tfGcKR5x7JqXbTRS5vUyjnZ6urmSldadz9350GzyMvmPHMGhEHl2yLWSmmjAY2uf1TRKgEC2qfSaKxkniEG1md9eqUFhTVOzn9wXrmTtjLmtmz6Zw9VK2bFnOH3UF+/TL6jKYcRjMWA0mLAYTFoMZi9GE2WjGYrJgMdV/N1uxWCxYLVYsVhs269bVQeyRmfcdDhwuB3aXE2eSE2eyA1dyEs5UF0mpSSSlu3AkJ2NPScLqcmIyyVRg+0BysdgjWmsW3vcmyx9+ksWBKj7wFLIqWNvir+NQJuwGEyZlxKQMGJQBkzJgMhhZ5SllsKszRmXEZDBhMhgxGiM/b/1uMpkwGk3bVgkxmjCZtq4QElktJPrdYsZssUS+zJHVQixWM5boaiFWrPatq4VEvmwOGzanHZvThtVuxe60YXHYsdhsmB02LFZrqw8tF+1GYhUllFJ/BW4EOgNLgWu11tN387Q9OpEjs3oxrXRd9PEgR2e6pA+kz5AD2e+YY9n/0GH062EjPaXjdQCkKCFEi0rIK3Vb5GEh9kVT1yqtNYTDEAyjQyEIhiAU+Tng8bNyTTX5D77I3Omf8pl3E4t2XO0jgeQ4kvj622kMPehPsQ4lEUgu7sCieSGsI1+hUP02DaEwhMORx6Ew4WCQcDBEMBAgWFJO2eX3Eygpp1oHuaNqKV83tVKHaNQZB03ghS8/JiUlJdahiNhLnKKEUuos4A3gr8CM+u+TgEFa6w27eGqzTyRUVYsjNQX/djd1JhvMDEjqSdcuwxk4/mhGHTaKoUNzyMhNx+kwYzYl5LVsr0hRQogWlXDJoy3ysBD7IrSpFFNuFkWdj99pX2HIw+SK+cwOVMQgsrZ34aHH8L8fpsY6jEQgubid0aEQVdc/juf975vVfnmgmtPKfqNSt85qeaJp/3v5ZS6cNCnWYYjYS6iixCxgkdb60u22rQI+0FrfsounNvtEtNac0n0QnxasaFZ7uzJiVyZsBhM2gxmb0YLVaMFmsmI1W7FabFgtdmxWO3a7kxF/e4Du3TpxyuFpzQ0prkhRQogWlYgd4VbPw0Lsi9r/+5ikK05ttCjxTO0a/l3TvOt7e5FmsKBQGJSKfAcUkdW6DFD/PfK19eftv6vt2mz7bth2PLXt2Eo10mbrtu0eG5Rhu+1b2xqijw0GA0oZSHGZMJvqtxsibQwGAwaDIi8zh3NHjAVlAKXAEPlSditJ1/15T98mycXtjP/35ZSddGOz299atYRX3OtbMSKxK1YM2/IN9Tmqft/Wn5UiujW6Deqf1/C5KHazrf5ICgxbt+ywf8f22z9GbdtuUNEW0XPY/njsECNq+7Zqu+9bn7dtf8PjbhfDdo+3z7/scNzGXgu1w2srRfLWXLu1bVNfwF+Gj6VvZs7WN5/6A257XP+z7cgDsIweuCe/Bk3m4bgam6CUsgCjgId22PUNcFALvg4frV3MT8+/wbsvv8YXi2ZTEKhrsr1Hh/DoEIQbn39iR6u/upa8PuaELUoIITqutsrDQuwL64RRTe47zNqJp2vXUNGBPg2tCPtjHUKrOMyaxSm/l+60XaU496YokVAkF++eeUgfzEPzCCxa3az2x9lypCgRQz7Cuy+XdYhyWvwbvaKCXGvWbtsZszP2tCjRpLi6U0IplQsUAodorX/ebvudwF+01v13aH8ZcFn9Qxvg3YuXzQR2vuJ1bPKe7Ezek8bJ+7KzHd+TUq31MbEKZk/FKA+D/C41Rd6Xpsl70zR5b3Ymubh96Si/43Ke7U9HOdfGzrPJPBxXd0psZ8dKiWpkG1rr54Hn9+WFlFJztNb778sx2ht5T3Ym70nj5H3ZWTt6T9osD0O7et9alLwvTZP3pmny3rQrbZqLE0VH+R2X82x/Osq57ul5GlozmL1QCoSAnB22dwJkqlshhGh9koeFECL2JBcLITqMuCpKaK39wFzgyB12HQn80vYRCSFExyJ5WAghYk9ysRCiI4nH4RuPAK8rpWYDM4ErgFzguVZ6vQ5zq9sekPdkZ/KeNE7el521h/ekrfMwtI/3rTXI+9I0eW+aJu9N+xCLXJwoOsrvuJxn+9NRznWPzjOuJrrcSin1V+AmoDOwBLhu+0l+hBBCtC7Jw0IIEXuSi4UQHUFcFiWEEEIIIYQQQgjR/sXVnBJCCCGEEEIIIYToOKQoUU8pdZlS6gelVKVSSiulesY6pramlPqrUmqtUsqrlJqrlBof65hiSSl1sFJqilKqsP534sJYxxRrSqlblFK/K6WqlVJblFKfKaUGxzquWFNKTVZKLap/X6qVUr8qpY6PdVyJTEV8Vf+3d3qs44k1pVS6UupJpdQKpZRHKbVRKfWsUioj1rHFglyvGpLcLDqq9tp/7yg5riP0tTtSft6X/rAUJbZxAN8A/4xxHDGhlDoLeBz4DzCCyMzOU5VS3WMaWGy5iIzfvAbwxDiWeHEo8AxwEHAYEASmKaXSYxlUHCgAbgZGAvsD3wOfKKWGxjSqxPZ3IsvhiYhcoAuRseVDgHOBg4G3YxlULMj1qlGHIrlZdEztrv/ewXJcR+hrH0rHyc973R+WOSV2oJTaH/gd6KW1XhfjcNqMUmoWsEhrfel221YBH2itb4ldZPFBKVULXKW1fiXWscQTpZQLqAJO1lp/Fut44olSqhy4RWv9f7GOJdHU5+GPgVFAMXCG1vqD2EYVf5RSxwGfA6la6+pYx9NW5Hq1e5KbRUfTnvrvHTXHdZS+dkfLz83tD8udEgKllIVI5/+bHXZ9Q6SqJ0RTkojkkYpYBxIvlFJGpdTZRKr/spb8HlJKJRH59P9yrXVJrOOJc8mAD3DHOpC2IterZpPcLEQCkhzXIXSI/Lyn/WEpSgiATMBI5BPJ7RUDOW0fjkggjwMLgF9jHEfMKaWG1Ff5fUTWkD9Fa704xmEloueAr7TWX8Y6kHimlEoF7gFe0FoHYxxOW5LrVfNIbhYiMUmOa//adX7e2/5wuy5KKKX+XT9pyq6+Do11nHFkx7E8qpFtQgCglHoEGAecprWWsf+wEhgOjAGeBV5trxMZ7anm5mKl1HnAMODGWMfcVvbmOqWUcgKfAYVE5pjoiOR61QTJzSLRSf8dkBzXLnWQ/LxX/WFTKwcVa48Bb+ymzYY2iCPelRKZUG7HCmwndq7UCoFS6lHgbGCC1jo/1vHEA621H1hd/3COUmo0cB1wceyiihuP0bxcfCEwCKhVSm2/712l1K9a63GtEl1sPcYeXKfqx6JuvYvkBK21t5XiildyvdoFyc2inXiMjtt/lxzXTnWU/Ly3/eF2XZTQWpcS+eMWu6C19iul5gJHAu9vt+tI4MPYRCXilVLqcSJJ9VCt9YpYxxPHDIA11kHEg+bmYqXUbcBDO2xeDNwAfNoKocXcnlyn6ufbmErkE7NjtNa1rRlbPJLrVdMkN4v2oiP33yXHtU8dPD83qz/crosSe0IplUOkKtmvftOg+jG7G7TW5TELrO08AryulJoNzASuILIE3XMxjSqG6j+RzKt/aAC6K6WGA+Va6/Zaod8lpdTTwHnAyUBF/d8NQG1H/A/SVkqp+4EvgI1EJjA6h8gSUM1am1lEaK0LiQxJiKq/Y2Jje/5UoTnqCxLfEJnc8mTAWT+MAyI5yR+r2GJArlc7kNwsOqp22n/vMDmuI/S1O1J+3pf+sCwJWk8p9U/grkZ2TWrvS9NspZT6K5HxyZ2JrBl8ndb659hGFTv14xV/aGTXq1rrC9s0mDihlGoqYfxLa/3PtowlniilXgEmEOkYVQGLgAe11l/HMq72oP53rsMvCbqLfASRW0F/bLNg4oBcrxqS3Cw6qvbaf+8oOa4j9LU7Un7el/6wFCWEEEIIIYQQQggRE+169Q0hhBBCCCGEEELELylKCCGEEEIIIYQQIiakKCGEEEIIIYQQQoiYkKKEEEIIIYQQQgghYkKKEkIIIYQQQgghhIgJKUoIIYQQQgghhBAiJqQoIYQQQgghhBBCiJiQooQQQgghhBBCCCFiQooSQiQopdQUpVSFUuqDWMcihBAdleRiIYSILcnDiU+KEkIkrkeB82MdhBBCdHCSi4UQIrYkDyc4KUqIDkEplaaUKlZK9Wml43+ulHqlNY7dFK31D0BNI7F8oJS6vi1jEUKI5pBcLIQQsSV5WMQjKUqIFqWUylZKPa6UWqOU8imlCpVSU5VSx9Xvf0Uppeu/gkqpDUqpZ5VSaTsc5xWl1OeNHH//+uf23MPQbgW+1Fqv2dVrKKVOUEq5lVL37uHx48m/gNuVUimxDkQIERuSi+OC5GIhOjDJw3FB8nCCMMU6ANF+1CfFmUQqlbcAC4kUvg4HngO61zedBpxH5PdvEPAykAr8uZXicgCXACfupt15wIvATVrrx1sjluZSSi1pYtexWuuNu3qu1nqxUiofOBd4usWDE0LENcnFLUdysRBib0gebjmShzsGKUqIlvQMoID9tda1221frpR6c7vHPq315vqfC5RS7wIXtmJcxwFhIheHRimlrgH+C1yitX59VwerT+jPAKcDdcDjO+w/BrgNGAxo4HfgWq318vr95xMZ+5artfZt97w3gSSt9USt9eA9PckdTCFyQZMELETHI7kYycVCiJiSPIzkYdF8MnxDtAilVDpwDPDUDskXAK11RRPP613/vEArhjcemKu11k3EcA9wH3Dq7pJvvYeAI4HTiFS8RwAHb7ffCTwG/Ak4FKgCPlNKWer3v0/kb++k7WJIAU4BXmruSe3GbOBPSil7Cx1PCJEAJBdLLhZCxJbkYcnDYs/JnRKipeQRqQgvb0bbY5RStYARsNVva2wSmq3ttrc3hbQewKYm9h0JHA+coLX+YncHUkq5gIuBi7TWX9dvmwQUbG2jtf5wh+dMAqqJJOQZWmtPfQX4IuC9+mbn1LfZbQzbHXcaMAxwKqUKgDO01r/W7y4CzEAusKaJQwgh2h/JxfUkFwshYkTycD3Jw6K5pCghWorag7Y/A5cBduBSoA/wxC7abW8w8PEexmYHipvYtwRIAe5SSs3UWlfu5lh9AAuwNdGhta5VSi3e+lhFZjO+BzgAyCJy0TCwbfwgwAvAPKVUV611AZFk/KrWOtjck9JaH7GL3Z7671IVFqJjkVxcT3KxECJGJA/XkzwsmkuGb4iWsorIWLGBzWjr1lqv1lov1lr/DXAAd+yiXfSL7aqve6AUSGti3ybgECJJeNqOMx43ojkXms+IJN7LiSThEUCQSOIGQGu9EJgHXKiUGgzsT2Ryo5aSXv99SwseUwgR/yQXbyO5WAgRC5KHt5E8LJpFihKiRWity4Gvgavqb+dqQCmVuoun/wu4WSmV20rhzScyo3GjtNaFRMa5OYHvlFIZuzjWaiJj/cZs3aCUchKpVlP/3IHAf7TW0+on8kmi8buSXiAymdElwEyt9crmn9JuDQaKtNZNVcOFEO2Q5GLJxUKI2JI8LHlY7DkpSoiW9FciVdM5SqkzlFL9lVIDlFJXAouaepLW+kdgKXB7K8X1NTBwV4lVa72JSBK2AN8rpTKbaFdLZOKdB5RSRyql9iNSzTXWN6kgUoW+VCmVp5Q6hMjST43dgvY2kANcSctN5rPVeOCrFj6mECIxSC6WXCyEiC3Jw5KHxR6QooRoMVrrtcBI4FvgASJJ93tgIpHbtnblEeBipVSPVohrMZGZd8/eTbtiYEL9wx+UUp2aaHoD8AORcXw/EBmD93P9McLAWcDQ+u1PE7kNz7fjQbTWNUQm9fGzbXKffaaUshGZtfiFljqmECJxSC6WXCyEiC3Jw5KHxZ5RTawII0S7Ur9O8uPAIK11KNbxbKWUmgoUaK0vbcFjTgZO0lof1VLHFEKIliC5WAghYkvysIhHsvqG6BC01l8ppZ4GugLrYx1P/RrWRwBHEVnCqCUFgKtb+JhCCLHPJBcLIURsSR4W8UjulBAiBpRS64jMBnyv1vqBGIcjhBAdkuRiIYSILcnDAqQoIYQQQgghhBBCiBiRiS6FEEIIIYQQQggRE1KUEEIIIYQQQgghRExIUUIIIYQQQgghhBAxIUUJIYQQQgghhBBCxIQUJYQQQgghhBBCCBETUpQQQgghhBBCCCFETEhRQgghhBBCCCGEEDEhRQkhhBBCCCGEEELEhBQlhBBCCCGEEEIIERNSlBBCCCGEEEIIIURM/D9gJsApS9jC4QAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 1296x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(18, 5))\n",
+    "i = 0\n",
+    "\n",
+    "for ax in axes.flat:\n",
+    "    ax.tick_params(labelsize=14)\n",
+    "    ax.spines['top'].set_color('none')\n",
+    "    ax.spines['right'].set_color('none')\n",
+    "    ax.spines['left'].set_color('none')\n",
+    "    \n",
+    "    if i == 0:\n",
+    "        ax.set_title('Shallow cumulus', fontsize=15,pad=15)\n",
+    "        \n",
+    "        ax.plot((ds_lib2[3]['ddt_radlw_mean'].values),z_fl2,color='#4363d8',\n",
+    "                linestyle='-',linewidth=3,label='LEM clouds')\n",
+    "        \n",
+    "        ax.plot((ds_lib2[4]['ddt_radlw_mean'].values),z_fl2,color='#e6194B',\n",
+    "                linestyle='-',linewidth=3,label='NWP grid-box clouds')\n",
+    "        \n",
+    "        ax.plot((ds_lib2[5]['ddt_radlw_mean'].values),z_fl2,color='k',\n",
+    "                linestyle='-',linewidth=3,label='NWP grid-box clouds with fraction')\n",
+    "        \n",
+    "        #ax.spines['bottom'].set_bounds(-3,3)\n",
+    "        #ax.set_xticks(np.linspace(-3,3,5))\n",
+    "        #ax.set_xlim([-3.2,3])\n",
+    "        ax.set_ylim([0,12])\n",
+    "        \n",
+    "        ax.axvline(x=0, ymin=0.0, ymax=1,c='black', lw=1)\n",
+    "        ax.set_ylabel('Height (km)', fontsize=14)\n",
+    "        ax.set_xlabel('CRH  (K day$^{-1}$)', fontsize=14)#,labelpad=10)\n",
+    "        ax.text(0.0, 1.06, '(a)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "        lg=colorlegend.color_legend(ax,loc=1,fsize=14)\n",
+    "        \n",
+    "    if i == 1:\n",
+    "        ax.set_title('Shallow cumulus', fontsize=15,pad=15)\n",
+    "\n",
+    "        ax.plot((ds_lib2[0]['ddt_radlw_mean'].values),z_fl2,color='#4363d8',\n",
+    "                linestyle='-',linewidth=3,label='LEM clouds')\n",
+    "\n",
+    "        ax.plot((ds_lib2[1]['ddt_radlw_mean'].values),z_fl2,color='#e6194B',\n",
+    "                linestyle='-',linewidth=3,label='NWP grid-box clouds')\n",
+    "\n",
+    "        ax.plot((ds_lib2[2]['ddt_radlw_mean'].values),z_fl2,color='k',\n",
+    "                linestyle='-',linewidth=3,label='NWP grid-box clouds with fraction')\n",
+    "\n",
+    "        #ax.spines['bottom'].set_bounds(-3,3)\n",
+    "        #ax.set_xticks(np.linspace(-3,3,5))\n",
+    "        #ax.set_xlim([-3.2,3])\n",
+    "        ax.set_ylim([0,12])\n",
+    "\n",
+    "        ax.axvline(x=0, ymin=0.0, ymax=1,c='black', lw=1)\n",
+    "\n",
+    "        ax.set_xlabel('CRH  (K day$^{-1}$)', fontsize=14)#,labelpad=10)\n",
+    "        ax.text(0.0, 1.06, '(b)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    if i == 2:\n",
+    "        # total effect\n",
+    "        net1 = ds_lib2[0]['ddt_radlw_mean'].values+ds_lib2[3]['ddt_radlw_mean'].values\n",
+    "        net2 = ds_lib2[1]['ddt_radlw_mean'].values+ds_lib2[4]['ddt_radlw_mean'].values\n",
+    "        net3 = ds_lib2[2]['ddt_radlw_mean'].values+ds_lib2[5]['ddt_radlw_mean'].values\n",
+    "        ax.set_title('Shallow cumulus', fontsize=15,pad=15)\n",
+    "\n",
+    "        ax.plot(net1,z_fl2,color='#4363d8',\n",
+    "                linestyle='-',linewidth=3,label='LEM clouds')\n",
+    "\n",
+    "        ax.plot(net2,z_fl2,color='#e6194B',\n",
+    "                linestyle='-',linewidth=3,label='NWP grid-box clouds')\n",
+    "\n",
+    "        ax.plot(net3,z_fl2,color='k',\n",
+    "                linestyle='-',linewidth=3,label='NWP grid-box clouds with fraction')\n",
+    "\n",
+    "        #ax.spines['bottom'].set_bounds(-3,3)\n",
+    "        #ax.set_xticks(np.linspace(-3,3,5))\n",
+    "        #ax.set_xlim([-3.2,3])\n",
+    "        ax.set_ylim([0,12])\n",
+    "\n",
+    "        ax.axvline(x=0, ymin=0.0, ymax=1,c='black', lw=1)\n",
+    "\n",
+    "        ax.set_xlabel('CRH  (K day$^{-1}$)', fontsize=14)#,labelpad=10)\n",
+    "        ax.text(0.0, 1.06, '(c)', transform=ax.transAxes, \n",
+    "            size=14)\n",
+    "        \n",
+    "    i = i + 1\n",
+    "\n",
+    "    \n",
+    "fig.subplots_adjust(wspace=0.2,hspace=0.35)\n",
+    "#plt.savefig('figure9.pdf', bbox_inches = 'tight')\n",
+    "#plt.savefig('figure9.png', bbox_inches = 'tight',dpi=300) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "3ec3a960-8e8e-491c-9c12-4d1ab1e3abbf",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1296x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# with remapped clouds\n",
+    "plt.figure(figsize=(18,5))\n",
+    "\n",
+    "plt.subplot(131)\n",
+    "\n",
+    "diff1 = ds_lib1[1]['ddt_radlw_mean'].values-ds_lib1[0]['ddt_radlw_mean'].values\n",
+    "plt.plot(abs(diff1),z_fl2,color='#4363d8',linewidth=3,label='horizontal heterogenity')\n",
+    "\n",
+    "diff1 = ds_lib1[2]['ddt_radlw_mean'].values-ds_lib1[1]['ddt_radlw_mean'].values\n",
+    "plt.plot(abs(diff1),z_fl2,color='#e6194B',linewidth=3,label='vertical overlap')\n",
+    "\n",
+    "plt.legend(fontsize=12)\n",
+    "plt.tick_params(labelsize=14)\n",
+    "plt.ylabel('Height (km)',fontsize=14)\n",
+    "plt.xlabel('LW CRH abs diff (K/day)',fontsize=14)\n",
+    "plt.ylim(0,3)\n",
+    "plt.title('Shallow cumulus',fontsize=14)\n",
+    "plt.grid()\n",
+    "\n",
+    "plt.subplot(132)\n",
+    "\n",
+    "diff1 = ds_lib1[4]['ddt_radlw_mean'].values-ds_lib1[3]['ddt_radlw_mean'].values\n",
+    "plt.plot(abs(diff1),z_fl2,color='#4363d8',linewidth=3,label='horizontal heterogenity')\n",
+    "\n",
+    "diff1 = ds_lib1[5]['ddt_radlw_mean'].values-ds_lib1[4]['ddt_radlw_mean'].values\n",
+    "plt.plot(abs(diff1),z_fl2,color='#e6194B',linewidth=3,label='vertical overlap')\n",
+    "\n",
+    "plt.legend(fontsize=12)\n",
+    "plt.tick_params(labelsize=14)\n",
+    "plt.ylabel('Height (km)',fontsize=14)\n",
+    "plt.xlabel('SW CRH abs diff (K/day)',fontsize=14)\n",
+    "plt.ylim(0,3)\n",
+    "plt.title('Shallow cumulus',fontsize=14)\n",
+    "plt.grid()\n",
+    "\n",
+    "plt.subplot(133)\n",
+    "\n",
+    "net1 = ds_lib1[0]['ddt_radlw_mean'].values+ds_lib1[3]['ddt_radlw_mean'].values\n",
+    "net2 = ds_lib1[1]['ddt_radlw_mean'].values+ds_lib1[4]['ddt_radlw_mean'].values\n",
+    "net3 = ds_lib1[2]['ddt_radlw_mean'].values+ds_lib1[5]['ddt_radlw_mean'].values\n",
+    "\n",
+    "diff1 = net1 - net2\n",
+    "plt.plot(abs(diff1),z_fl2,color='#4363d8',linewidth=3,label='horizontal heterogenity')\n",
+    "\n",
+    "diff1 = net2 - net3\n",
+    "plt.plot(abs(diff1),z_fl2,color='#e6194B',linewidth=3,label='vertical overlap')\n",
+    "\n",
+    "plt.legend(fontsize=12)\n",
+    "plt.tick_params(labelsize=14)\n",
+    "plt.ylabel('Height (km)',fontsize=14)\n",
+    "plt.xlabel('Net CRH abs diff (K/day)',fontsize=14)\n",
+    "plt.ylim(0,3)\n",
+    "plt.title('Shallow cumulus',fontsize=14)\n",
+    "plt.grid()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "0fbe574d-4278-4b98-b92e-d0cfb4b7c1d0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "client.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1122fa67-57d7-4812-b9f7-7198bb48cbbc",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pycrh",
+   "language": "python",
+   "name": "pycrh"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/plots4paper/figure9.pdf b/plots4paper/figure9.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..9b66a5414d896958fe299b8a2272be5c2d9ce708
Binary files /dev/null and b/plots4paper/figure9.pdf differ
diff --git a/sims/README.md b/sims/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..4c7b69e07ae8cb6bb071cba15a0bd721e718a514
--- /dev/null
+++ b/sims/README.md
@@ -0,0 +1,11 @@
+This directory contains the scripts for setting up the ICON model for both the baroclinic life cycle simulation with ICON-NWP and the large eddy model simulations with ICON-LEM.
+
+* The **planar_grid** subdirectory contains the script to generate the planar grid for large eddy model simulations using the MPI grid generator.
+
+* The procedure for the baroclinic life cycle simulation setup can be found https://gitlab.phaidra.org/climate/keshtgar-etal-crh-cyclone-wcd2022/-/tree/main/blc_initial_conditions
+
+* The **preprocessing** subdirectory contains the scripts to prepare the initial and lateral boundary conditions from the ICON-NWP simulation for ICON-LEM simulations using DWD_ICON_tools and CDO. Run the scripts in order, 1, 2 and 3.
+
+* The **runscript** subdirectory contains the ICON runscript for the baroclinic life cycle and large eddy model simulations.
+
+ - The baroclinic life cycle simulation (*LC1-channel-4000x9000km-2km-0002*) follows the same model setup as in Keshtgar et al., 2023 (https://wcd.copernicus.org/articles/4/115/2023/).
diff --git a/sims/planar_grid/grid.create_plane.run b/sims/planar_grid/grid.create_plane.run
new file mode 100755
index 0000000000000000000000000000000000000000..d9f737903feb273731cfb402160a6716c5df0f1d
--- /dev/null
+++ b/sims/planar_grid/grid.create_plane.run
@@ -0,0 +1,120 @@
+#!/bin/bash
+#=============================================================================
+#SBATCH --account=bb1135
+#SBATCH --job-name=grid.create_plane_grids.run
+#SBATCH --partition=compute
+####SBATCH --workdir=
+#SBATCH --nodes=1
+#SBATCH --threads-per-core=2
+#SBATCH --mem=120000
+#SBATCH --output=/work/bb1135/b381185/tools/GridGenerator_master/run/LOG.grid.create_plane_grids.run.%j.o
+#SBATCH --error=/work/bb1135/b381185/tools/GridGenerator_master/run/LOG.grid.create_plane_grids.run.%j.o
+#SBATCH --exclusive
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is mpipc
+# target use_compiler is intel
+# with mpi=no
+# with openmp=yes
+# memory_model=huge
+# submit with 
+# 
+#=============================================================================
+set -x
+. ./add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="zmaw.de"
+target="mpipc"
+compiler="intel"
+loadmodule="gcc/5.1.0 intel/16.0.0 "
+with_mpi="no"
+with_openmp="yes"
+job_name="grid.create_channels.run"
+submit=""
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=2
+export ICON_THREADS=2
+export OMP_SCHEDULE=static
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=512M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=
+no_of_nodes=1
+mpi_procs_pernode=1
+mpi_total_procs=1
+START=""
+#-----------------------------------------------------------------------------
+# load ../setting if exists  
+if [ -a ../setting ]
+then
+  echo "Load Setting"
+  . ../setting
+fi
+#-----------------------------------------------------------------------------
+bindir="${basedir}/build/x86_64-unknown-linux-gnu/bin"   # binaries
+BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a  /client/etc/profile.zmaw ] ; then
+. /client/etc/profile.zmaw
+#=============================================================================
+#=============================================================================
+# load modules
+# module purge
+module unload gcc intel nag 
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/wheezy-x64/netcdf-4.3.2-static-gccsys/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=64
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+#!/bin/bash
+#=============================================================================
+WORKDIR=${basedir}/grids
+. ${thisdir}/create_grids_tools
+#-----------------------------------------------------------------------------
+set -x
+#-----------------------------------------------------------------------------
+# Defining grid properties for each LEM domains
+# choose the lat/lon center of desired domain
+# dom01: shallow cumulus clouds   lon_center=25.0, lat_center=40.0
+# dom02: WCB ascent region        lon_center=40.0, lat_center=44.0
+# dom03: WCB cyclonic outflow     lon_center=30.0, lat_center=53.0
+# dom04: WCB anticyclonic otflow  lon_center=50.0, lat_center=48.0
+
+lon_center=40.0
+lat_center=44.0
+lon_range=6
+lat_range=6
+
+# using latitude/longitude distance calculator (https://www.nhc.noaa.gov/gccalc.shtml)
+
+x_length_km=471
+y_length_km=667
+
+resolution=300
+
+create_raggedOrthogonal_ofTriangles $x_length_km $y_length_km $resolution
+
+# defining lateral boundary cells
+create_limited_area_boundaries "raggedOrthogonal_${x_length_km}x${y_length_km}_${resolution}.nc" "raggedOrthogonal_${x_length_km}x${y_length_km}_${resolution}_with_boundary_dom01.nc"
+
+exit
+
diff --git a/sims/preprocessing_scripts/1.extpar_nest_plane.sh b/sims/preprocessing_scripts/1.extpar_nest_plane.sh
new file mode 100755
index 0000000000000000000000000000000000000000..63fc45b3c232a43ece4d150ade4bec266bfc4bb7
--- /dev/null
+++ b/sims/preprocessing_scripts/1.extpar_nest_plane.sh
@@ -0,0 +1,166 @@
+#! /bin/bash
+set -x
+ulimit -s unlimited
+#=============================================================================
+# OpenMP environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=$OMP_NUM_THREADS
+export OMP_SCHEDULE="guided"
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=500M
+#
+# MPI variables
+# -------------
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((128 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+
+ulimit -s $((4 * 1024 * 1024))
+ulimit -c 0
+
+
+# Loading modules 
+
+module purge
+module load cdo/2.0.5-gcc-11.2.0
+module load python3/2022.01-gcc-11.2.0
+module load nco/5.0.6-gcc-11.2.0
+
+module list
+
+#--------------------------------------------------------------------------------------
+# ICONTOOLS directory
+
+ICONTOOLS_DIR=/home/b/b381185/dwd_icon_tools/icontools
+
+BINARY_ICONSUB=iconsub
+BINARY_REMAP=iconremap
+BINARY_GRIDGEN=icongridgen
+
+#--------------------------------------------------------------------------------------
+# here choose the grid for different LEM domains
+# dom01: shallow cumulus clouds   lon_center=25.0, lat_center=40.0
+# dom02: WCB ascent region        lon_center=40.0, lat_center=44.0
+# dom03: WCB cyclonic outflow     lon_center=30.0, lat_center=53.0
+# dom04: WCB anticyclonic otflow  lon_center=50.0, lat_center=48.0
+
+
+mkdir plane_nest_300m_r6x6_2mom_25_40_0001
+cd plane_nest_300m_r6x6_2mom_25_40_0001
+
+# import the grid from grid_generator
+gridfile=raggedOrthogonal_471x667_300_with_boundary_dom01.nc
+cp /work/bb1135/from_Mistral/bb1135/b381185/tools/GridGenerator_master/grids/$gridfile ./
+#---------------------------------------------------------------------------------------
+# 1- Remmaping the external prameters file onto the nest grid
+# Aquaplanet extpar file
+
+for field in SOILTYP FR_LAND ICE PLCOV_MX LAI_MX RSMIN URBAN FOR_D FOR_E EMIS_RAD ROOTDP Z0 NDVI_MAX topography_c SSO_STDH SSO_THETA SSO_GAMMA SSO_SIGMA T_CL FR_LAKE DEPTH_LK topography_v LU_CLASS_FRACTION NDVI NDVI_MRAT AER_BC AER_DUST AER_ORG AER_SO4 AER_SS ALB ALNID ALUVD lon lat clon clat clon_vertices clat_vertices ; do
+
+cat >> NAMELIST_ICONREMAP_FIELDS << EOF_2A
+!
+&input_field_nml
+ inputname      = "${field}"
+ outputname     = "${field}"
+ intp_method    = 3
+/
+EOF_2A
+
+done
+
+cat NAMELIST_ICONREMAP_FIELDS
+
+cat > NAMELIST_ICONREMAP << EOF_2C
+&remap_nml
+ in_grid_filename  = '../inputs/icon_grid_0010_R02B04_G.nc'
+ in_filename       = '../inputs/icon_extpar_0010_R02B04_G_aquaplanet.nc'
+ in_type           = 2
+ out_grid_filename = '$gridfile'
+ out_filename      = 'extpar_remapped.nc'
+ out_type          = 2
+ out_filetype      = 4
+ l_have3dbuffer    = .false.
+ ncstorage_file    = "ncstorage.tmp"
+/
+EOF_2C
+
+
+${ICONTOOLS_DIR}/${BINARY_REMAP} \
+            --remap_nml NAMELIST_ICONREMAP                                  \
+            --input_field_nml NAMELIST_ICONREMAP_FIELDS 2>&1
+
+#-----------------------------------------------------------------------------
+# clean-up
+
+rm -f ncstorage.tmp*
+rm -f nml.log  NAMELIST_SUB NAMELIST_ICONREMAP NAMELIST_ICONREMAP_FIELDS
+
+#-----------------------------------------------------------------------------
+
+
+# 4- Remmaping the Ozone file onto the channel grid
+
+# APE O3 file for irad_o3 = 4
+
+for field in O3 ; do
+
+cat >> NAMELIST_ICONREMAP_FIELDS << EOF_2A
+!
+&input_field_nml
+ inputname      = "${field}"
+ outputname     = "${field}"
+ intp_method    = 3
+/
+EOF_2A
+
+done
+
+cat > NAMELIST_ICONREMAP << EOF_2C
+&remap_nml
+ in_grid_filename  = ''
+ in_filename       = '../inputs/ape_o3_R2B04_1Pa_cell.t63grid.nc'
+ in_type           = 1
+ out_grid_filename = '$gridfile'
+ out_filename      = 'ape_O3_remapped.nc'
+ out_type          = 2
+ out_filetype      = 4
+ l_have3dbuffer    = .false.
+ ncstorage_file    = "ncstorage.tmp"
+/
+EOF_2C
+
+${ICONTOOLS_DIR}/${BINARY_REMAP} \
+            --remap_nml NAMELIST_ICONREMAP                                  \
+            --input_field_nml NAMELIST_ICONREMAP_FIELDS 2>&1
+
+#-----------------------------------------------------------------------------
+# clean-up
+
+rm -f ncstorage.tmp*
+rm -f nml.log  NAMELIST_SUB NAMELIST_ICONREMAP NAMELIST_ICONREMAP_FIELDS
+
+#-----------------------------------------------------------------------------
+# Correction of remmaped extpar file 
+
+ncatted -a rawdata,global,c,c,"GLOBCOVER2009, FAO DSMW, GLOBE, Lake Database" extpar_remapped.nc
+
+# Run the python script for correction of the type and month dimension
+
+python ../inputs/extpar_helper.py
+
+# modification of grid global attributes
+python ../inputs/grid_change.py $gridfile
+#----------------------------------------------------------------------------
+
+# Correction of remmaped ozone file
+
+ncrename -d plev,level ape_O3_remapped.nc
+ncrename -v plev,level ape_O3_remapped.nc
+
+# clean 
+rm extpar_remapped.nc 
+
+
+
+
diff --git a/sims/preprocessing_scripts/2.iconnwp_4iconnest_init_lc1.sh b/sims/preprocessing_scripts/2.iconnwp_4iconnest_init_lc1.sh
new file mode 100755
index 0000000000000000000000000000000000000000..3cca85d73fc538a6fc8519deeec6103174d8be89
--- /dev/null
+++ b/sims/preprocessing_scripts/2.iconnwp_4iconnest_init_lc1.sh
@@ -0,0 +1,162 @@
+#! /bin/bash
+set -x
+ulimit -s unlimited
+#=============================================================================
+# OpenMP environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=$OMP_NUM_THREADS
+export OMP_SCHEDULE="guided"
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=500M
+#
+# MPI variables
+# -------------
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((128 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+
+ulimit -s $((4 * 1024 * 1024))
+ulimit -c 0
+
+
+# Loading modules
+
+module purge
+module load cdo/2.0.5-gcc-11.2.0
+module load python3/2022.01-gcc-11.2.0
+module load nco/5.0.6-gcc-11.2.0
+
+module list
+
+#--------------------------------------------------------------------------------------
+# ICONTOOLS directory
+
+ICONTOOLS_DIR=/home/b/b381185/dwd_icon_tools/icontools
+
+BINARY_ICONSUB=iconsub
+BINARY_REMAP=iconremap
+BINARY_GRIDGEN=icongridgen
+
+#=============================================================================
+# here choose the grid for different LEM domains
+# dom01: shallow cumulus clouds   lon_center=25.0, lat_center=40.0
+# dom02: WCB ascent region        lon_center=40.0, lat_center=44.0
+# dom03: WCB cyclonic outflow     lon_center=30.0, lat_center=53.0
+# dom04: WCB anticyclonic otflow  lon_center=50.0, lat_center=48.0
+#=============================================================================
+
+EXPNAME=plane_nest_300m_r6x6_2mom_25_40_0001
+
+gridfile=raggedOrthogonal_471x667_300_with_boundary_dom01.nc
+
+INDATDIR=/work/bb1135/icon_output/LC1-channel-4000x9000km-2km-0002  # the folder for the icon-nwp input
+OUTDATDIR=/work/bb1135/LES_Simulations/initial_conditions/${EXPNAME}    # the folder for the remapped output
+
+in_grid_File=/work/bb1135/icon_output/LC1-channel-4000x9000km-2km-0002/grid_DOM01.nc
+out_grid_File=/work/bb1135/LES_Simulations/initial_conditions/${EXPNAME}/$gridfile
+
+in_data_File=${INDATDIR}/icon-fg_ML_202201
+out_data_File=${OUTDATDIR}/icon-fg_ML_nest
+
+### interpolation method
+# 2 -> interpolation : conservative (gives problems in the outer most cells in HDCP2-DE)
+# 3 -> interpolation : rbf: scalar  (RBF method, needs correct values for rbf_scale_scalar)
+intp_method=3
+
+# the directory for the experiment will be created, if not already there
+if [ ${OUTDATDIR}.notset = .notset ]; then
+    echo "OUTDATDIR not set"
+    exit 1
+fi
+
+if [ ! -d $OUTDATDIR ]; then
+    mkdir -p $OUTDATDIR
+fi
+#
+cd $OUTDATDIR
+#
+
+for day in 05 ; do
+
+for hour in 06 ; do 
+
+rm -f indata.nc indata-vn.nc
+cp ${in_data_File}${day}T${hour}0030Z.nc indata.nc
+# add grid to in_data_File, needed for vn interpolation
+# ATTENTION: make sure to use cdo/1.7.0-magicsxx-gcc48, other cdo version can lead
+# to a crash of the vn remapping with the dwdicontools
+
+cdo -P 32 setgrid,${in_grid_File} -selname,vn ${in_data_File}${day}T${hour}0030Z.nc indata-vn.nc
+
+# create ICON master namelist: obtained from Matthias Brueck
+# ------------------------
+cat > ${OUTDATDIR}/tmp.nml << REMAP_NML_EOF
+! REMAPPING NAMELIST FILE
+!
+&remap_nml
+ in_grid_filename   = '${in_grid_File}'
+ in_filename        = 'indata.nc'
+ in_type            = 2
+ out_grid_filename  = '${out_grid_File}'
+ out_filename       = 'outdata.nc'
+ out_type           = 2
+ out_filetype       = 5
+ !s_maxsize         = 1000000
+ lsynthetic_grid    = .FALSE.
+/
+REMAP_NML_EOF
+
+for field in  w rho theta_v qv qc qi qr qs tke u v pres_sfc temp pres z_ifc t_2m td_2m u_10m v_10m fr_land gz0 t_g t_ice h_ice alb_si qv_s fr_seaice t_sk t_seasfc w_i t_so w_so w_so_ice t_snow w_snow rho_snow h_snow freshsnow snowfrac_lc rho_snow_mult t_snow_mult wliq_snow wtot_snow dzh_snow ; do 
+
+cat >> ${OUTDATDIR}/tmp.nml << REMAP_NML_EOF 
+! 
+&input_field_nml  
+ inputname      = "${field}"  
+ outputname     = "${field}"  
+ intp_method    = ${intp_method} 
+/ 
+REMAP_NML_EOF
+
+done
+
+cat > ${OUTDATDIR}/tmp-vn.nml << REMAP_NML_EOF
+! REMAPPING NAMELIST FILE
+!
+&remap_nml
+ in_grid_filename   = '${in_grid_File}'
+ in_filename        = 'indata-vn.nc'
+ in_type            = 2
+ out_grid_filename  = '${out_grid_File}'
+ out_filename       = 'outdata-vn.nc'
+ out_type           = 2
+ out_filetype       = 5
+ !s_maxsize         = 1000000
+ lsynthetic_grid    = .FALSE.
+/
+! DEFINITION FOR INPUT DATA FIELD
+!
+&input_field_nml
+ inputname      = "vn"
+ outputname     = "vn"
+!! intp_method    = ${intp_method}
+/
+REMAP_NML_EOF
+
+# Hint: option -vvvvvvvvvvvv activates a lot of diagnostic output
+#${START} ${ICONTOOLS_DIR}/${BINARY_REMAP} --remap_nml ${OUTDATDIR}/tmp.nml    2>&1
+#${START} ${ICONTOOLS_DIR}/${BINARY_REMAP} --remap_nml ${OUTDATDIR}/tmp-vn.nml 2>&1
+
+${ICONTOOLS_DIR}/${BINARY_REMAP} --remap_nml ${OUTDATDIR}/tmp.nml    2>&1
+${ICONTOOLS_DIR}/${BINARY_REMAP} --remap_nml ${OUTDATDIR}/tmp-vn.nml 2>&1
+
+# merge interpolated output
+cdo -O merge outdata.nc outdata-vn.nc ${out_data_File}_202201${day}T${hour}0030Z.nc
+
+# clean up
+rm -f indata.nc indata-vn.nc outdata.nc outdata-vn.nc
+
+done # end of loop over times (hours)
+
+done
+##cd ${RUNSCRIPTDIR}
diff --git a/sims/preprocessing_scripts/3.iconnwp_4iconnest_latbc_lc1.sh b/sims/preprocessing_scripts/3.iconnwp_4iconnest_latbc_lc1.sh
new file mode 100755
index 0000000000000000000000000000000000000000..f66b7e6654e0ab1798c99410b5e160dd2620cc8d
--- /dev/null
+++ b/sims/preprocessing_scripts/3.iconnwp_4iconnest_latbc_lc1.sh
@@ -0,0 +1,139 @@
+#! /bin/bash
+set -x
+ulimit -s unlimited
+#=============================================================================
+# OpenMP environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=$OMP_NUM_THREADS
+export OMP_SCHEDULE="guided"
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=500M
+#
+# MPI variables
+# -------------
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((128 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+
+ulimit -s $((4 * 1024 * 1024))
+ulimit -c 0
+
+
+# Loading modules
+
+module purge
+module load cdo/2.0.5-gcc-11.2.0
+module load python3/2022.01-gcc-11.2.0
+module load nco/5.0.6-gcc-11.2.0
+
+module list
+
+#--------------------------------------------------------------------------------------
+# ICONTOOLS directory
+
+ICONTOOLS_DIR=/home/b/b381185/dwd_icon_tools/icontools
+
+BINARY_ICONSUB=iconsub
+BINARY_REMAP=iconremap
+BINARY_GRIDGEN=icongridgen
+
+#=============================================================================
+# here choose the grid for different LEM domains
+# dom01: shallow cumulus clouds   lon_center=25.0, lat_center=40.0
+# dom02: WCB ascent region        lon_center=40.0, lat_center=44.0
+# dom03: WCB cyclonic outflow     lon_center=30.0, lat_center=53.0
+# dom04: WCB anticyclonic otflow  lon_center=50.0, lat_center=48.0
+#=============================================================================
+
+EXPNAME=plane_nest_300m_r6x6_2mom_25_40_0001
+
+gridfile=raggedOrthogonal_471x667_300_with_boundary_dom01.nc
+
+INDATDIR=/work/bb1135/icon_output/LC1-channel-4000x9000km-2km-0002  # the folder for the icon-nwp input
+OUTDATDIR=/work/bb1135/LES_Simulations/initial_conditions/${EXPNAME}    # the folder for the remapped output
+
+in_grid_File=/work/bb1135/icon_output/LC1-channel-4000x9000km-2km-0002/grid_DOM01.nc
+out_grid_File=/work/bb1135/LES_Simulations/initial_conditions/${EXPNAME}/$gridfile
+
+in_data_File=${INDATDIR}/icon-fg_ML_202201
+out_data_File=${OUTDATDIR}/icon-fg_ML_nest
+
+
+### interpolation method
+# 2 -> interpolation : conservative (gives problems in the outer most cells in HDCP2-DE)
+# 3 -> interpolation : rbf: scalar  (RBF method, needs correct values for rbf_scale_scalar)
+intp_method=3
+
+# the directory for the experiment will be created, if not already there
+if [ ${OUTDATDIR}.notset = .notset ]; then
+    echo "OUTDATDIR not set"
+    exit 1
+fi
+
+if [ ! -d $OUTDATDIR ]; then
+    mkdir -p $OUTDATDIR
+fi
+#
+cd $OUTDATDIR
+#
+
+year=2022
+month=01
+
+for day in 05 ; do
+
+for hour in 06 07 08 09 10 11 12 13 14 15; do
+
+#rm -f indata.nc indata-vn.nc
+#cp ${in_data_File}${day}T${hour}0030Z.nc indata.nc
+
+# create ICON master namelist: obtained from Matthias Brueck
+# ------------------------
+cat > ${OUTDATDIR}/tmp.nml << REMAP_NML_EOF
+! REMAPPING NAMELIST FILE
+!
+&remap_nml
+ in_grid_filename   = '${in_grid_File}'
+ in_filename        = '${in_data_File}${day}T${hour}0030Z.nc'
+ in_type            = 2
+ out_grid_filename  = '${out_grid_File}'
+ out_filename       = 'outdata.nc'
+ out_type           = 2
+ out_filetype       = 5
+ !s_maxsize         = 1000000
+ lsynthetic_grid    = .FALSE.
+/
+REMAP_NML_EOF
+
+for field in z_ifc U V W THETA_V RHO QV QC QI QR QS ; do 
+
+cat >> ${OUTDATDIR}/tmp.nml << REMAP_NML_EOF 
+! 
+&input_field_nml  
+ inputname      = "${field}"  
+ outputname     = "${field}"  
+ intp_method    = ${intp_method} 
+/ 
+REMAP_NML_EOF
+
+done
+
+#${START} ${ICONTOOLS_DIR}/${BINARY_REMAP} --remap_nml ${OUTDATDIR}/tmp.nml    2>&1
+${ICONTOOLS_DIR}/${BINARY_REMAP} --remap_nml ${OUTDATDIR}/tmp.nml    2>&1
+
+#${START}${ICONTOOLS_DIR}/${BINARY_REMAP} --remap_nml ${OUTDATDIR}/tmp-vn.nml 2>&1
+
+
+# merge interpolated output
+#cdo -O merge outdata.nc outdata-vn.nc ${out_data_File}_${year}-${month}-${day}T${hour}.nc
+
+mv outdata.nc ${out_data_File}_${year}-${month}-${day}T${hour}.nc
+
+# clean up
+#rm -f indata.nc 
+
+done # end of loop over times (hours)
+
+done
+##cd ${RUNSCRIPTDIR}
diff --git a/sims/preprocessing_scripts/inputs/ape_o3_R2B04_1Pa_cell.t63grid.nc b/sims/preprocessing_scripts/inputs/ape_o3_R2B04_1Pa_cell.t63grid.nc
new file mode 100644
index 0000000000000000000000000000000000000000..3ab36a5f7056f1ffeb8807fede792752df55106a
Binary files /dev/null and b/sims/preprocessing_scripts/inputs/ape_o3_R2B04_1Pa_cell.t63grid.nc differ
diff --git a/sims/preprocessing_scripts/inputs/extpar_helper.py b/sims/preprocessing_scripts/inputs/extpar_helper.py
new file mode 100644
index 0000000000000000000000000000000000000000..d7d12df0b5cf5cf674950b4508fd2d395a8c966b
--- /dev/null
+++ b/sims/preprocessing_scripts/inputs/extpar_helper.py
@@ -0,0 +1,27 @@
+import xarray as xr
+import numpy as np
+
+ds = xr.open_dataset('extpar_remapped.nc')
+
+time_array = np.array([1.111011e+07, 1.111021e+07, 1.111031e+07, 1.111041e+07, 1.111051e+07,1.111061e+07, 1.111071e+07, 1.111081e+07,1.111091e+07, 1.111101e+07,1.111111e+07, 1.111121e+07])
+
+newds_list = []
+for ind in range(0,12):
+    temp = ds.copy(deep=True)
+    temp['time']= temp['time']*0 + time_array[ind]
+    newds_list.append(temp)
+
+temp = ds.copy(deep=True)
+
+newds = xr.merge(newds_list)
+
+newds['SOILTYP'] = newds['SOILTYP'].astype('int32', copy=True)
+
+newds.attrs = ds.attrs
+
+for var in ds.data_vars.keys():
+    newds[var].attrs = ds[var].attrs
+
+newds['T_CL'][:] = 285
+
+newds.to_netcdf('extpar_remapped_12_months.nc')
diff --git a/sims/preprocessing_scripts/inputs/grid_change.py b/sims/preprocessing_scripts/inputs/grid_change.py
new file mode 100644
index 0000000000000000000000000000000000000000..63336a16ee94cd77a81f0ae39f0f01424d787e33
--- /dev/null
+++ b/sims/preprocessing_scripts/inputs/grid_change.py
@@ -0,0 +1,9 @@
+import xarray as xr
+import sys
+
+
+grid = xr.open_dataset(sys.argv[1])
+grid.attrs['grid_geometry'] = 3.
+grid.attrs['domain_length'] = grid.attrs['domain_length']*3
+
+grid.to_netcdf('./new_'+str(sys.argv[1]))
diff --git a/sims/preprocessing_scripts/inputs/icon_extpar_0010_R02B04_G_aquaplanet.nc b/sims/preprocessing_scripts/inputs/icon_extpar_0010_R02B04_G_aquaplanet.nc
new file mode 100644
index 0000000000000000000000000000000000000000..c6774c9dc41d738ead93452afdf209fe691b42de
Binary files /dev/null and b/sims/preprocessing_scripts/inputs/icon_extpar_0010_R02B04_G_aquaplanet.nc differ
diff --git a/sims/preprocessing_scripts/inputs/icon_grid_0010_R02B04_G.nc b/sims/preprocessing_scripts/inputs/icon_grid_0010_R02B04_G.nc
new file mode 100644
index 0000000000000000000000000000000000000000..7325b1d31f823c96675f965d0e4681b2c27d85b7
Binary files /dev/null and b/sims/preprocessing_scripts/inputs/icon_grid_0010_R02B04_G.nc differ
diff --git a/sims/preprocessing_scripts/map_file.latbc b/sims/preprocessing_scripts/map_file.latbc
new file mode 100644
index 0000000000000000000000000000000000000000..e8e34cd89f3f66d61760f92cbe10e665a4be2425
--- /dev/null
+++ b/sims/preprocessing_scripts/map_file.latbc
@@ -0,0 +1,21 @@
+# Dictionary for mapping between internal names and GRIB2 shortNames
+# needed by GRIB2 read procedures.
+#
+# internal name     GRIB2 shortName
+u                   u
+v                   v
+w                   w
+temp                temp
+pres                pres
+qv                  qv
+qc                  qc
+qi                  qi
+qr                  qr
+qs                  qs
+pres_sfc            LNPS
+#z_ifc               HHL
+vn                  VN
+GEOSP               GEOSP
+GEOP_ML             GEOP_ML
+theta_v             theta_v
+rho                 rho
diff --git a/sims/runscripts/LC1-LES-471x667km-lon25-lat40-300m-0006/exp.LC1-LES-471x667km-lon25-lat40-300m-0006.run b/sims/runscripts/LC1-LES-471x667km-lon25-lat40-300m-0006/exp.LC1-LES-471x667km-lon25-lat40-300m-0006.run
new file mode 100644
index 0000000000000000000000000000000000000000..33467ba7e16255f051830bd366375d3eed9e663f
--- /dev/null
+++ b/sims/runscripts/LC1-LES-471x667km-lon25-lat40-300m-0006/exp.LC1-LES-471x667km-lon25-lat40-300m-0006.run
@@ -0,0 +1,722 @@
+#! /bin/bash
+#=============================================================================
+
+# levante  cpu batch job parameters
+
+#SBATCH --account=bb1135
+#SBATCH --job-name=LC1300m0006
+#SBATCH --partition=compute
+#SBATCH --chdir=/home/b/b381185/simulations/runscripts/LC1-LES-471x667km-lon25-lat40-300m-0006
+#SBATCH --nodes=150
+#SBATCH --threads-per-core=2
+# the following is needed to work around a bug that otherwise leads to
+# a too low number of ranks when using compute,compute2 as queue
+#SBATCH --mem=0
+#SBATCH --output=/home/b/b381185/simulations/runscripts/LC1-LES-471x667km-lon25-lat40-300m-0006/LOG.exp.LC1-LES-471x667km-lon25-lat40-300m-0005.run.%j.o
+#SBATCH --error=/home/b/b381185/simulations/runscripts/LC1-LES-471x667km-lon25-lat40-300m-0006/LOG.exp.LC1-LES-471x667km-lon25-lat40-300m-0006.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=08:00:00
+
+#=============================================================================
+set -x
+ulimit -s unlimited
+#=============================================================================
+# OpenMP environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=$OMP_NUM_THREADS
+export OMP_SCHEDULE="guided"
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=500M
+#
+# MPI variables
+# -------------
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((128 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+#=============================================================================
+# load local setting, if existing
+# -------------------------------
+if [ -a ../setting ]
+then
+  echo "Load Setting"
+  . ../setting
+fi
+# environment variables for the experiment and the target system
+# --------------------------------------------------------------
+export KMP_AFFINITY="granularity=fine,scatter"
+export KMP_LIBRARY="turnaround"
+export OMPI_MCA_pml="ucx"
+export OMPI_MCA_btl=self
+export OMPI_MCA_osc="pt2pt"
+export UCX_IB_ADDR_TYPE=ib_global
+export OMPI_MCA_coll="^ml"
+export OMPI_MCA_coll_hcoll_enable="1"
+export HCOLL_ENABLE_MCAST_ALL="1"
+export HCOLL_MAIN_IB=mlx5_0:1
+export UCX_NET_DEVICES=mlx5_0:1
+export UCX_TLS=mm,knem,cma,dc_mlx5,dc_x,self
+export UCX_UNIFIED_MODE=y
+export HDF5_USE_FILE_LOCKING=FALSE
+export OMPI_MCA_io="romio321"
+export MALLOC_TRIM_THRESHOLD_="-1"
+export MKL_ENABLE_INSTRUCTIONS=AVX2
+export MKL_DEBUG_CPU_TYPE=5
+export UCX_HANDLE_ERRORS=bt
+# load profile
+# ------------
+if [[ -a  /etc/profile ]]
+then
+        . /etc/profile
+fi
+
+#=============================================================================
+# directories with absolute paths
+# -------------------------------
+basedir="/home/b/b381185/icon-on-jet"
+export basedir
+
+# how to start the icon model
+# ---------------------------
+mask="0xf,0xf0000,0xf00000000,0xf000000000000,0xf0000000000000000,0xf00000000000000000000,0xf000000000000000000000000,0xf0000000000000000000000000000,0xf0,0xf00000,0xf000000000,0xf0000000000000,0xf00000000000000000,0xf000000000000000000000,0xf0000000000000000000000000,0xf00000000000000000000000000000,0xf00,0xf000000,0xf0000000000,0xf00000000000000,0xf000000000000000000,0xf0000000000000000000000,0xf00000000000000000000000000,0xf000000000000000000000000000000,0xf000,0xf0000000,0xf00000000000,0xf000000000000000,0xf0000000000000000000,0xf00000000000000000000000,0xf000000000000000000000000000,0xf0000000000000000000000000000000"
+export START="srun -l --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --ntasks=$((no_of_nodes * mpi_procs_pernode)) --propagate=STACK,CORE --distribution=block:block --cpu-bind=v,mask_cpu=$mask "
+export MODEL="${basedir}/bin/icon"
+
+# --------------------------
+submit="sbatch"
+job_name="exp.LC1-LES-471x667km-lon25-lat40-300m-0006.run"
+
+#=============================================================================
+
+ulimit -s $((4 * 1024 * 1024))
+ulimit -c 0
+
+#-----------------------------------------------------------------------------
+export EXPNAME="LC1-LES-471x667km-lon25-lat40-300m-0006"
+
+# atmo namelist filename
+atmo_namelist=NAMELIST_${EXPNAME}_atm
+
+# directories definition
+RUNSCRIPTDIR=/home/b/b381185/simulations/runscripts/LC1-LES-471x667km-lon25-lat40-300m-0006/  # run script directory
+
+#-----------------------------------------------------------------------------
+# global timing
+start_date="2022-01-05T06:00:30Z"
+end_date="2022-01-05T12:00:33Z"
+
+start_date_out="2022-01-05T06:00:33Z"
+end_date_out="2022-01-05T12:00:33Z" 
+
+# restart intervals
+checkpoint_interval="PT3H"
+restart_interval="P1D"
+
+# output intervals_2d
+output_interval_2d="PT15M"
+file_interval_2d="PT15M"
+
+# output intervals_3d
+output_interval_3d="PT30M"
+file_interval_3d="PT30M"
+
+# regular  grid: nlat=96, nlon=192, npts=18432, dlat=1.875 deg, dlon=1.875 deg
+reg_lat_def_reg=-90.0,1.0,90.0
+reg_lon_def_reg=-180.0,1.0,180.0
+
+#-----------------------------------------------------------------------------
+# model parameters
+model_equations=3             # equation system
+#                     1=hydrost. atm. T
+#                     1=hydrost. atm. theta dp
+#                     3=non-hydrost. atm.,
+#                     0=shallow water model
+#                    -1=hydrost. ocean
+#-----------------------------------------------------------------------------
+
+# experiment directory, with plenty of space, create if new
+
+EXPDIR=/work/bb1135/icon_output/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ;  then
+  mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ;  then
+    mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+# Link experiment initial files 
+inputdir='/work/bb1135/LES_Simulations/initial_conditions/plane_nest_300m_r6x6_2mom_25_40_0004'
+# Grid
+ln -sf $inputdir/new_raggedOrthogonal_471x667_300_with_boundary_dom04.nc grid_DOM01.nc
+# Extpar
+ln -sf $inputdir/extpar_remapped_12_months.nc extpar_DOM01.nc
+# Initial file
+ln -sf $inputdir/icon-fg_ML_nest_20220105T060030Z.nc icon_latbcdata_DOM01_06.nc
+# Ozon
+ln -sf $inputdir/ape_O3_remapped.nc o3_icon_DOM01.nc
+
+# Model required files
+ln -sf $basedir/data/rrtmg_lw.nc              ./
+ln -sf $basedir/data/rrtmg_sw.nc              ./
+ln -sf $basedir/data/ECHAM6_CldOptProps.nc    ./
+ln -sf $basedir/data/dmin_wetgrowth_lookup.nc ./
+
+RADDIR=$basedir/externals/ecrad/data
+ln -sf ${RADDIR}/* .
+
+# var dict for LATBC
+ln -sf /work/bb1135/LES_Simulations/initial_conditions/map_file.latbc ./map_file.latbc
+#=============================================================================
+
+# create ICON namelist parameters
+
+cat > ${atmo_namelist} << EOF
+&parallel_nml
+ nproma         = 16  ! optimal setting 8 for CRAY; use 16 or 24 for IBM
+ p_test_run     = .false.
+ l_test_openmp  = .false.
+ l_log_checks   = .false.
+ num_io_procs   = 20   ! asynchronous output for values >= 1
+ itype_comm     = 1
+ !iorder_sendrecv = 3  ! best value for CRAY (slightly faster than option 1)
+ num_prefetch_proc = 1
+ num_restart_procs = 1
+/
+&grid_nml
+ dynamics_grid_filename  = 'grid_DOM01.nc'
+ dynamics_parent_grid_id = 0
+ lredgrid_phys           = .false.
+ is_plane_cylinder       = .TRUE.
+ l_limited_area          = .true.
+ corio_lat               = 45.0
+/
+&initicon_nml
+ init_mode   = 7           ! operation mode 2: IFS
+ dwdfg_filename         = 'icon_latbcdata_DOM01_06.nc'
+ !nlevsoil_in            =  8
+ lread_ana              = .FALSE. 
+ ltile_coldstart = .TRUE.
+ !zpbl1       = 500. 
+ !zpbl2       = 1000.
+ !l_sst_in    = .true. 
+/
+&limarea_nml
+ itype_latbc = 1            ! transient
+ dtime_latbc = 3600.0
+ latbc_path = '/work/bb1135/LES_Simulations/initial_conditions/plane_nest_300m_r6x6_2mom_25_40_0004'
+ latbc_filename = "icon-fg_ML_nest_<y>-<m>-<d>T<h>.nc"
+ latbc_varnames_map_file     =            'map_file.latbc'
+ init_latbc_from_fg          =                     .FALSE.        ! .TRUE.: take lbc for initial time from first guess
+/
+&run_nml
+ num_lev        = 150          
+ dtime          = 3  ! 360 for R2B5 180 for R2B7    
+ ldynamics      = .TRUE.
+ ltransport     = .true.
+ iforcing       = 3            ! NWP forcing
+ ltestcase      = .false.      ! false: run with real data
+ msg_level      = 20            ! print maximum wind speeds every 5 time steps
+ ltimer         = .TRUE.      ! set .TRUE. for timer output
+ timers_level   = 10           ! can be increased up to 10 for detailed timer output
+ output         = "nml"
+ activate_sync_timers = .True.
+/
+&nwp_phy_nml
+ inwp_gscp       = 4
+ inwp_convection = 0
+ inwp_radiation  = 4  ! ecrad
+ inwp_cldcover   = 5
+ inwp_turb       = 5
+ inwp_satad      = 1
+ inwp_sso        = 0
+ inwp_gwd        = 0
+ inwp_surface    = 1
+ icapdcycl       = 3
+ latm_above_top  = .false.  ! the second entry refers to the nested domain (if present)
+ efdt_min_raylfric = 7200.
+ ldetrain_conv_prec = .false.
+ itype_z0         = 2
+ icpl_aero_conv   = 0 
+ icpl_aero_gscp   = 0
+ !icalc_reff       = 101     ! getting cloud-ice/water effective radi from rrtm
+ ! resolution-dependent settings - please choose the appropriate one
+ dt_rad    = 600 
+ dt_conv   = 300 
+ dt_sso    = 300 
+ dt_gwd    = 300. 
+ lcloudradonly    = .true. 
+ !ldiag_ddt_temp_dyn2          = .true.
+/
+&les_nml
+!! same values as in Matthias Brueck NA
+ smag_constant     = 0.3 ! this is default value
+ isrfc_type        = 1  !1=TERRA,2=Fixed flux, 5=fixed SST, 3=fixed bflux
+ ldiag_les_out     = .false.
+ les_metric        = .true.
+/
+&turbdiff_nml
+ tkhmin  = 0.75  
+ tkmmin  = 0.75             
+ pat_len = 100. !750.
+ c_diff  = 0.2
+ rat_sea = 10  
+ ltkesso = .true.
+ frcsmot = 0.2      
+ imode_frcsmot = 2  
+ itype_sher = 3    
+ ltkeshs    = .true.
+ a_hshr     = 2.0     
+/
+&lnd_nml
+ ntiles         = 3      !!! 1 for assimilation cycle and forecast
+ nlev_snow      = 3      !!! 1 for assimilation cycle and forecast
+ lmulti_snow    = .true. !!! .false. for assimilation cycle and forecast
+ itype_heatcond = 2
+ idiag_snowfrac = 2
+ lsnowtile      = .false.  !! later on .true. if GRIB encoding issues are solved
+ lseaice        = .true. !!Sophia Schäfer, 23/03/2017: reads in sea ice (e.g. zero ice), instead of setting by temperature
+ llake          = .false.
+ itype_lndtbl   = 3  ! minimizes moist/cold bias in lower tropical troposphere
+ itype_root     = 2  			        
+/
+&radiation_nml
+ irad_o3       = 4 
+ irad_aero     = 0
+ irad_cfc11    = 0
+ irad_cfc12    = 0
+ albedo_type   = 2 ! Modis albedo
+ vmr_co2       = 348.0e-6 ! values representative for 2012
+ vmr_ch4       = 1650.0e-09
+ vmr_n2o       = 396.0e-09
+ vmr_o2        = 0.20946
+ izenith       = 3
+ vmr_cfc11     = 0
+ vmr_cfc12     = 0
+ llw_cloud_scat = .true.
+/
+&nonhydrostatic_nml
+ iadv_rhotheta  = 2
+ ivctype        = 2
+ itime_scheme   = 4
+ exner_expol    = 0.333
+ vwind_offctr   = 0.2
+ damp_height    = 22500.
+ rayleigh_coeff = 0.10   ! R3B7: 1.0 for forecasts, 5.0 in assimilation cycle; 0.5/2.5 for R2B6 (i.e. ensemble) runs
+ lhdiff_rcf     = .true.
+ divdamp_order  = 24    ! setting for forecast runs; use '2' for assimilation cycle
+ divdamp_type   = 32    ! ** new setting for assimilation and forecast runs **
+ divdamp_fac    = 0.004 ! use 0.032 in conjunction with divdamp_order = 2 in assimilation cycle
+ l_open_ubc     = .false.
+ igradp_method  = 3
+ l_zdiffu_t     = .true.
+ thslp_zdiffu   = 0.02
+ thhgtd_zdiffu  = 125.
+ htop_moist_proc= 22500.
+ hbot_qvsubstep = 22500. ! use 22500. with R2B6
+/
+&sleve_nml
+ min_lay_thckn   = 20.
+ max_lay_thckn   = 400.   ! maximum layer thickness below htop_thcknlimit
+ htop_thcknlimit = 14000. ! this implies that the upcoming COSMO-EU nest will have 60 levels
+ top_height      = 30000.
+ stretch_fac     = 0.9
+ decay_scale_1   = 4000.
+ decay_scale_2   = 2500.
+ decay_exp       = 1.2
+ flat_height     = 16000.
+/
+&dynamics_nml
+ iequations     = 3
+ idiv_method    = 1
+ divavg_cntrwgt = 0.50
+ !lcoriolis      = .TRUE.
+/
+&transport_nml
+ ctracer_list  = '12345'
+ ivadv_tracer  = 3,3,3,3,3
+ itype_hlimit  = 3,4,4,4,4
+ ihadv_tracer  = 52,2,2,2,2
+ iadv_tke      = 0
+/
+&diffusion_nml
+ hdiff_order      = 5
+ itype_vn_diffu   = 1
+ itype_t_diffu    = 2
+ hdiff_efdt_ratio = 24.0
+ hdiff_smag_fac   = 0.025
+ lhdiff_vn        = .TRUE.
+ lhdiff_temp      = .TRUE.
+/
+&interpol_nml
+ !nudge_zone_width  = 0.0  ! deactivating nudging as needed for mass conservation for this setup 
+ !nudge_efold_width = 0.0
+ !nudge_max_coeff   = 0.00000
+ lsq_high_ord      = 3
+ l_intp_c2l        = .true.
+ l_mono_c2l        = .true.
+ support_baryctr_intp = .false.
+ rbf_vec_scale_c              = 0.01
+ rbf_vec_scale_v              = 0.025
+ rbf_vec_scale_e              = 0.05
+/
+&extpar_nml
+ itopo          = 1
+ extpar_filename             = 'extpar_DOM01.nc'
+ n_iter_smooth_topo = 1         
+ heightdiff_threshold = 3000.
+/
+&io_nml
+ itype_pres_msl = 5  ! New extrapolation method to circumvent Ninjo problem with surface inversions
+ itype_rh       = 1  ! RH w.r.t. water
+ restart_file_type = 5       ! 4: netcdf2, 5: netcdf4
+ !lkeep_in_sync                = .true.  ! was false in 2.5km NWP
+/
+&nh_pzlev_nml
+ nplev             = 18     ! number of p level output (attention: levels in [Pa] and top-down)
+ nzlev             = 20     ! number of z level output (attention: levels in [m] and top-down)
+ plevels           = 5000,7000,10000,15000,20000,25000,30000,40000,50000,60000,70000,
+                     80000,85000,90000,92500,95000,97500,100000
+ zlevels           = 25000,20000,18000,16000,14000,12000,10000,8000,6000,5000,4000,3000,2000,
+                     1000,800,600,400,200,100,10
+/
+&output_nml
+ filetype         =  5                        ! output format: 2=GRIB2, 4=NETCDFv2
+ output_start     = "${start_date_out}"
+ output_end       = "${end_date_out}"
+ output_interval  = "${output_interval_3d}"
+ file_interval    = "${file_interval_3d}"
+ include_last                 =  .TRUE.
+ output_filename              = 'icon-atm3d' ! file name base
+ filename_format  = "<output_filename>_ML_<datetime2>"
+ ml_varlist                   = 'u','v','pv','temp','qi','qc','qv','pres','rho','w','z_ifc','tot_qv_dia','O3'
+ remap                        = 0
+ !reg_lon_def                  = ${reg_lon_def_reg}
+ !reg_lat_def                  = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype                     =  5                      ! output format: 2=GRIB2, 4=NETCDFv2
+ dom                          =  1                      ! write all domains
+ output_start     = "${start_date_out}"
+ output_end       = "${end_date_out}"
+ output_interval  = "${output_interval_3d}"
+ file_interval    = "${file_interval_3d}"
+ include_last                 =  .TRUE.
+ output_filename              = 'icon-cld3d' ! file name base
+ filename_format  = "<output_filename>_ML_<datetime2>"
+ ml_varlist                   = 'clc','tot_qi_dia','tot_qc_dia','acdnc','reff_qc_ecrad','reff_qi_ecrad','pres_ifc','rho_ic','tsfctrad'
+ remap                        = 0
+ !reg_lon_def                  = ${reg_lon_def_reg}
+ !reg_lat_def                  = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype                     =  5                      ! output format: 2=GRIB2, 4=NETCDFv2
+ dom                          =  1                      ! write all domains
+ output_start     = "${start_date_out}"
+ output_end       = "${end_date_out}"
+ output_interval  = "${output_interval_3d}"
+ file_interval    = "${file_interval_3d}"
+ include_last                 =  .TRUE.
+ output_filename              = 'icon-ddt_temp' ! file name base
+ filename_format  = "<output_filename>_ML_<datetime2>"
+ ml_varlist                   = 'ddt_temp_radswnw','ddt_temp_radlwnw','ddt_temp_radswcs','ddt_temp_radlwcs','ddt_temp_turb','ddt_temp_gscp','ddt_temp_mphy'
+ remap                        = 0
+ !reg_lon_def                  = ${reg_lon_def_reg}
+ !reg_lat_def                  = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype                     =  5                      ! output format: 2=GRIB2, 4=NETCDFv2
+ dom                          =  1                      ! write all domains
+ output_start     = "${start_date_out}"
+ output_end       = "${end_date_out}"
+ output_interval  = "${output_interval_3d}"
+ file_interval    = "${file_interval_3d}"
+ include_last                 =  .TRUE.
+ output_filename              = 'icon-rad3d' ! file name base
+ filename_format  = "<output_filename>_ML_<datetime2>"
+ ml_varlist                   = 'lwflxall', 'swflxall', 'swflxclr', 'lwflxclr'
+ remap                        = 0
+ !reg_lon_def                  = ${reg_lon_def_reg}
+ !reg_lat_def                  = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype                     =  5                      ! output format: 2=GRIB2, 4=NETCDFv2
+ dom                          =  1                      ! write all domains
+ output_start     = "${start_date_out}"
+ output_end       = "${end_date_out}"
+ output_interval  = "${output_interval_3d}"
+ file_interval    = "${file_interval_3d}"
+ include_last                 =  .TRUE.
+ output_filename              = 'icon-radbz' ! file name base
+ filename_format  = "<output_filename>_ML_<datetime2>"
+ ml_varlist                   = 'temphl_bz', 'preshl_bz', 'tempfl_bz', 'presfl_bz','tsfc_bz','qc_bz','qi_bz','qv_bz','clc_bz','cosz_bz'
+ remap                        = 0
+ !reg_lon_def                  = ${reg_lon_def_reg}
+ !reg_lat_def                  = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype                     =  5              ! output format: 2=GRIB2, 4=NETCDFv2
+ dom                          =  1                       ! write all domains
+ output_start     = "${start_date_out}"
+ output_end       = "${end_date_out}"
+ output_interval  = "${output_interval_2d}"
+ file_interval    = "${file_interval_2d}"
+ include_last                 = .TRUE.
+ output_filename              = 'icon-atm2d'   ! file name base
+ filename_format  = "<output_filename>_ML_<datetime2>"
+ ml_varlist                   = 'pres_sfc','u_10m','v_10m','t_g','t_2m','qv_2m','td_2m','rh_2m','clct','clch','clcm','clcl','tqv_dia','tqc_dia','tqi_dia','shfl_s','lhfl_s','cape_ml','cin_ml','rain_gsp_rate','tot_prec'
+ remap                        = 0
+ !reg_lon_def                  = ${reg_lon_def_reg}
+ !reg_lat_def                  = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype                     =  5              ! output format: 2=GRIB2, 4=NETCDFv2
+ dom                          =  1                       ! write all domains
+ output_start     = "${start_date_out}"
+ output_end       = "${end_date_out}"
+ output_interval  = "${output_interval_2d}"
+ file_interval    = "${file_interval_2d}"
+ include_last                 = .TRUE.
+ output_filename              = 'icon-rad2d'   ! file name base
+ filename_format  = "<output_filename>_ML_<datetime2>"
+ ml_varlist                   = 'sob_t','thb_t','sobclr_t','thbclr_t','sob_s','thb_s','thbclr_s','sobclr_s'
+ remap                        = 0
+ !reg_lon_def                  = ${reg_lon_def_reg}
+ !reg_lat_def                  = ${reg_lat_def_reg}
+/
+&output_nml
+ output_filename  = "icon-extra"
+ filename_format  = "<output_filename>_<levtype_l>"
+ filetype         = 5
+ remap            = 0
+ output_grid      = .TRUE.
+ output_start     = "${start_date_out}"          ! output_start = output_end
+ output_end       = "${start_date_out}"          ! --> write once only irrespective of
+ output_interval  = "PT15M"  !     the output interval and
+ file_interval    = "${file_interval_2d}"    !     the file interval
+ ml_varlist       = 'z_mc'
+/
+EOF
+
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration. 
+#-----------------------------------------------------------------------------
+
+final_status_file=${EXPDIR}/${job_name}.final_status #!NICOLE
+rm -f ${final_status_file}
+#-----------------------------------------------------------------------------
+
+# set up the model lists if they do not exist
+# this works for single model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+#  minrank_list=(        0           )
+#  maxrank_list=(     65535          )
+#  incrank_list=(        1           )
+  minrank_list[0]=0
+  maxrank_list[0]=65535
+  incrank_list[0]=1
+  if [ x$atmo_namelist != x ]; then
+    # this is the atmo model
+    namelist_list[0]="$atmo_namelist"
+    modelname_list[0]="atmo"
+    modeltype_list[0]=1
+    run_atmo="true"
+  else
+    check_error 1 "No namelist is defined"
+  fi
+fi
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+  restart=.true.
+  #  do not delete switch-file, to enable restart after unintended abort
+  #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+  if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+    sleep 10;
+  fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+  run_atmo="true"
+fi
+#-----------------------------------------------------------------------------
+# get restart files
+
+if  [ x$restart_atmo_from != "x" ] ; then
+  rm -f restart_atm_DOM01.nc
+#  ln -s ${basedir}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+  cp ${basedir}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+  ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+  restart=".true."
+fi
+if  [ x$restart_ocean_from != "x" ] ; then
+  rm -f restart_oce.nc
+#  ln -s ${basedir}/experiments/${restart_from_folder}/${restart_ocean_from} ${EXPDIR}/restart_oce.nc
+  cp ${basedir}/experiments/${restart_from_folder}/${restart_ocean_from} cp_restart_oce_DOM01.nc
+  ln -s cp_restart_oce_DOM01.nc restart_oce_DOM01.nc
+  restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+
+read_restart_namelists=${read_restart_namelists:=".true."}
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart            = $restart
+/
+&master_time_control_nml
+ experimentStartDate  = "$start_date"
+ restartTimeIntval    = "$restart_interval"
+ checkpointTimeIntval = "$checkpoint_interval"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+  calendar='proleptic gregorian'
+  calendar_type=1
+else
+  calendar=$calendar
+  calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart            = $restart
+ read_restart_namelists = $read_restart_namelists
+/
+&master_time_control_nml
+ calendar             = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval    = "$restart_interval"
+ experimentStartDate  = "$start_date"
+ experimentStopDate   = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+  model_namelist_filename="$1"
+  model_name=$2
+  model_type=$3
+  model_min_rank=$4
+  model_max_rank=$5
+  model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+  model_name="$model_name"
+  model_namelist_filename="$model_namelist_filename"
+  model_type=$model_type
+  model_min_rank=$model_min_rank
+  model_max_rank=$model_max_rank
+  model_inc_rank=$model_inc_rank
+/
+EOF
+
+}
+#-----------------------------------------------------------------------------
+
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+  add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+  j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+
+# start experiment
+
+rm -f finish.status
+date
+${START} ${MODEL}
+date
+#
+if [ -r finish.status ] ; then
+  check_final_status 0 "${START} ${MODEL}"
+else
+  check_final_status -1 "${START} ${MODEL}"
+fi
+#
+
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+#   Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ]; then
+  echo "restart next experiment..."
+  this_script="${RUNSCRIPTDIR}/exp.${EXPNAME}.run"
+  echo 'this_script: ' "$this_script"
+  # note that if ${restartSemaphoreFilename} does not exist yet, then touch will create it
+  touch ${restartSemaphoreFilename}
+  cd ${RUNSCRIPTDIR}
+  sbatch exp.${EXPNAME}.run
+else
+  [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+
+cd ${RUNSCRIPTDIR}
+
+#-----------------------------------------------------------------------------
diff --git a/sims/runscripts/LC1-LES-471x667km-lon30-lat53-300m-0005/exp.LC1-LES-471x667km-lon30-lat53-300m-0005.run b/sims/runscripts/LC1-LES-471x667km-lon30-lat53-300m-0005/exp.LC1-LES-471x667km-lon30-lat53-300m-0005.run
new file mode 100644
index 0000000000000000000000000000000000000000..baeb2e274f2e930fcf57b62238f1f0679b059b92
--- /dev/null
+++ b/sims/runscripts/LC1-LES-471x667km-lon30-lat53-300m-0005/exp.LC1-LES-471x667km-lon30-lat53-300m-0005.run
@@ -0,0 +1,722 @@
+#! /bin/bash
+#=============================================================================
+
+# levante  cpu batch job parameters
+
+#SBATCH --account=bb1135
+#SBATCH --job-name=LC1300m0005
+#SBATCH --partition=compute
+#SBATCH --chdir=/home/b/b381185/simulations/runscripts/LC1-LES-471x667km-lon30-lat53-300m-0005
+#SBATCH --nodes=150
+#SBATCH --threads-per-core=2
+# the following is needed to work around a bug that otherwise leads to
+# a too low number of ranks when using compute,compute2 as queue
+#SBATCH --mem=0
+#SBATCH --output=/home/b/b381185/simulations/runscripts/LC1-LES-471x667km-lon30-lat53-300m-0005/LOG.exp.LC1-LES-471x667km-lon30-lat53-300m-0005.run.%j.o
+#SBATCH --error=/home/b/b381185/simulations/runscripts/LC1-LES-471x667km-lon30-lat53-300m-0005/LOG.exp.LC1-LES-471x667km-lon30-lat53-300m-0005.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=08:00:00
+
+#=============================================================================
+set -x
+ulimit -s unlimited
+#=============================================================================
+# OpenMP environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=$OMP_NUM_THREADS
+export OMP_SCHEDULE="guided"
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=500M
+#
+# MPI variables
+# -------------
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((128 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+#=============================================================================
+# load local setting, if existing
+# -------------------------------
+if [ -a ../setting ]
+then
+  echo "Load Setting"
+  . ../setting
+fi
+# environment variables for the experiment and the target system
+# --------------------------------------------------------------
+export KMP_AFFINITY="granularity=fine,scatter"
+export KMP_LIBRARY="turnaround"
+export OMPI_MCA_pml="ucx"
+export OMPI_MCA_btl=self
+export OMPI_MCA_osc="pt2pt"
+export UCX_IB_ADDR_TYPE=ib_global
+export OMPI_MCA_coll="^ml"
+export OMPI_MCA_coll_hcoll_enable="1"
+export HCOLL_ENABLE_MCAST_ALL="1"
+export HCOLL_MAIN_IB=mlx5_0:1
+export UCX_NET_DEVICES=mlx5_0:1
+export UCX_TLS=mm,knem,cma,dc_mlx5,dc_x,self
+export UCX_UNIFIED_MODE=y
+export HDF5_USE_FILE_LOCKING=FALSE
+export OMPI_MCA_io="romio321"
+export MALLOC_TRIM_THRESHOLD_="-1"
+export MKL_ENABLE_INSTRUCTIONS=AVX2
+export MKL_DEBUG_CPU_TYPE=5
+export UCX_HANDLE_ERRORS=bt
+# load profile
+# ------------
+if [[ -a  /etc/profile ]]
+then
+        . /etc/profile
+fi
+
+#=============================================================================
+# directories with absolute paths
+# -------------------------------
+basedir="/home/b/b381185/icon-on-jet"
+export basedir
+
+# how to start the icon model
+# ---------------------------
+mask="0xf,0xf0000,0xf00000000,0xf000000000000,0xf0000000000000000,0xf00000000000000000000,0xf000000000000000000000000,0xf0000000000000000000000000000,0xf0,0xf00000,0xf000000000,0xf0000000000000,0xf00000000000000000,0xf000000000000000000000,0xf0000000000000000000000000,0xf00000000000000000000000000000,0xf00,0xf000000,0xf0000000000,0xf00000000000000,0xf000000000000000000,0xf0000000000000000000000,0xf00000000000000000000000000,0xf000000000000000000000000000000,0xf000,0xf0000000,0xf00000000000,0xf000000000000000,0xf0000000000000000000,0xf00000000000000000000000,0xf000000000000000000000000000,0xf0000000000000000000000000000000"
+export START="srun -l --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --ntasks=$((no_of_nodes * mpi_procs_pernode)) --propagate=STACK,CORE --distribution=block:block --cpu-bind=v,mask_cpu=$mask "
+export MODEL="${basedir}/bin/icon"
+
+# --------------------------
+submit="sbatch"
+job_name="exp.LC1-LES-471x667km-lon30-lat53-300m-0005.run"
+
+#=============================================================================
+
+ulimit -s $((4 * 1024 * 1024))
+ulimit -c 0
+
+#-----------------------------------------------------------------------------
+export EXPNAME="LC1-LES-471x667km-lon30-lat53-300m-0005"
+
+# atmo namelist filename
+atmo_namelist=NAMELIST_${EXPNAME}_atm
+
+# directories definition
+RUNSCRIPTDIR=/home/b/b381185/simulations/runscripts/LC1-LES-471x667km-lon30-lat53-300m-0005/  # run script directory
+
+#-----------------------------------------------------------------------------
+# global timing
+start_date="2022-01-05T06:00:30Z"
+end_date="2022-01-05T12:00:33Z"
+
+start_date_out="2022-01-05T06:00:33Z"
+end_date_out="2022-01-05T12:00:33Z" 
+
+# restart intervals
+checkpoint_interval="PT3H"
+restart_interval="P1D"
+
+# output intervals_2d
+output_interval_2d="PT15M"
+file_interval_2d="PT15M"
+
+# output intervals_3d
+output_interval_3d="PT30M"
+file_interval_3d="PT30M"
+
+# regular  grid: nlat=96, nlon=192, npts=18432, dlat=1.875 deg, dlon=1.875 deg
+reg_lat_def_reg=-90.0,1.0,90.0
+reg_lon_def_reg=-180.0,1.0,180.0
+
+#-----------------------------------------------------------------------------
+# model parameters
+model_equations=3             # equation system
+#                     1=hydrost. atm. T
+#                     1=hydrost. atm. theta dp
+#                     3=non-hydrost. atm.,
+#                     0=shallow water model
+#                    -1=hydrost. ocean
+#-----------------------------------------------------------------------------
+
+# experiment directory, with plenty of space, create if new
+
+EXPDIR=/work/bb1135/icon_output/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ;  then
+  mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ;  then
+    mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+# Link experiment initial files 
+inputdir='/work/bb1135/LES_Simulations/initial_conditions/plane_nest_300m_r6x6_2mom_30_53_0003'
+# Grid
+ln -sf $inputdir/new_raggedOrthogonal_471x667_300_with_boundary_dom03.nc grid_DOM01.nc
+# Extpar
+ln -sf $inputdir/extpar_remapped_12_months.nc extpar_DOM01.nc
+# Initial file
+ln -sf $inputdir/icon-fg_ML_nest_20220105T060030Z.nc icon_latbcdata_DOM01_06.nc
+# Ozon
+ln -sf $inputdir/ape_O3_remapped.nc o3_icon_DOM01.nc
+
+# Model required files
+ln -sf $basedir/data/rrtmg_lw.nc              ./
+ln -sf $basedir/data/rrtmg_sw.nc              ./
+ln -sf $basedir/data/ECHAM6_CldOptProps.nc    ./
+ln -sf $basedir/data/dmin_wetgrowth_lookup.nc ./
+
+RADDIR=$basedir/externals/ecrad/data
+ln -sf ${RADDIR}/* .
+
+# var dict for LATBC
+ln -sf /work/bb1135/LES_Simulations/initial_conditions/map_file.latbc ./map_file.latbc
+#=============================================================================
+
+# create ICON namelist parameters
+
+cat > ${atmo_namelist} << EOF
+&parallel_nml
+ nproma         = 16  ! optimal setting 8 for CRAY; use 16 or 24 for IBM
+ p_test_run     = .false.
+ l_test_openmp  = .false.
+ l_log_checks   = .false.
+ num_io_procs   = 20   ! asynchronous output for values >= 1
+ itype_comm     = 1
+ !iorder_sendrecv = 3  ! best value for CRAY (slightly faster than option 1)
+ num_prefetch_proc = 1
+ num_restart_procs = 1
+/
+&grid_nml
+ dynamics_grid_filename  = 'grid_DOM01.nc'
+ dynamics_parent_grid_id = 0
+ lredgrid_phys           = .false.
+ is_plane_cylinder       = .TRUE.
+ l_limited_area          = .true.
+ corio_lat               = 45.0
+/
+&initicon_nml
+ init_mode   = 7           ! operation mode 2: IFS
+ dwdfg_filename         = 'icon_latbcdata_DOM01_06.nc'
+ !nlevsoil_in            =  8
+ lread_ana              = .FALSE. 
+ ltile_coldstart = .TRUE.
+ !zpbl1       = 500. 
+ !zpbl2       = 1000.
+ !l_sst_in    = .true. 
+/
+&limarea_nml
+ itype_latbc = 1            ! transient
+ dtime_latbc = 3600.0
+ latbc_path = '/work/bb1135/LES_Simulations/initial_conditions/plane_nest_300m_r6x6_2mom_30_53_0003'
+ latbc_filename = "icon-fg_ML_nest_<y>-<m>-<d>T<h>.nc"
+ latbc_varnames_map_file     =            'map_file.latbc'
+ init_latbc_from_fg          =                     .FALSE.        ! .TRUE.: take lbc for initial time from first guess
+/
+&run_nml
+ num_lev        = 150          
+ dtime          = 3  ! 360 for R2B5 180 for R2B7    
+ ldynamics      = .TRUE.
+ ltransport     = .true.
+ iforcing       = 3            ! NWP forcing
+ ltestcase      = .false.      ! false: run with real data
+ msg_level      = 20            ! print maximum wind speeds every 5 time steps
+ ltimer         = .TRUE.      ! set .TRUE. for timer output
+ timers_level   = 10           ! can be increased up to 10 for detailed timer output
+ output         = "nml"
+ activate_sync_timers = .True.
+/
+&nwp_phy_nml
+ inwp_gscp       = 4
+ inwp_convection = 0
+ inwp_radiation  = 4  ! ecrad
+ inwp_cldcover   = 5
+ inwp_turb       = 5
+ inwp_satad      = 1
+ inwp_sso        = 0
+ inwp_gwd        = 0
+ inwp_surface    = 1
+ icapdcycl       = 3
+ latm_above_top  = .false.  ! the second entry refers to the nested domain (if present)
+ efdt_min_raylfric = 7200.
+ ldetrain_conv_prec = .false.
+ itype_z0         = 2
+ icpl_aero_conv   = 0 
+ icpl_aero_gscp   = 0
+ !icalc_reff       = 101     ! getting cloud-ice/water effective radi from rrtm
+ ! resolution-dependent settings - please choose the appropriate one
+ dt_rad    = 600 
+ dt_conv   = 300 
+ dt_sso    = 300 
+ dt_gwd    = 300. 
+ lcloudradonly    = .true. 
+ !ldiag_ddt_temp_dyn2          = .true.
+/
+&les_nml
+!! same values as in Matthias Brueck NA
+ smag_constant     = 0.3 ! this is default value
+ isrfc_type        = 1  !1=TERRA,2=Fixed flux, 5=fixed SST, 3=fixed bflux
+ ldiag_les_out     = .false.
+ les_metric        = .true.
+/
+&turbdiff_nml
+ tkhmin  = 0.75  
+ tkmmin  = 0.75             
+ pat_len = 100. !750.
+ c_diff  = 0.2
+ rat_sea = 10  
+ ltkesso = .true.
+ frcsmot = 0.2      
+ imode_frcsmot = 2  
+ itype_sher = 3    
+ ltkeshs    = .true.
+ a_hshr     = 2.0     
+/
+&lnd_nml
+ ntiles         = 3      !!! 1 for assimilation cycle and forecast
+ nlev_snow      = 3      !!! 1 for assimilation cycle and forecast
+ lmulti_snow    = .true. !!! .false. for assimilation cycle and forecast
+ itype_heatcond = 2
+ idiag_snowfrac = 2
+ lsnowtile      = .false.  !! later on .true. if GRIB encoding issues are solved
+ lseaice        = .true. !!Sophia Schäfer, 23/03/2017: reads in sea ice (e.g. zero ice), instead of setting by temperature
+ llake          = .false.
+ itype_lndtbl   = 3  ! minimizes moist/cold bias in lower tropical troposphere
+ itype_root     = 2  			        
+/
+&radiation_nml
+ irad_o3       = 4 
+ irad_aero     = 0
+ irad_cfc11    = 0
+ irad_cfc12    = 0
+ albedo_type   = 2 ! Modis albedo
+ vmr_co2       = 348.0e-6 ! values representative for 2012
+ vmr_ch4       = 1650.0e-09
+ vmr_n2o       = 396.0e-09
+ vmr_o2        = 0.20946
+ izenith       = 3
+ vmr_cfc11     = 0
+ vmr_cfc12     = 0
+ llw_cloud_scat = .true.
+/
+&nonhydrostatic_nml
+ iadv_rhotheta  = 2
+ ivctype        = 2
+ itime_scheme   = 4
+ exner_expol    = 0.333
+ vwind_offctr   = 0.2
+ damp_height    = 22500.
+ rayleigh_coeff = 0.10   ! R3B7: 1.0 for forecasts, 5.0 in assimilation cycle; 0.5/2.5 for R2B6 (i.e. ensemble) runs
+ lhdiff_rcf     = .true.
+ divdamp_order  = 24    ! setting for forecast runs; use '2' for assimilation cycle
+ divdamp_type   = 32    ! ** new setting for assimilation and forecast runs **
+ divdamp_fac    = 0.004 ! use 0.032 in conjunction with divdamp_order = 2 in assimilation cycle
+ l_open_ubc     = .false.
+ igradp_method  = 3
+ l_zdiffu_t     = .true.
+ thslp_zdiffu   = 0.02
+ thhgtd_zdiffu  = 125.
+ htop_moist_proc= 22500.
+ hbot_qvsubstep = 22500. ! use 22500. with R2B6
+/
+&sleve_nml
+ min_lay_thckn   = 20.
+ max_lay_thckn   = 400.   ! maximum layer thickness below htop_thcknlimit
+ htop_thcknlimit = 14000. ! this implies that the upcoming COSMO-EU nest will have 60 levels
+ top_height      = 30000.
+ stretch_fac     = 0.9
+ decay_scale_1   = 4000.
+ decay_scale_2   = 2500.
+ decay_exp       = 1.2
+ flat_height     = 16000.
+/
+&dynamics_nml
+ iequations     = 3
+ idiv_method    = 1
+ divavg_cntrwgt = 0.50
+ !lcoriolis      = .TRUE.
+/
+&transport_nml
+ ctracer_list  = '12345'
+ ivadv_tracer  = 3,3,3,3,3
+ itype_hlimit  = 3,4,4,4,4
+ ihadv_tracer  = 52,2,2,2,2
+ iadv_tke      = 0
+/
+&diffusion_nml
+ hdiff_order      = 5
+ itype_vn_diffu   = 1
+ itype_t_diffu    = 2
+ hdiff_efdt_ratio = 24.0
+ hdiff_smag_fac   = 0.025
+ lhdiff_vn        = .TRUE.
+ lhdiff_temp      = .TRUE.
+/
+&interpol_nml
+ !nudge_zone_width  = 0.0  ! deactivating nudging as needed for mass conservation for this setup 
+ !nudge_efold_width = 0.0
+ !nudge_max_coeff   = 0.00000
+ lsq_high_ord      = 3
+ l_intp_c2l        = .true.
+ l_mono_c2l        = .true.
+ support_baryctr_intp = .false.
+ rbf_vec_scale_c              = 0.01
+ rbf_vec_scale_v              = 0.025
+ rbf_vec_scale_e              = 0.05
+/
+&extpar_nml
+ itopo          = 1
+ extpar_filename             = 'extpar_DOM01.nc'
+ n_iter_smooth_topo = 1         
+ heightdiff_threshold = 3000.
+/
+&io_nml
+ itype_pres_msl = 5  ! New extrapolation method to circumvent Ninjo problem with surface inversions
+ itype_rh       = 1  ! RH w.r.t. water
+ restart_file_type = 5       ! 4: netcdf2, 5: netcdf4
+ !lkeep_in_sync                = .true.  ! was false in 2.5km NWP
+/
+&nh_pzlev_nml
+ nplev             = 18     ! number of p level output (attention: levels in [Pa] and top-down)
+ nzlev             = 20     ! number of z level output (attention: levels in [m] and top-down)
+ plevels           = 5000,7000,10000,15000,20000,25000,30000,40000,50000,60000,70000,
+                     80000,85000,90000,92500,95000,97500,100000
+ zlevels           = 25000,20000,18000,16000,14000,12000,10000,8000,6000,5000,4000,3000,2000,
+                     1000,800,600,400,200,100,10
+/
+&output_nml
+ filetype         =  5                        ! output format: 2=GRIB2, 4=NETCDFv2
+ output_start     = "${start_date_out}"
+ output_end       = "${end_date_out}"
+ output_interval  = "${output_interval_3d}"
+ file_interval    = "${file_interval_3d}"
+ include_last                 =  .TRUE.
+ output_filename              = 'icon-atm3d' ! file name base
+ filename_format  = "<output_filename>_ML_<datetime2>"
+ ml_varlist                   = 'u','v','pv','temp','qi','qc','qv','pres','rho','w','z_ifc','tot_qv_dia','O3'
+ remap                        = 0
+ !reg_lon_def                  = ${reg_lon_def_reg}
+ !reg_lat_def                  = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype                     =  5                      ! output format: 2=GRIB2, 4=NETCDFv2
+ dom                          =  1                      ! write all domains
+ output_start     = "${start_date_out}"
+ output_end       = "${end_date_out}"
+ output_interval  = "${output_interval_3d}"
+ file_interval    = "${file_interval_3d}"
+ include_last                 =  .TRUE.
+ output_filename              = 'icon-cld3d' ! file name base
+ filename_format  = "<output_filename>_ML_<datetime2>"
+ ml_varlist                   = 'clc','tot_qi_dia','tot_qc_dia','acdnc','reff_qc_ecrad','reff_qi_ecrad','pres_ifc','rho_ic','tsfctrad'
+ remap                        = 0
+ !reg_lon_def                  = ${reg_lon_def_reg}
+ !reg_lat_def                  = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype                     =  5                      ! output format: 2=GRIB2, 4=NETCDFv2
+ dom                          =  1                      ! write all domains
+ output_start     = "${start_date_out}"
+ output_end       = "${end_date_out}"
+ output_interval  = "${output_interval_3d}"
+ file_interval    = "${file_interval_3d}"
+ include_last                 =  .TRUE.
+ output_filename              = 'icon-ddt_temp' ! file name base
+ filename_format  = "<output_filename>_ML_<datetime2>"
+ ml_varlist                   = 'ddt_temp_radswnw','ddt_temp_radlwnw','ddt_temp_radswcs','ddt_temp_radlwcs','ddt_temp_turb','ddt_temp_gscp','ddt_temp_mphy'
+ remap                        = 0
+ !reg_lon_def                  = ${reg_lon_def_reg}
+ !reg_lat_def                  = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype                     =  5                      ! output format: 2=GRIB2, 4=NETCDFv2
+ dom                          =  1                      ! write all domains
+ output_start     = "${start_date_out}"
+ output_end       = "${end_date_out}"
+ output_interval  = "${output_interval_3d}"
+ file_interval    = "${file_interval_3d}"
+ include_last                 =  .TRUE.
+ output_filename              = 'icon-rad3d' ! file name base
+ filename_format  = "<output_filename>_ML_<datetime2>"
+ ml_varlist                   = 'lwflxall', 'swflxall', 'swflxclr', 'lwflxclr'
+ remap                        = 0
+ !reg_lon_def                  = ${reg_lon_def_reg}
+ !reg_lat_def                  = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype                     =  5                      ! output format: 2=GRIB2, 4=NETCDFv2
+ dom                          =  1                      ! write all domains
+ output_start     = "${start_date_out}"
+ output_end       = "${end_date_out}"
+ output_interval  = "${output_interval_3d}"
+ file_interval    = "${file_interval_3d}"
+ include_last                 =  .TRUE.
+ output_filename              = 'icon-radbz' ! file name base
+ filename_format  = "<output_filename>_ML_<datetime2>"
+ ml_varlist                   = 'temphl_bz', 'preshl_bz', 'tempfl_bz', 'presfl_bz','tsfc_bz','qc_bz','qi_bz','qv_bz','clc_bz','cosz_bz'
+ remap                        = 0
+ !reg_lon_def                  = ${reg_lon_def_reg}
+ !reg_lat_def                  = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype                     =  5              ! output format: 2=GRIB2, 4=NETCDFv2
+ dom                          =  1                       ! write all domains
+ output_start     = "${start_date_out}"
+ output_end       = "${end_date_out}"
+ output_interval  = "${output_interval_2d}"
+ file_interval    = "${file_interval_2d}"
+ include_last                 = .TRUE.
+ output_filename              = 'icon-atm2d'   ! file name base
+ filename_format  = "<output_filename>_ML_<datetime2>"
+ ml_varlist                   = 'pres_sfc','u_10m','v_10m','t_g','t_2m','qv_2m','td_2m','rh_2m','clct','clch','clcm','clcl','tqv_dia','tqc_dia','tqi_dia','shfl_s','lhfl_s','cape_ml','cin_ml','rain_gsp_rate','tot_prec'
+ remap                        = 0
+ !reg_lon_def                  = ${reg_lon_def_reg}
+ !reg_lat_def                  = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype                     =  5              ! output format: 2=GRIB2, 4=NETCDFv2
+ dom                          =  1                       ! write all domains
+ output_start     = "${start_date_out}"
+ output_end       = "${end_date_out}"
+ output_interval  = "${output_interval_2d}"
+ file_interval    = "${file_interval_2d}"
+ include_last                 = .TRUE.
+ output_filename              = 'icon-rad2d'   ! file name base
+ filename_format  = "<output_filename>_ML_<datetime2>"
+ ml_varlist                   = 'sob_t','thb_t','sobclr_t','thbclr_t','sob_s','thb_s','thbclr_s','sobclr_s'
+ remap                        = 0
+ !reg_lon_def                  = ${reg_lon_def_reg}
+ !reg_lat_def                  = ${reg_lat_def_reg}
+/
+&output_nml
+ output_filename  = "icon-extra"
+ filename_format  = "<output_filename>_<levtype_l>"
+ filetype         = 5
+ remap            = 0
+ output_grid      = .TRUE.
+ output_start     = "${start_date_out}"          ! output_start = output_end
+ output_end       = "${start_date_out}"          ! --> write once only irrespective of
+ output_interval  = "PT15M"  !     the output interval and
+ file_interval    = "${file_interval_2d}"    !     the file interval
+ ml_varlist       = 'z_mc'
+/
+EOF
+
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration. 
+#-----------------------------------------------------------------------------
+
+final_status_file=${EXPDIR}/${job_name}.final_status #!NICOLE
+rm -f ${final_status_file}
+#-----------------------------------------------------------------------------
+
+# set up the model lists if they do not exist
+# this works for single model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+#  minrank_list=(        0           )
+#  maxrank_list=(     65535          )
+#  incrank_list=(        1           )
+  minrank_list[0]=0
+  maxrank_list[0]=65535
+  incrank_list[0]=1
+  if [ x$atmo_namelist != x ]; then
+    # this is the atmo model
+    namelist_list[0]="$atmo_namelist"
+    modelname_list[0]="atmo"
+    modeltype_list[0]=1
+    run_atmo="true"
+  else
+    check_error 1 "No namelist is defined"
+  fi
+fi
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+  restart=.true.
+  #  do not delete switch-file, to enable restart after unintended abort
+  #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+  if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+    sleep 10;
+  fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+  run_atmo="true"
+fi
+#-----------------------------------------------------------------------------
+# get restart files
+
+if  [ x$restart_atmo_from != "x" ] ; then
+  rm -f restart_atm_DOM01.nc
+#  ln -s ${basedir}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+  cp ${basedir}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+  ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+  restart=".true."
+fi
+if  [ x$restart_ocean_from != "x" ] ; then
+  rm -f restart_oce.nc
+#  ln -s ${basedir}/experiments/${restart_from_folder}/${restart_ocean_from} ${EXPDIR}/restart_oce.nc
+  cp ${basedir}/experiments/${restart_from_folder}/${restart_ocean_from} cp_restart_oce_DOM01.nc
+  ln -s cp_restart_oce_DOM01.nc restart_oce_DOM01.nc
+  restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+
+read_restart_namelists=${read_restart_namelists:=".true."}
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart            = $restart
+/
+&master_time_control_nml
+ experimentStartDate  = "$start_date"
+ restartTimeIntval    = "$restart_interval"
+ checkpointTimeIntval = "$checkpoint_interval"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+  calendar='proleptic gregorian'
+  calendar_type=1
+else
+  calendar=$calendar
+  calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart            = $restart
+ read_restart_namelists = $read_restart_namelists
+/
+&master_time_control_nml
+ calendar             = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval    = "$restart_interval"
+ experimentStartDate  = "$start_date"
+ experimentStopDate   = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+  model_namelist_filename="$1"
+  model_name=$2
+  model_type=$3
+  model_min_rank=$4
+  model_max_rank=$5
+  model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+  model_name="$model_name"
+  model_namelist_filename="$model_namelist_filename"
+  model_type=$model_type
+  model_min_rank=$model_min_rank
+  model_max_rank=$model_max_rank
+  model_inc_rank=$model_inc_rank
+/
+EOF
+
+}
+#-----------------------------------------------------------------------------
+
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+  add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+  j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+
+# start experiment
+
+rm -f finish.status
+date
+${START} ${MODEL}
+date
+#
+if [ -r finish.status ] ; then
+  check_final_status 0 "${START} ${MODEL}"
+else
+  check_final_status -1 "${START} ${MODEL}"
+fi
+#
+
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+#   Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ]; then
+  echo "restart next experiment..."
+  this_script="${RUNSCRIPTDIR}/exp.${EXPNAME}.run"
+  echo 'this_script: ' "$this_script"
+  # note that if ${restartSemaphoreFilename} does not exist yet, then touch will create it
+  touch ${restartSemaphoreFilename}
+  cd ${RUNSCRIPTDIR}
+  sbatch exp.${EXPNAME}.run
+else
+  [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+
+cd ${RUNSCRIPTDIR}
+
+#-----------------------------------------------------------------------------
diff --git a/sims/runscripts/LC1-LES-471x667km-lon40-lat44-300m-0003/exp.LC1-LES-471x667km-lon40-lat44-300m-0003.run b/sims/runscripts/LC1-LES-471x667km-lon40-lat44-300m-0003/exp.LC1-LES-471x667km-lon40-lat44-300m-0003.run
new file mode 100644
index 0000000000000000000000000000000000000000..d896494048fd85e3d5aaad7aaa3aa3c7b3b5808a
--- /dev/null
+++ b/sims/runscripts/LC1-LES-471x667km-lon40-lat44-300m-0003/exp.LC1-LES-471x667km-lon40-lat44-300m-0003.run
@@ -0,0 +1,722 @@
+#! /bin/bash
+#=============================================================================
+
+# levante  cpu batch job parameters
+
+#SBATCH --account=bb1135
+#SBATCH --job-name=LC1300m0003
+#SBATCH --partition=compute
+#SBATCH --chdir=/home/b/b381185/simulations/runscripts/LC1-LES-471x667km-lon40-lat44-300m-0003
+#SBATCH --nodes=150
+#SBATCH --threads-per-core=2
+# the following is needed to work around a bug that otherwise leads to
+# a too low number of ranks when using compute,compute2 as queue
+#SBATCH --mem=0
+#SBATCH --output=/home/b/b381185/simulations/runscripts/LC1-LES-471x667km-lon40-lat44-300m-0003/LOG.exp.LC1-LES-471x667km-lon40-lat44-300m-0003.run.%j.o
+#SBATCH --error=/home/b/b381185/simulations/runscripts/LC1-LES-471x667km-lon40-lat44-300m-0003/LOG.exp.LC1-LES-471x667km-lon40-lat44-300m-0003.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=08:00:00
+
+#=============================================================================
+set -x
+ulimit -s unlimited
+#=============================================================================
+# OpenMP environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=$OMP_NUM_THREADS
+export OMP_SCHEDULE="guided"
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=500M
+#
+# MPI variables
+# -------------
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((128 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+#=============================================================================
+# load local setting, if existing
+# -------------------------------
+if [ -a ../setting ]
+then
+  echo "Load Setting"
+  . ../setting
+fi
+# environment variables for the experiment and the target system
+# --------------------------------------------------------------
+export KMP_AFFINITY="granularity=fine,scatter"
+export KMP_LIBRARY="turnaround"
+export OMPI_MCA_pml="ucx"
+export OMPI_MCA_btl=self
+export OMPI_MCA_osc="pt2pt"
+export UCX_IB_ADDR_TYPE=ib_global
+export OMPI_MCA_coll="^ml"
+export OMPI_MCA_coll_hcoll_enable="1"
+export HCOLL_ENABLE_MCAST_ALL="1"
+export HCOLL_MAIN_IB=mlx5_0:1
+export UCX_NET_DEVICES=mlx5_0:1
+export UCX_TLS=mm,knem,cma,dc_mlx5,dc_x,self
+export UCX_UNIFIED_MODE=y
+export HDF5_USE_FILE_LOCKING=FALSE
+export OMPI_MCA_io="romio321"
+export MALLOC_TRIM_THRESHOLD_="-1"
+export MKL_ENABLE_INSTRUCTIONS=AVX2
+export MKL_DEBUG_CPU_TYPE=5
+export UCX_HANDLE_ERRORS=bt
+# load profile
+# ------------
+if [[ -a  /etc/profile ]]
+then
+        . /etc/profile
+fi
+
+#=============================================================================
+# directories with absolute paths
+# -------------------------------
+basedir="/home/b/b381185/icon-on-jet"
+export basedir
+
+# how to start the icon model
+# ---------------------------
+mask="0xf,0xf0000,0xf00000000,0xf000000000000,0xf0000000000000000,0xf00000000000000000000,0xf000000000000000000000000,0xf0000000000000000000000000000,0xf0,0xf00000,0xf000000000,0xf0000000000000,0xf00000000000000000,0xf000000000000000000000,0xf0000000000000000000000000,0xf00000000000000000000000000000,0xf00,0xf000000,0xf0000000000,0xf00000000000000,0xf000000000000000000,0xf0000000000000000000000,0xf00000000000000000000000000,0xf000000000000000000000000000000,0xf000,0xf0000000,0xf00000000000,0xf000000000000000,0xf0000000000000000000,0xf00000000000000000000000,0xf000000000000000000000000000,0xf0000000000000000000000000000000"
+export START="srun -l --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --ntasks=$((no_of_nodes * mpi_procs_pernode)) --propagate=STACK,CORE --distribution=block:block --cpu-bind=v,mask_cpu=$mask "
+export MODEL="${basedir}/bin/icon"
+
+# --------------------------
+submit="sbatch"
+job_name="exp.LC1-LES-471x667km-lon40-lat44-300m-0003.run"
+
+#=============================================================================
+
+ulimit -s $((4 * 1024 * 1024))
+ulimit -c 0
+
+#-----------------------------------------------------------------------------
+export EXPNAME="LC1-LES-471x667km-lon40-lat44-300m-0003"
+
+# atmo namelist filename
+atmo_namelist=NAMELIST_${EXPNAME}_atm
+
+# directories definition
+RUNSCRIPTDIR=/home/b/b381185/simulations/runscripts/LC1-LES-471x667km-lon40-lat44-300m-0003/  # run script directory
+
+#-----------------------------------------------------------------------------
+# global timing
+start_date="2022-01-05T06:00:30Z"
+end_date="2022-01-05T18:00:33Z"
+
+start_date_out="2022-01-05T06:00:33Z"
+end_date_out="2022-01-05T18:00:33Z" 
+
+# restart intervals
+checkpoint_interval="PT3H"
+restart_interval="P1D"
+
+# output intervals_2d
+output_interval_2d="PT15M"
+file_interval_2d="PT15M"
+
+# output intervals_3d
+output_interval_3d="PT30M"
+file_interval_3d="PT30M"
+
+# regular  grid: nlat=96, nlon=192, npts=18432, dlat=1.875 deg, dlon=1.875 deg
+reg_lat_def_reg=-90.0,1.0,90.0
+reg_lon_def_reg=-180.0,1.0,180.0
+
+#-----------------------------------------------------------------------------
+# model parameters
+model_equations=3             # equation system
+#                     1=hydrost. atm. T
+#                     1=hydrost. atm. theta dp
+#                     3=non-hydrost. atm.,
+#                     0=shallow water model
+#                    -1=hydrost. ocean
+#-----------------------------------------------------------------------------
+
+# experiment directory, with plenty of space, create if new
+
+EXPDIR=/work/bb1135/icon_output/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ;  then
+  mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ;  then
+    mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+# Link experiment initial files 
+inputdir='/work/bb1135/LES_Simulations/initial_conditions/plane_nest_300m_r6x6_2mom_40_44_0001'
+# Grid
+ln -sf $inputdir/new_raggedOrthogonal_471x667_300_with_boundary.nc grid_DOM01.nc
+# Extpar
+ln -sf $inputdir/extpar_remapped_12_months.nc extpar_DOM01.nc
+# Initial file
+ln -sf $inputdir/icon-fg_ML_nest_20220105T060030Z.nc icon_latbcdata_DOM01_06.nc
+# Ozon
+ln -sf $inputdir/ape_O3_remapped.nc o3_icon_DOM01.nc
+
+# Model required files
+ln -sf $basedir/data/rrtmg_lw.nc              ./
+ln -sf $basedir/data/rrtmg_sw.nc              ./
+ln -sf $basedir/data/ECHAM6_CldOptProps.nc    ./
+ln -sf $basedir/data/dmin_wetgrowth_lookup.nc ./
+
+RADDIR=$basedir/externals/ecrad/data
+ln -sf ${RADDIR}/* .
+
+# var dict for LATBC
+ln -sf /work/bb1135/LES_Simulations/initial_conditions/map_file.latbc ./map_file.latbc
+#=============================================================================
+
+# create ICON namelist parameters
+
+cat > ${atmo_namelist} << EOF
+&parallel_nml
+ nproma         = 16  ! optimal setting 8 for CRAY; use 16 or 24 for IBM
+ p_test_run     = .false.
+ l_test_openmp  = .false.
+ l_log_checks   = .false.
+ num_io_procs   = 20   ! asynchronous output for values >= 1
+ itype_comm     = 1
+ !iorder_sendrecv = 3  ! best value for CRAY (slightly faster than option 1)
+ num_prefetch_proc = 1
+ num_restart_procs = 1
+/
+&grid_nml
+ dynamics_grid_filename  = 'grid_DOM01.nc'
+ dynamics_parent_grid_id = 0
+ lredgrid_phys           = .false.
+ is_plane_cylinder       = .TRUE.
+ l_limited_area          = .true.
+ corio_lat               = 45.0
+/
+&initicon_nml
+ init_mode   = 7           ! operation mode 2: IFS
+ dwdfg_filename         = 'icon_latbcdata_DOM01_06.nc'
+ !nlevsoil_in            =  8
+ lread_ana              = .FALSE. 
+ ltile_coldstart = .TRUE.
+ !zpbl1       = 500. 
+ !zpbl2       = 1000.
+ !l_sst_in    = .true. 
+/
+&limarea_nml
+ itype_latbc = 1            ! transient
+ dtime_latbc = 3600.0
+ latbc_path = '/work/bb1135/LES_Simulations/initial_conditions/plane_nest_300m_r6x6_2mom_40_44_0001'
+ latbc_filename = "icon-fg_ML_nest_<y>-<m>-<d>T<h>.nc"
+ latbc_varnames_map_file     =            'map_file.latbc'
+ init_latbc_from_fg          =                     .FALSE.        ! .TRUE.: take lbc for initial time from first guess
+/
+&run_nml
+ num_lev        = 150          
+ dtime          = 3  ! 360 for R2B5 180 for R2B7    
+ ldynamics      = .TRUE.
+ ltransport     = .true.
+ iforcing       = 3            ! NWP forcing
+ ltestcase      = .false.      ! false: run with real data
+ msg_level      = 20            ! print maximum wind speeds every 5 time steps
+ ltimer         = .TRUE.      ! set .TRUE. for timer output
+ timers_level   = 10           ! can be increased up to 10 for detailed timer output
+ output         = "nml"
+ activate_sync_timers = .True.
+/
+&nwp_phy_nml
+ inwp_gscp       = 4
+ inwp_convection = 0
+ inwp_radiation  = 4  ! ecrad
+ inwp_cldcover   = 5
+ inwp_turb       = 5
+ inwp_satad      = 1
+ inwp_sso        = 0
+ inwp_gwd        = 0
+ inwp_surface    = 1
+ icapdcycl       = 3
+ latm_above_top  = .false.  ! the second entry refers to the nested domain (if present)
+ efdt_min_raylfric = 7200.
+ ldetrain_conv_prec = .false.
+ itype_z0         = 2
+ icpl_aero_conv   = 0 
+ icpl_aero_gscp   = 0
+ !icalc_reff       = 101     ! getting cloud-ice/water effective radi from rrtm
+ ! resolution-dependent settings - please choose the appropriate one
+ dt_rad    = 600 
+ dt_conv   = 300 
+ dt_sso    = 300 
+ dt_gwd    = 300. 
+ lcloudradonly    = .true. 
+ !ldiag_ddt_temp_dyn2          = .true.
+/
+&les_nml
+!! same values as in Matthias Brueck NA
+ smag_constant     = 0.3 ! this is default value
+ isrfc_type        = 1  !1=TERRA,2=Fixed flux, 5=fixed SST, 3=fixed bflux
+ ldiag_les_out     = .false.
+ les_metric        = .true.
+/
+&turbdiff_nml
+ tkhmin  = 0.75  
+ tkmmin  = 0.75             
+ pat_len = 100. !750.
+ c_diff  = 0.2
+ rat_sea = 10  
+ ltkesso = .true.
+ frcsmot = 0.2      
+ imode_frcsmot = 2  
+ itype_sher = 3    
+ ltkeshs    = .true.
+ a_hshr     = 2.0     
+/
+&lnd_nml
+ ntiles         = 3      !!! 1 for assimilation cycle and forecast
+ nlev_snow      = 3      !!! 1 for assimilation cycle and forecast
+ lmulti_snow    = .true. !!! .false. for assimilation cycle and forecast
+ itype_heatcond = 2
+ idiag_snowfrac = 2
+ lsnowtile      = .false.  !! later on .true. if GRIB encoding issues are solved
+ lseaice        = .true. !!Sophia Schäfer, 23/03/2017: reads in sea ice (e.g. zero ice), instead of setting by temperature
+ llake          = .false.
+ itype_lndtbl   = 3  ! minimizes moist/cold bias in lower tropical troposphere
+ itype_root     = 2  			        
+/
+&radiation_nml
+ irad_o3       = 4 
+ irad_aero     = 0
+ irad_cfc11    = 0
+ irad_cfc12    = 0
+ albedo_type   = 2 ! Modis albedo
+ vmr_co2       = 348.0e-6 ! values representative for 2012
+ vmr_ch4       = 1650.0e-09
+ vmr_n2o       = 396.0e-09
+ vmr_o2        = 0.20946
+ izenith       = 3
+ vmr_cfc11     = 0
+ vmr_cfc12     = 0
+ llw_cloud_scat = .true.
+/
+&nonhydrostatic_nml
+ iadv_rhotheta  = 2
+ ivctype        = 2
+ itime_scheme   = 4
+ exner_expol    = 0.333
+ vwind_offctr   = 0.2
+ damp_height    = 22500.
+ rayleigh_coeff = 0.10   ! R3B7: 1.0 for forecasts, 5.0 in assimilation cycle; 0.5/2.5 for R2B6 (i.e. ensemble) runs
+ lhdiff_rcf     = .true.
+ divdamp_order  = 24    ! setting for forecast runs; use '2' for assimilation cycle
+ divdamp_type   = 32    ! ** new setting for assimilation and forecast runs **
+ divdamp_fac    = 0.004 ! use 0.032 in conjunction with divdamp_order = 2 in assimilation cycle
+ l_open_ubc     = .false.
+ igradp_method  = 3
+ l_zdiffu_t     = .true.
+ thslp_zdiffu   = 0.02
+ thhgtd_zdiffu  = 125.
+ htop_moist_proc= 22500.
+ hbot_qvsubstep = 22500. ! use 22500. with R2B6
+/
+&sleve_nml
+ min_lay_thckn   = 20.
+ max_lay_thckn   = 400.   ! maximum layer thickness below htop_thcknlimit
+ htop_thcknlimit = 14000. ! this implies that the upcoming COSMO-EU nest will have 60 levels
+ top_height      = 30000.
+ stretch_fac     = 0.9
+ decay_scale_1   = 4000.
+ decay_scale_2   = 2500.
+ decay_exp       = 1.2
+ flat_height     = 16000.
+/
+&dynamics_nml
+ iequations     = 3
+ idiv_method    = 1
+ divavg_cntrwgt = 0.50
+ !lcoriolis      = .TRUE.
+/
+&transport_nml
+ ctracer_list  = '12345'
+ ivadv_tracer  = 3,3,3,3,3
+ itype_hlimit  = 3,4,4,4,4
+ ihadv_tracer  = 52,2,2,2,2
+ iadv_tke      = 0
+/
+&diffusion_nml
+ hdiff_order      = 5
+ itype_vn_diffu   = 1
+ itype_t_diffu    = 2
+ hdiff_efdt_ratio = 24.0
+ hdiff_smag_fac   = 0.025
+ lhdiff_vn        = .TRUE.
+ lhdiff_temp      = .TRUE.
+/
+&interpol_nml
+ !nudge_zone_width  = 0.0  ! deactivating nudging as needed for mass conservation for this setup 
+ !nudge_efold_width = 0.0
+ !nudge_max_coeff   = 0.00000
+ lsq_high_ord      = 3
+ l_intp_c2l        = .true.
+ l_mono_c2l        = .true.
+ support_baryctr_intp = .false.
+ rbf_vec_scale_c              = 0.01
+ rbf_vec_scale_v              = 0.025
+ rbf_vec_scale_e              = 0.05
+/
+&extpar_nml
+ itopo          = 1
+ extpar_filename             = 'extpar_DOM01.nc'
+ n_iter_smooth_topo = 1         
+ heightdiff_threshold = 3000.
+/
+&io_nml
+ itype_pres_msl = 5  ! New extrapolation method to circumvent Ninjo problem with surface inversions
+ itype_rh       = 1  ! RH w.r.t. water
+ restart_file_type = 5       ! 4: netcdf2, 5: netcdf4
+ !lkeep_in_sync                = .true.  ! was false in 2.5km NWP
+/
+&nh_pzlev_nml
+ nplev             = 18     ! number of p level output (attention: levels in [Pa] and top-down)
+ nzlev             = 20     ! number of z level output (attention: levels in [m] and top-down)
+ plevels           = 5000,7000,10000,15000,20000,25000,30000,40000,50000,60000,70000,
+                     80000,85000,90000,92500,95000,97500,100000
+ zlevels           = 25000,20000,18000,16000,14000,12000,10000,8000,6000,5000,4000,3000,2000,
+                     1000,800,600,400,200,100,10
+/
+&output_nml
+ filetype         =  5                        ! output format: 2=GRIB2, 4=NETCDFv2
+ output_start     = "${start_date_out}"
+ output_end       = "${end_date_out}"
+ output_interval  = "${output_interval_3d}"
+ file_interval    = "${file_interval_3d}"
+ include_last                 =  .TRUE.
+ output_filename              = 'icon-atm3d' ! file name base
+ filename_format  = "<output_filename>_ML_<datetime2>"
+ ml_varlist                   = 'u','v','pv','temp','qi','qc','qv','pres','rho','w','z_ifc','tot_qv_dia','O3'
+ remap                        = 0
+ !reg_lon_def                  = ${reg_lon_def_reg}
+ !reg_lat_def                  = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype                     =  5                      ! output format: 2=GRIB2, 4=NETCDFv2
+ dom                          =  1                      ! write all domains
+ output_start     = "${start_date_out}"
+ output_end       = "${end_date_out}"
+ output_interval  = "${output_interval_3d}"
+ file_interval    = "${file_interval_3d}"
+ include_last                 =  .TRUE.
+ output_filename              = 'icon-cld3d' ! file name base
+ filename_format  = "<output_filename>_ML_<datetime2>"
+ ml_varlist                   = 'clc','tot_qi_dia','tot_qc_dia','acdnc','reff_qc_ecrad','reff_qi_ecrad','pres_ifc','rho_ic','tsfctrad'
+ remap                        = 0
+ !reg_lon_def                  = ${reg_lon_def_reg}
+ !reg_lat_def                  = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype                     =  5                      ! output format: 2=GRIB2, 4=NETCDFv2
+ dom                          =  1                      ! write all domains
+ output_start     = "${start_date_out}"
+ output_end       = "${end_date_out}"
+ output_interval  = "${output_interval_3d}"
+ file_interval    = "${file_interval_3d}"
+ include_last                 =  .TRUE.
+ output_filename              = 'icon-ddt_temp' ! file name base
+ filename_format  = "<output_filename>_ML_<datetime2>"
+ ml_varlist                   = 'ddt_temp_radswnw','ddt_temp_radlwnw','ddt_temp_radswcs','ddt_temp_radlwcs','ddt_temp_turb','ddt_temp_gscp','ddt_temp_mphy'
+ remap                        = 0
+ !reg_lon_def                  = ${reg_lon_def_reg}
+ !reg_lat_def                  = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype                     =  5                      ! output format: 2=GRIB2, 4=NETCDFv2
+ dom                          =  1                      ! write all domains
+ output_start     = "${start_date_out}"
+ output_end       = "${end_date_out}"
+ output_interval  = "${output_interval_3d}"
+ file_interval    = "${file_interval_3d}"
+ include_last                 =  .TRUE.
+ output_filename              = 'icon-rad3d' ! file name base
+ filename_format  = "<output_filename>_ML_<datetime2>"
+ ml_varlist                   = 'lwflxall', 'swflxall', 'swflxclr', 'lwflxclr'
+ remap                        = 0
+ !reg_lon_def                  = ${reg_lon_def_reg}
+ !reg_lat_def                  = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype                     =  5                      ! output format: 2=GRIB2, 4=NETCDFv2
+ dom                          =  1                      ! write all domains
+ output_start     = "${start_date_out}"
+ output_end       = "${end_date_out}"
+ output_interval  = "${output_interval_3d}"
+ file_interval    = "${file_interval_3d}"
+ include_last                 =  .TRUE.
+ output_filename              = 'icon-radbz' ! file name base
+ filename_format  = "<output_filename>_ML_<datetime2>"
+ ml_varlist                   = 'temphl_bz', 'preshl_bz', 'tempfl_bz', 'presfl_bz','tsfc_bz','qc_bz','qi_bz','qv_bz','clc_bz','cosz_bz'
+ remap                        = 0
+ !reg_lon_def                  = ${reg_lon_def_reg}
+ !reg_lat_def                  = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype                     =  5              ! output format: 2=GRIB2, 4=NETCDFv2
+ dom                          =  1                       ! write all domains
+ output_start     = "${start_date_out}"
+ output_end       = "${end_date_out}"
+ output_interval  = "${output_interval_2d}"
+ file_interval    = "${file_interval_2d}"
+ include_last                 = .TRUE.
+ output_filename              = 'icon-atm2d'   ! file name base
+ filename_format  = "<output_filename>_ML_<datetime2>"
+ ml_varlist                   = 'pres_sfc','u_10m','v_10m','t_g','t_2m','qv_2m','td_2m','rh_2m','clct','clch','clcm','clcl','tqv_dia','tqc_dia','tqi_dia','shfl_s','lhfl_s','cape_ml','cin_ml','rain_gsp_rate','tot_prec'
+ remap                        = 0
+ !reg_lon_def                  = ${reg_lon_def_reg}
+ !reg_lat_def                  = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype                     =  5              ! output format: 2=GRIB2, 4=NETCDFv2
+ dom                          =  1                       ! write all domains
+ output_start     = "${start_date_out}"
+ output_end       = "${end_date_out}"
+ output_interval  = "${output_interval_2d}"
+ file_interval    = "${file_interval_2d}"
+ include_last                 = .TRUE.
+ output_filename              = 'icon-rad2d'   ! file name base
+ filename_format  = "<output_filename>_ML_<datetime2>"
+ ml_varlist                   = 'sob_t','thb_t','sobclr_t','thbclr_t','sob_s','thb_s','thbclr_s','sobclr_s'
+ remap                        = 0
+ !reg_lon_def                  = ${reg_lon_def_reg}
+ !reg_lat_def                  = ${reg_lat_def_reg}
+/
+&output_nml
+ output_filename  = "icon-extra"
+ filename_format  = "<output_filename>_<levtype_l>"
+ filetype         = 5
+ remap            = 0
+ output_grid      = .TRUE.
+ output_start     = "${start_date_out}"          ! output_start = output_end
+ output_end       = "${start_date_out}"          ! --> write once only irrespective of
+ output_interval  = "PT15M"  !     the output interval and
+ file_interval    = "${file_interval_2d}"    !     the file interval
+ ml_varlist       = 'z_mc'
+/
+EOF
+
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration. 
+#-----------------------------------------------------------------------------
+
+final_status_file=${EXPDIR}/${job_name}.final_status #!NICOLE
+rm -f ${final_status_file}
+#-----------------------------------------------------------------------------
+
+# set up the model lists if they do not exist
+# this works for single model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+#  minrank_list=(        0           )
+#  maxrank_list=(     65535          )
+#  incrank_list=(        1           )
+  minrank_list[0]=0
+  maxrank_list[0]=65535
+  incrank_list[0]=1
+  if [ x$atmo_namelist != x ]; then
+    # this is the atmo model
+    namelist_list[0]="$atmo_namelist"
+    modelname_list[0]="atmo"
+    modeltype_list[0]=1
+    run_atmo="true"
+  else
+    check_error 1 "No namelist is defined"
+  fi
+fi
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+  restart=.true.
+  #  do not delete switch-file, to enable restart after unintended abort
+  #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+  if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+    sleep 10;
+  fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+  run_atmo="true"
+fi
+#-----------------------------------------------------------------------------
+# get restart files
+
+if  [ x$restart_atmo_from != "x" ] ; then
+  rm -f restart_atm_DOM01.nc
+#  ln -s ${basedir}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+  cp ${basedir}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+  ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+  restart=".true."
+fi
+if  [ x$restart_ocean_from != "x" ] ; then
+  rm -f restart_oce.nc
+#  ln -s ${basedir}/experiments/${restart_from_folder}/${restart_ocean_from} ${EXPDIR}/restart_oce.nc
+  cp ${basedir}/experiments/${restart_from_folder}/${restart_ocean_from} cp_restart_oce_DOM01.nc
+  ln -s cp_restart_oce_DOM01.nc restart_oce_DOM01.nc
+  restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+
+read_restart_namelists=${read_restart_namelists:=".true."}
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart            = $restart
+/
+&master_time_control_nml
+ experimentStartDate  = "$start_date"
+ restartTimeIntval    = "$restart_interval"
+ checkpointTimeIntval = "$checkpoint_interval"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+  calendar='proleptic gregorian'
+  calendar_type=1
+else
+  calendar=$calendar
+  calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart            = $restart
+ read_restart_namelists = $read_restart_namelists
+/
+&master_time_control_nml
+ calendar             = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval    = "$restart_interval"
+ experimentStartDate  = "$start_date"
+ experimentStopDate   = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+  model_namelist_filename="$1"
+  model_name=$2
+  model_type=$3
+  model_min_rank=$4
+  model_max_rank=$5
+  model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+  model_name="$model_name"
+  model_namelist_filename="$model_namelist_filename"
+  model_type=$model_type
+  model_min_rank=$model_min_rank
+  model_max_rank=$model_max_rank
+  model_inc_rank=$model_inc_rank
+/
+EOF
+
+}
+#-----------------------------------------------------------------------------
+
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+  add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+  j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+
+# start experiment
+
+rm -f finish.status
+date
+${START} ${MODEL}
+date
+#
+if [ -r finish.status ] ; then
+  check_final_status 0 "${START} ${MODEL}"
+else
+  check_final_status -1 "${START} ${MODEL}"
+fi
+#
+
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+#   Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ]; then
+  echo "restart next experiment..."
+  this_script="${RUNSCRIPTDIR}/exp.${EXPNAME}.run"
+  echo 'this_script: ' "$this_script"
+  # note that if ${restartSemaphoreFilename} does not exist yet, then touch will create it
+  touch ${restartSemaphoreFilename}
+  cd ${RUNSCRIPTDIR}
+  sbatch exp.${EXPNAME}.run
+else
+  [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+
+cd ${RUNSCRIPTDIR}
+
+#-----------------------------------------------------------------------------
diff --git a/sims/runscripts/LC1-LES-471x667km-lon50-lat48-300m-0004/exp.LC1-LES-471x667km-lon50-lat48-300m-0004.run b/sims/runscripts/LC1-LES-471x667km-lon50-lat48-300m-0004/exp.LC1-LES-471x667km-lon50-lat48-300m-0004.run
new file mode 100644
index 0000000000000000000000000000000000000000..4f5199cd9c93c71f333c8df1a78dd4dcc4f5b52f
--- /dev/null
+++ b/sims/runscripts/LC1-LES-471x667km-lon50-lat48-300m-0004/exp.LC1-LES-471x667km-lon50-lat48-300m-0004.run
@@ -0,0 +1,722 @@
+#! /bin/bash
+#=============================================================================
+
+# levante  cpu batch job parameters
+
+#SBATCH --account=bb1135
+#SBATCH --job-name=LC1300m0004
+#SBATCH --partition=compute
+#SBATCH --chdir=/home/b/b381185/simulations/runscripts/LC1-LES-471x667km-lon50-lat48-300m-0004
+#SBATCH --nodes=150
+#SBATCH --threads-per-core=2
+# the following is needed to work around a bug that otherwise leads to
+# a too low number of ranks when using compute,compute2 as queue
+#SBATCH --mem=0
+#SBATCH --output=/home/b/b381185/simulations/runscripts/LC1-LES-471x667km-lon50-lat48-300m-0004/LOG.exp.LC1-LES-471x667km-lon50-lat48-300m-0004.run.%j.o
+#SBATCH --error=/home/b/b381185/simulations/runscripts/LC1-LES-471x667km-lon50-lat48-300m-0004/LOG.exp.LC1-LES-471x667km-lon50-lat48-300m-0004.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=08:00:00
+
+#=============================================================================
+set -x
+ulimit -s unlimited
+#=============================================================================
+# OpenMP environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=$OMP_NUM_THREADS
+export OMP_SCHEDULE="guided"
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=500M
+#
+# MPI variables
+# -------------
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((128 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+#=============================================================================
+# load local setting, if existing
+# -------------------------------
+if [ -a ../setting ]
+then
+  echo "Load Setting"
+  . ../setting
+fi
+# environment variables for the experiment and the target system
+# --------------------------------------------------------------
+export KMP_AFFINITY="granularity=fine,scatter"
+export KMP_LIBRARY="turnaround"
+export OMPI_MCA_pml="ucx"
+export OMPI_MCA_btl=self
+export OMPI_MCA_osc="pt2pt"
+export UCX_IB_ADDR_TYPE=ib_global
+export OMPI_MCA_coll="^ml"
+export OMPI_MCA_coll_hcoll_enable="1"
+export HCOLL_ENABLE_MCAST_ALL="1"
+export HCOLL_MAIN_IB=mlx5_0:1
+export UCX_NET_DEVICES=mlx5_0:1
+export UCX_TLS=mm,knem,cma,dc_mlx5,dc_x,self
+export UCX_UNIFIED_MODE=y
+export HDF5_USE_FILE_LOCKING=FALSE
+export OMPI_MCA_io="romio321"
+export MALLOC_TRIM_THRESHOLD_="-1"
+export MKL_ENABLE_INSTRUCTIONS=AVX2
+export MKL_DEBUG_CPU_TYPE=5
+export UCX_HANDLE_ERRORS=bt
+# load profile
+# ------------
+if [[ -a  /etc/profile ]]
+then
+        . /etc/profile
+fi
+
+#=============================================================================
+# directories with absolute paths
+# -------------------------------
+basedir="/home/b/b381185/icon-on-jet"
+export basedir
+
+# how to start the icon model
+# ---------------------------
+mask="0xf,0xf0000,0xf00000000,0xf000000000000,0xf0000000000000000,0xf00000000000000000000,0xf000000000000000000000000,0xf0000000000000000000000000000,0xf0,0xf00000,0xf000000000,0xf0000000000000,0xf00000000000000000,0xf000000000000000000000,0xf0000000000000000000000000,0xf00000000000000000000000000000,0xf00,0xf000000,0xf0000000000,0xf00000000000000,0xf000000000000000000,0xf0000000000000000000000,0xf00000000000000000000000000,0xf000000000000000000000000000000,0xf000,0xf0000000,0xf00000000000,0xf000000000000000,0xf0000000000000000000,0xf00000000000000000000000,0xf000000000000000000000000000,0xf0000000000000000000000000000000"
+export START="srun -l --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --ntasks=$((no_of_nodes * mpi_procs_pernode)) --propagate=STACK,CORE --distribution=block:block --cpu-bind=v,mask_cpu=$mask "
+export MODEL="${basedir}/bin/icon"
+
+# --------------------------
+submit="sbatch"
+job_name="exp.LC1-LES-471x667km-lon50-lat48-300m-0004.run"
+
+#=============================================================================
+
+ulimit -s $((4 * 1024 * 1024))
+ulimit -c 0
+
+#-----------------------------------------------------------------------------
+export EXPNAME="LC1-LES-471x667km-lon50-lat48-300m-0004"
+
+# atmo namelist filename
+atmo_namelist=NAMELIST_${EXPNAME}_atm
+
+# directories definition
+RUNSCRIPTDIR=/home/b/b381185/simulations/runscripts/LC1-LES-471x667km-lon50-lat48-300m-0004/  # run script directory
+
+#-----------------------------------------------------------------------------
+# global timing
+start_date="2022-01-05T12:00:30Z"
+end_date="2022-01-05T23:00:33Z"
+
+start_date_out="2022-01-05T12:00:33Z"
+end_date_out="2022-01-05T23:00:33Z" 
+
+# restart intervals
+checkpoint_interval="PT3H"
+restart_interval="P1D"
+
+# output intervals_2d
+output_interval_2d="PT15M"
+file_interval_2d="PT15M"
+
+# output intervals_3d
+output_interval_3d="PT30M"
+file_interval_3d="PT30M"
+
+# regular  grid: nlat=96, nlon=192, npts=18432, dlat=1.875 deg, dlon=1.875 deg
+reg_lat_def_reg=-90.0,1.0,90.0
+reg_lon_def_reg=-180.0,1.0,180.0
+
+#-----------------------------------------------------------------------------
+# model parameters
+model_equations=3             # equation system
+#                     1=hydrost. atm. T
+#                     1=hydrost. atm. theta dp
+#                     3=non-hydrost. atm.,
+#                     0=shallow water model
+#                    -1=hydrost. ocean
+#-----------------------------------------------------------------------------
+
+# experiment directory, with plenty of space, create if new
+
+EXPDIR=/work/bb1135/icon_output/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ;  then
+  mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ;  then
+    mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+# Link experiment initial files 
+inputdir='/work/bb1135/LES_Simulations/initial_conditions/plane_nest_300m_r6x6_2mom_50_48_0002'
+# Grid
+ln -sf $inputdir/new_raggedOrthogonal_471x667_300_with_boundary_dom02.nc grid_DOM01.nc
+# Extpar
+ln -sf $inputdir/extpar_remapped_12_months.nc extpar_DOM01.nc
+# Initial file
+ln -sf $inputdir/icon-fg_ML_nest_20220105T120030Z.nc icon_latbcdata_DOM01_12.nc
+# Ozon
+ln -sf $inputdir/ape_O3_remapped.nc o3_icon_DOM01.nc
+
+# Model required files
+ln -sf $basedir/data/rrtmg_lw.nc              ./
+ln -sf $basedir/data/rrtmg_sw.nc              ./
+ln -sf $basedir/data/ECHAM6_CldOptProps.nc    ./
+ln -sf $basedir/data/dmin_wetgrowth_lookup.nc ./
+
+RADDIR=$basedir/externals/ecrad/data
+ln -sf ${RADDIR}/* .
+
+# var dict for LATBC
+ln -sf /work/bb1135/LES_Simulations/initial_conditions/map_file.latbc ./map_file.latbc
+#=============================================================================
+
+# create ICON namelist parameters
+
+cat > ${atmo_namelist} << EOF
+&parallel_nml
+ nproma         = 16  ! optimal setting 8 for CRAY; use 16 or 24 for IBM
+ p_test_run     = .false.
+ l_test_openmp  = .false.
+ l_log_checks   = .false.
+ num_io_procs   = 20   ! asynchronous output for values >= 1
+ itype_comm     = 1
+ !iorder_sendrecv = 3  ! best value for CRAY (slightly faster than option 1)
+ num_prefetch_proc = 1
+ num_restart_procs = 1
+/
+&grid_nml
+ dynamics_grid_filename  = 'grid_DOM01.nc'
+ dynamics_parent_grid_id = 0
+ lredgrid_phys           = .false.
+ is_plane_cylinder       = .TRUE.
+ l_limited_area          = .true.
+ corio_lat               = 45.0
+/
+&initicon_nml
+ init_mode   = 7           ! operation mode 2: IFS
+ dwdfg_filename         = 'icon_latbcdata_DOM01_12.nc'
+ !nlevsoil_in            =  8
+ lread_ana              = .FALSE. 
+ ltile_coldstart = .TRUE.
+ !zpbl1       = 500. 
+ !zpbl2       = 1000.
+ !l_sst_in    = .true. 
+/
+&limarea_nml
+ itype_latbc = 1            ! transient
+ dtime_latbc = 3600.0
+ latbc_path = '/work/bb1135/LES_Simulations/initial_conditions/plane_nest_300m_r6x6_2mom_50_48_0002'
+ latbc_filename = "icon-fg_ML_nest_<y>-<m>-<d>T<h>.nc"
+ latbc_varnames_map_file     =            'map_file.latbc'
+ init_latbc_from_fg          =                     .FALSE.        ! .TRUE.: take lbc for initial time from first guess
+/
+&run_nml
+ num_lev        = 150          
+ dtime          = 3  ! 360 for R2B5 180 for R2B7    
+ ldynamics      = .TRUE.
+ ltransport     = .true.
+ iforcing       = 3            ! NWP forcing
+ ltestcase      = .false.      ! false: run with real data
+ msg_level      = 20            ! print maximum wind speeds every 5 time steps
+ ltimer         = .TRUE.      ! set .TRUE. for timer output
+ timers_level   = 10           ! can be increased up to 10 for detailed timer output
+ output         = "nml"
+ activate_sync_timers = .True.
+/
+&nwp_phy_nml
+ inwp_gscp       = 4
+ inwp_convection = 0
+ inwp_radiation  = 4  ! ecrad
+ inwp_cldcover   = 5
+ inwp_turb       = 5
+ inwp_satad      = 1
+ inwp_sso        = 0
+ inwp_gwd        = 0
+ inwp_surface    = 1
+ icapdcycl       = 3
+ latm_above_top  = .false.  ! the second entry refers to the nested domain (if present)
+ efdt_min_raylfric = 7200.
+ ldetrain_conv_prec = .false.
+ itype_z0         = 2
+ icpl_aero_conv   = 0 
+ icpl_aero_gscp   = 0
+ !icalc_reff       = 101     ! getting cloud-ice/water effective radi from rrtm
+ ! resolution-dependent settings - please choose the appropriate one
+ dt_rad    = 600 
+ dt_conv   = 300 
+ dt_sso    = 300 
+ dt_gwd    = 300. 
+ lcloudradonly    = .true. 
+ !ldiag_ddt_temp_dyn2          = .true.
+/
+&les_nml
+!! same values as in Matthias Brueck NA
+ smag_constant     = 0.3 ! this is default value
+ isrfc_type        = 1  !1=TERRA,2=Fixed flux, 5=fixed SST, 3=fixed bflux
+ ldiag_les_out     = .false.
+ les_metric        = .true.
+/
+&turbdiff_nml
+ tkhmin  = 0.75  
+ tkmmin  = 0.75             
+ pat_len = 100. !750.
+ c_diff  = 0.2
+ rat_sea = 10  
+ ltkesso = .true.
+ frcsmot = 0.2      
+ imode_frcsmot = 2  
+ itype_sher = 3    
+ ltkeshs    = .true.
+ a_hshr     = 2.0     
+/
+&lnd_nml
+ ntiles         = 3      !!! 1 for assimilation cycle and forecast
+ nlev_snow      = 3      !!! 1 for assimilation cycle and forecast
+ lmulti_snow    = .true. !!! .false. for assimilation cycle and forecast
+ itype_heatcond = 2
+ idiag_snowfrac = 2
+ lsnowtile      = .false.  !! later on .true. if GRIB encoding issues are solved
+ lseaice        = .true. !!Sophia Schäfer, 23/03/2017: reads in sea ice (e.g. zero ice), instead of setting by temperature
+ llake          = .false.
+ itype_lndtbl   = 3  ! minimizes moist/cold bias in lower tropical troposphere
+ itype_root     = 2  			        
+/
+&radiation_nml
+ irad_o3       = 4 
+ irad_aero     = 0
+ irad_cfc11    = 0
+ irad_cfc12    = 0
+ albedo_type   = 2 ! Modis albedo
+ vmr_co2       = 348.0e-6 ! values representative for 2012
+ vmr_ch4       = 1650.0e-09
+ vmr_n2o       = 396.0e-09
+ vmr_o2        = 0.20946
+ izenith       = 3
+ vmr_cfc11     = 0
+ vmr_cfc12     = 0
+ llw_cloud_scat = .true.
+/
+&nonhydrostatic_nml
+ iadv_rhotheta  = 2
+ ivctype        = 2
+ itime_scheme   = 4
+ exner_expol    = 0.333
+ vwind_offctr   = 0.2
+ damp_height    = 22500.
+ rayleigh_coeff = 0.10   ! R3B7: 1.0 for forecasts, 5.0 in assimilation cycle; 0.5/2.5 for R2B6 (i.e. ensemble) runs
+ lhdiff_rcf     = .true.
+ divdamp_order  = 24    ! setting for forecast runs; use '2' for assimilation cycle
+ divdamp_type   = 32    ! ** new setting for assimilation and forecast runs **
+ divdamp_fac    = 0.004 ! use 0.032 in conjunction with divdamp_order = 2 in assimilation cycle
+ l_open_ubc     = .false.
+ igradp_method  = 3
+ l_zdiffu_t     = .true.
+ thslp_zdiffu   = 0.02
+ thhgtd_zdiffu  = 125.
+ htop_moist_proc= 22500.
+ hbot_qvsubstep = 22500. ! use 22500. with R2B6
+/
+&sleve_nml
+ min_lay_thckn   = 20.
+ max_lay_thckn   = 400.   ! maximum layer thickness below htop_thcknlimit
+ htop_thcknlimit = 14000. ! this implies that the upcoming COSMO-EU nest will have 60 levels
+ top_height      = 30000.
+ stretch_fac     = 0.9
+ decay_scale_1   = 4000.
+ decay_scale_2   = 2500.
+ decay_exp       = 1.2
+ flat_height     = 16000.
+/
+&dynamics_nml
+ iequations     = 3
+ idiv_method    = 1
+ divavg_cntrwgt = 0.50
+ !lcoriolis      = .TRUE.
+/
+&transport_nml
+ ctracer_list  = '12345'
+ ivadv_tracer  = 3,3,3,3,3
+ itype_hlimit  = 3,4,4,4,4
+ ihadv_tracer  = 52,2,2,2,2
+ iadv_tke      = 0
+/
+&diffusion_nml
+ hdiff_order      = 5
+ itype_vn_diffu   = 1
+ itype_t_diffu    = 2
+ hdiff_efdt_ratio = 24.0
+ hdiff_smag_fac   = 0.025
+ lhdiff_vn        = .TRUE.
+ lhdiff_temp      = .TRUE.
+/
+&interpol_nml
+ !nudge_zone_width  = 0.0  ! deactivating nudging as needed for mass conservation for this setup 
+ !nudge_efold_width = 0.0
+ !nudge_max_coeff   = 0.00000
+ lsq_high_ord      = 3
+ l_intp_c2l        = .true.
+ l_mono_c2l        = .true.
+ support_baryctr_intp = .false.
+ rbf_vec_scale_c              = 0.01
+ rbf_vec_scale_v              = 0.025
+ rbf_vec_scale_e              = 0.05
+/
+&extpar_nml
+ itopo          = 1
+ extpar_filename             = 'extpar_DOM01.nc'
+ n_iter_smooth_topo = 1         
+ heightdiff_threshold = 3000.
+/
+&io_nml
+ itype_pres_msl = 5  ! New extrapolation method to circumvent Ninjo problem with surface inversions
+ itype_rh       = 1  ! RH w.r.t. water
+ restart_file_type = 5       ! 4: netcdf2, 5: netcdf4
+ !lkeep_in_sync                = .true.  ! was false in 2.5km NWP
+/
+&nh_pzlev_nml
+ nplev             = 18     ! number of p level output (attention: levels in [Pa] and top-down)
+ nzlev             = 20     ! number of z level output (attention: levels in [m] and top-down)
+ plevels           = 5000,7000,10000,15000,20000,25000,30000,40000,50000,60000,70000,
+                     80000,85000,90000,92500,95000,97500,100000
+ zlevels           = 25000,20000,18000,16000,14000,12000,10000,8000,6000,5000,4000,3000,2000,
+                     1000,800,600,400,200,100,10
+/
+&output_nml
+ filetype         =  5                        ! output format: 2=GRIB2, 4=NETCDFv2
+ output_start     = "${start_date_out}"
+ output_end       = "${end_date_out}"
+ output_interval  = "${output_interval_3d}"
+ file_interval    = "${file_interval_3d}"
+ include_last                 =  .TRUE.
+ output_filename              = 'icon-atm3d' ! file name base
+ filename_format  = "<output_filename>_ML_<datetime2>"
+ ml_varlist                   = 'u','v','pv','temp','qi','qc','qv','pres','rho','w','z_ifc','tot_qv_dia','O3'
+ remap                        = 0
+ !reg_lon_def                  = ${reg_lon_def_reg}
+ !reg_lat_def                  = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype                     =  5                      ! output format: 2=GRIB2, 4=NETCDFv2
+ dom                          =  1                      ! write all domains
+ output_start     = "${start_date_out}"
+ output_end       = "${end_date_out}"
+ output_interval  = "${output_interval_3d}"
+ file_interval    = "${file_interval_3d}"
+ include_last                 =  .TRUE.
+ output_filename              = 'icon-cld3d' ! file name base
+ filename_format  = "<output_filename>_ML_<datetime2>"
+ ml_varlist                   = 'clc','tot_qi_dia','tot_qc_dia','acdnc','reff_qc_ecrad','reff_qi_ecrad','pres_ifc','rho_ic','tsfctrad'
+ remap                        = 0
+ !reg_lon_def                  = ${reg_lon_def_reg}
+ !reg_lat_def                  = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype                     =  5                      ! output format: 2=GRIB2, 4=NETCDFv2
+ dom                          =  1                      ! write all domains
+ output_start     = "${start_date_out}"
+ output_end       = "${end_date_out}"
+ output_interval  = "${output_interval_3d}"
+ file_interval    = "${file_interval_3d}"
+ include_last                 =  .TRUE.
+ output_filename              = 'icon-ddt_temp' ! file name base
+ filename_format  = "<output_filename>_ML_<datetime2>"
+ ml_varlist                   = 'ddt_temp_radswnw','ddt_temp_radlwnw','ddt_temp_radswcs','ddt_temp_radlwcs','ddt_temp_turb','ddt_temp_gscp','ddt_temp_mphy'
+ remap                        = 0
+ !reg_lon_def                  = ${reg_lon_def_reg}
+ !reg_lat_def                  = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype                     =  5                      ! output format: 2=GRIB2, 4=NETCDFv2
+ dom                          =  1                      ! write all domains
+ output_start     = "${start_date_out}"
+ output_end       = "${end_date_out}"
+ output_interval  = "${output_interval_3d}"
+ file_interval    = "${file_interval_3d}"
+ include_last                 =  .TRUE.
+ output_filename              = 'icon-rad3d' ! file name base
+ filename_format  = "<output_filename>_ML_<datetime2>"
+ ml_varlist                   = 'lwflxall', 'swflxall', 'swflxclr', 'lwflxclr'
+ remap                        = 0
+ !reg_lon_def                  = ${reg_lon_def_reg}
+ !reg_lat_def                  = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype                     =  5                      ! output format: 2=GRIB2, 4=NETCDFv2
+ dom                          =  1                      ! write all domains
+ output_start     = "${start_date_out}"
+ output_end       = "${end_date_out}"
+ output_interval  = "${output_interval_3d}"
+ file_interval    = "${file_interval_3d}"
+ include_last                 =  .TRUE.
+ output_filename              = 'icon-radbz' ! file name base
+ filename_format  = "<output_filename>_ML_<datetime2>"
+ ml_varlist                   = 'temphl_bz', 'preshl_bz', 'tempfl_bz', 'presfl_bz','tsfc_bz','qc_bz','qi_bz','qv_bz','clc_bz','cosz_bz'
+ remap                        = 0
+ !reg_lon_def                  = ${reg_lon_def_reg}
+ !reg_lat_def                  = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype                     =  5              ! output format: 2=GRIB2, 4=NETCDFv2
+ dom                          =  1                       ! write all domains
+ output_start     = "${start_date_out}"
+ output_end       = "${end_date_out}"
+ output_interval  = "${output_interval_2d}"
+ file_interval    = "${file_interval_2d}"
+ include_last                 = .TRUE.
+ output_filename              = 'icon-atm2d'   ! file name base
+ filename_format  = "<output_filename>_ML_<datetime2>"
+ ml_varlist                   = 'pres_sfc','u_10m','v_10m','t_g','t_2m','qv_2m','td_2m','rh_2m','clct','clch','clcm','clcl','tqv_dia','tqc_dia','tqi_dia','shfl_s','lhfl_s','cape_ml','cin_ml','rain_gsp_rate','tot_prec'
+ remap                        = 0
+ !reg_lon_def                  = ${reg_lon_def_reg}
+ !reg_lat_def                  = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype                     =  5              ! output format: 2=GRIB2, 4=NETCDFv2
+ dom                          =  1                       ! write all domains
+ output_start     = "${start_date_out}"
+ output_end       = "${end_date_out}"
+ output_interval  = "${output_interval_2d}"
+ file_interval    = "${file_interval_2d}"
+ include_last                 = .TRUE.
+ output_filename              = 'icon-rad2d'   ! file name base
+ filename_format  = "<output_filename>_ML_<datetime2>"
+ ml_varlist                   = 'sob_t','thb_t','sobclr_t','thbclr_t','sob_s','thb_s','thbclr_s','sobclr_s'
+ remap                        = 0
+ !reg_lon_def                  = ${reg_lon_def_reg}
+ !reg_lat_def                  = ${reg_lat_def_reg}
+/
+&output_nml
+ output_filename  = "icon-extra"
+ filename_format  = "<output_filename>_<levtype_l>"
+ filetype         = 5
+ remap            = 0
+ output_grid      = .TRUE.
+ output_start     = "${start_date_out}"          ! output_start = output_end
+ output_end       = "${start_date_out}"          ! --> write once only irrespective of
+ output_interval  = "PT15M"  !     the output interval and
+ file_interval    = "${file_interval_2d}"    !     the file interval
+ ml_varlist       = 'z_mc'
+/
+EOF
+
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration. 
+#-----------------------------------------------------------------------------
+
+final_status_file=${EXPDIR}/${job_name}.final_status #!NICOLE
+rm -f ${final_status_file}
+#-----------------------------------------------------------------------------
+
+# set up the model lists if they do not exist
+# this works for single model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+#  minrank_list=(        0           )
+#  maxrank_list=(     65535          )
+#  incrank_list=(        1           )
+  minrank_list[0]=0
+  maxrank_list[0]=65535
+  incrank_list[0]=1
+  if [ x$atmo_namelist != x ]; then
+    # this is the atmo model
+    namelist_list[0]="$atmo_namelist"
+    modelname_list[0]="atmo"
+    modeltype_list[0]=1
+    run_atmo="true"
+  else
+    check_error 1 "No namelist is defined"
+  fi
+fi
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+  restart=.true.
+  #  do not delete switch-file, to enable restart after unintended abort
+  #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+  if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+    sleep 10;
+  fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+  run_atmo="true"
+fi
+#-----------------------------------------------------------------------------
+# get restart files
+
+if  [ x$restart_atmo_from != "x" ] ; then
+  rm -f restart_atm_DOM01.nc
+#  ln -s ${basedir}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+  cp ${basedir}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+  ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+  restart=".true."
+fi
+if  [ x$restart_ocean_from != "x" ] ; then
+  rm -f restart_oce.nc
+#  ln -s ${basedir}/experiments/${restart_from_folder}/${restart_ocean_from} ${EXPDIR}/restart_oce.nc
+  cp ${basedir}/experiments/${restart_from_folder}/${restart_ocean_from} cp_restart_oce_DOM01.nc
+  ln -s cp_restart_oce_DOM01.nc restart_oce_DOM01.nc
+  restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+
+read_restart_namelists=${read_restart_namelists:=".true."}
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart            = $restart
+/
+&master_time_control_nml
+ experimentStartDate  = "$start_date"
+ restartTimeIntval    = "$restart_interval"
+ checkpointTimeIntval = "$checkpoint_interval"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+  calendar='proleptic gregorian'
+  calendar_type=1
+else
+  calendar=$calendar
+  calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart            = $restart
+ read_restart_namelists = $read_restart_namelists
+/
+&master_time_control_nml
+ calendar             = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval    = "$restart_interval"
+ experimentStartDate  = "$start_date"
+ experimentStopDate   = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+  model_namelist_filename="$1"
+  model_name=$2
+  model_type=$3
+  model_min_rank=$4
+  model_max_rank=$5
+  model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+  model_name="$model_name"
+  model_namelist_filename="$model_namelist_filename"
+  model_type=$model_type
+  model_min_rank=$model_min_rank
+  model_max_rank=$model_max_rank
+  model_inc_rank=$model_inc_rank
+/
+EOF
+
+}
+#-----------------------------------------------------------------------------
+
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+  add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+  j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+
+# start experiment
+
+rm -f finish.status
+date
+${START} ${MODEL}
+date
+#
+if [ -r finish.status ] ; then
+  check_final_status 0 "${START} ${MODEL}"
+else
+  check_final_status -1 "${START} ${MODEL}"
+fi
+#
+
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+#   Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ]; then
+  echo "restart next experiment..."
+  this_script="${RUNSCRIPTDIR}/exp.${EXPNAME}.run"
+  echo 'this_script: ' "$this_script"
+  # note that if ${restartSemaphoreFilename} does not exist yet, then touch will create it
+  touch ${restartSemaphoreFilename}
+  cd ${RUNSCRIPTDIR}
+  sbatch exp.${EXPNAME}.run
+else
+  [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+
+cd ${RUNSCRIPTDIR}
+
+#-----------------------------------------------------------------------------
diff --git a/sims/runscripts/LC1-channel-4000x9000km-2km-0002/exp.LC1-channel-4000x9000km-2km-0002.run b/sims/runscripts/LC1-channel-4000x9000km-2km-0002/exp.LC1-channel-4000x9000km-2km-0002.run
new file mode 100644
index 0000000000000000000000000000000000000000..ea4c9a5a0a006475abbd4f09d3e3e9da56ead698
--- /dev/null
+++ b/sims/runscripts/LC1-channel-4000x9000km-2km-0002/exp.LC1-channel-4000x9000km-2km-0002.run
@@ -0,0 +1,718 @@
+#! /bin/bash
+#=============================================================================
+
+# levante cpu batch job parameters
+
+#SBATCH --account=bb1135
+#SBATCH --job-name=LC12km0002
+#SBATCH --partition=compute
+#SBATCH --chdir=/home/b/b381185/simulations/runscripts/LC1-channel-4000x9000km-2km-0001
+#SBATCH --nodes=95
+#SBATCH --threads-per-core=2
+# the following is needed to work around a bug that otherwise leads to
+# a too low number of ranks when using compute,compute2 as queue
+#SBATCH --mem=0
+#SBATCH --output=/home/b/b381185/simulations/runscripts/LC1-channel-4000x9000km-2km-0002/LOG.exp.LC1-channel-4000x9000km-2km-0002.run.%j.o
+#SBATCH --error=/home/b/b381185/simulations/runscripts/LC1-channel-4000x9000km-2km-0002/LOG.exp.LC1-channel-4000x9000km-2km-0002.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=08:00:00
+
+#=============================================================================
+set -x
+ulimit -s unlimited
+#=============================================================================
+# OpenMP environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=$OMP_NUM_THREADS
+export OMP_SCHEDULE="guided"
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=500M
+#
+# MPI variables
+# -------------
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((128 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+#=============================================================================
+# load local setting, if existing
+# -------------------------------
+if [ -a ../setting ]
+then
+  echo "Load Setting"
+  . ../setting
+fi
+
+# environment variables for the experiment and the target system
+# --------------------------------------------------------------
+export KMP_AFFINITY="granularity=fine,scatter"
+export KMP_LIBRARY="turnaround"
+export OMPI_MCA_pml="ucx"
+export OMPI_MCA_btl=self
+export OMPI_MCA_osc="pt2pt"
+export UCX_IB_ADDR_TYPE=ib_global
+export OMPI_MCA_coll="^ml"
+export OMPI_MCA_coll_hcoll_enable="1"
+export HCOLL_ENABLE_MCAST_ALL="1"
+export HCOLL_MAIN_IB=mlx5_0:1
+export UCX_NET_DEVICES=mlx5_0:1
+export UCX_TLS=mm,knem,cma,dc_mlx5,dc_x,self
+export UCX_UNIFIED_MODE=y
+export HDF5_USE_FILE_LOCKING=FALSE
+export OMPI_MCA_io="romio321"
+export MALLOC_TRIM_THRESHOLD_="-1"
+export MKL_ENABLE_INSTRUCTIONS=AVX2
+export MKL_DEBUG_CPU_TYPE=5
+export UCX_HANDLE_ERRORS=bt
+# load profile
+# ------------
+if [[ -a  /etc/profile ]]
+then
+        . /etc/profile
+fi
+#=============================================================================
+# directories with absolute paths
+# -------------------------------
+basedir="/home/b/b381185/icon-on-jet"
+export basedir
+
+# how to start the icon model
+# ---------------------------
+mask="0xf,0xf0000,0xf00000000,0xf000000000000,0xf0000000000000000,0xf00000000000000000000,0xf000000000000000000000000,0xf0000000000000000000000000000,0xf0,0xf00000,0xf000000000,0xf0000000000000,0xf00000000000000000,0xf000000000000000000000,0xf0000000000000000000000000,0xf00000000000000000000000000000,0xf00,0xf000000,0xf0000000000,0xf00000000000000,0xf000000000000000000,0xf0000000000000000000000,0xf00000000000000000000000000,0xf000000000000000000000000000000,0xf000,0xf0000000,0xf00000000000,0xf000000000000000,0xf0000000000000000000,0xf00000000000000000000000,0xf000000000000000000000000000,0xf0000000000000000000000000000000"
+export START="srun -l --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --ntasks=$((no_of_nodes * mpi_procs_pernode)) --propagate=STACK,CORE --distribution=block:block --cpu-bind=v,mask_cpu=$mask "
+export MODEL="${basedir}/bin/icon"
+
+# --------------------------
+submit="sbatch"
+job_name="exp.LC1-channel-4000x9000km-2km-0002.run"
+
+#=============================================================================
+
+ulimit -s $((4 * 1024 * 1024))
+ulimit -c 0
+
+#-----------------------------------------------------------------------------
+export EXPNAME="LC1-channel-4000x9000km-2km-0002"
+
+# atmo namelist filename
+atmo_namelist=NAMELIST_${EXPNAME}_atm
+
+# directories definition
+RUNSCRIPTDIR=/home/b/b381185/simulations/runscripts/LC1-channel-4000x9000km-2km-0002/LC1-channel-4000x9000km-2km-0002/  # run script directory
+
+#-----------------------------------------------------------------------------
+# global timing
+start_date="2022-01-01T00:00:00Z"
+end_date="2022-01-10T00:00:30Z" 
+
+start_date_out="2022-01-01T00:00:30Z"
+end_date_out="2022-01-10T00:00:30Z"
+
+# restart intervals
+checkpoint_interval="P1D"
+restart_interval="P5D"
+
+# output intervals
+output_interval="PT1H"
+file_interval="PT1H"
+
+# regular  grid: nlat=96, nlon=192, npts=18432, dlat=1.875 deg, dlon=1.875 deg
+reg_lat_def_reg=-90.0,1.0,90.0
+reg_lon_def_reg=-180.0,1.0,180.0
+
+#-----------------------------------------------------------------------------
+# model parameters
+model_equations=3             # equation system
+#                     1=hydrost. atm. T
+#                     1=hydrost. atm. theta dp
+#                     3=non-hydrost. atm.,
+#                     0=shallow water model
+#                    -1=hydrost. ocean
+#-----------------------------------------------------------------------------
+
+# experiment directory, with plenty of space, create if new
+
+EXPDIR=/work/bb1135/icon_output/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ;  then
+  mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ;  then
+    mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+# Link experiment initial files
+inputdir='/work/bb1135/from_Mistral/bb1135/b381185/simulation_setup/LC1_Limited_channel/planar_channel_51x81_2km'
+# Grid
+ln -sf $inputdir/Channel_4000x9000_2500m_with_boundary.nc grid_DOM01.nc
+# Extpar
+ln -sf $inputdir/extpar_remapped_12_months.nc extpar_DOM01.nc
+# Initial file
+ln -sf $inputdir/lc1_rh80_remapped.nc ifs2icon_init.nc
+# Ozon
+ln -sf $inputdir/ape_O3_remapped.nc o3_icon_DOM01.nc
+
+# Model required files
+ln -sf $basedir/data/rrtmg_lw.nc              ./
+ln -sf $basedir/data/rrtmg_sw.nc              ./
+ln -sf $basedir/data/ECHAM6_CldOptProps.nc    ./
+ln -sf $basedir/data/dmin_wetgrowth_lookup.nc ./
+
+RADDIR=$basedir/externals/ecrad/data
+ln -sf ${RADDIR}/* .
+#=============================================================================
+
+# create ICON namelist parameters
+
+cat > ${atmo_namelist} << EOF
+&parallel_nml
+ nproma         = 16  ! optimal setting 8 for CRAY; use 16 or 24 for IBM
+ p_test_run     = .false.
+ l_test_openmp  = .false.
+ l_log_checks   = .false.
+ num_io_procs   = 10   ! asynchronous output for values >= 1
+ itype_comm     = 1
+ iorder_sendrecv = 3  ! best value for CRAY (slightly faster than option 1)
+ num_prefetch_proc = 1
+/
+&grid_nml
+ dynamics_grid_filename  = 'grid_DOM01.nc'
+ dynamics_parent_grid_id = 0
+ lredgrid_phys           = .false.
+ is_plane_cylinder       = .TRUE.
+ l_limited_area          = .true.
+ corio_lat               = 45.0
+/
+&initicon_nml
+ init_mode   = 2           ! operation mode 2: IFS
+ ifs2icon_filename = 'ifs2icon_init.nc' 
+ zpbl1       = 500. 
+ zpbl2       = 1000.
+ l_sst_in    = .true. 
+/
+&limarea_nml
+ itype_latbc    = 0            ! fix
+/
+&run_nml
+ num_lev        = 75           ! 90
+ dtime          = 20  ! 360 for R2B5 180 for R2B7    
+ ldynamics      = .TRUE.
+ ltransport     = .true.
+ iforcing       = 3            ! NWP forcing
+ ltestcase      = .false.      ! false: run with real data
+ msg_level      = 20            ! print maximum wind speeds every 5 time steps
+ ltimer         = .false.      ! set .TRUE. for timer output
+ timers_level   = 10            ! can be increased up to 10 for detailed timer output
+ output         = "nml"
+ activate_sync_timers = .True.
+/
+&nwp_phy_nml
+ inwp_gscp          = 4
+ inwp_convection    = 1
+ lshallowconv_only  = .true. ! use only shallow convection scheme
+ inwp_radiation     = 4
+ inwp_cldcover      = 1
+ inwp_turb          = 1
+ inwp_satad         = 1
+ inwp_sso           = 1
+ inwp_gwd           = 1
+ inwp_surface       = 1
+ icapdcycl          = 3
+ latm_above_top  = .false.  ! the second entry refers to the nested domain (if present)
+ efdt_min_raylfric = 7200.
+ ldetrain_conv_prec = .false.
+ itype_z0         = 2
+ icpl_aero_conv   = 0 
+ icpl_aero_gscp   = 0
+ ! resolution-dependent settings - please choose the appropriate one
+ dt_rad    = 600
+ dt_conv   = 300 
+ dt_sso    = 300 
+ dt_gwd    = 300. 
+ lcloudradonly    = .true. ! no radiation temperature tendency contribution i.e no radiation simulation
+ ldiag_ddt_temp_dyn2          = .true.
+/
+&turbdiff_nml
+ tkhmin  = 0.75  
+ tkmmin  = 0.75             
+ pat_len = 100. !750.
+ c_diff  = 0.2
+ rat_sea = 10  
+ ltkesso = .true.
+ frcsmot = 0.2      
+ imode_frcsmot = 2  
+ itype_sher = 3    
+ ltkeshs    = .true.
+ a_hshr     = 2.0     
+/
+&lnd_nml
+ ntiles         = 3      !!! 1 for assimilation cycle and forecast
+ nlev_snow      = 3      !!! 1 for assimilation cycle and forecast
+ lmulti_snow    = .true. !!! .false. for assimilation cycle and forecast
+ itype_heatcond = 2
+ idiag_snowfrac = 2
+ lsnowtile      = .false.  !! later on .true. if GRIB encoding issues are solved
+ lseaice        = .true. !!Sophia Schäfer, 23/03/2017: reads in sea ice (e.g. zero ice), instead of setting by temperature
+ llake          = .false.
+ itype_lndtbl   = 3  ! minimizes moist/cold bias in lower tropical troposphere
+ itype_root     = 2  			        
+/
+&radiation_nml
+ irad_o3       = 4
+ irad_aero     = 0
+ irad_cfc11    = 0
+ irad_cfc12    = 0
+ albedo_type   = 2 ! Modis albedo
+ vmr_co2       = 348.0e-6 ! values representative for 2012
+ vmr_ch4       = 1650.0e-09
+ vmr_n2o       = 396.0e-09
+ vmr_o2        = 0.20946
+ izenith       = 3
+ vmr_cfc11     = 0
+ vmr_cfc12     = 0
+ icld_overlap  = 1 !maxrand overlap
+/
+&nonhydrostatic_nml
+ iadv_rhotheta  = 2
+ ivctype        = 2
+ itime_scheme   = 4
+ exner_expol    = 0.333
+ vwind_offctr   = 0.2
+ damp_height    = 22500.
+ rayleigh_coeff = 0.10   ! R3B7: 1.0 for forecasts, 5.0 in assimilation cycle; 0.5/2.5 for R2B6 (i.e. ensemble) runs
+ lhdiff_rcf     = .true.
+ divdamp_order  = 24    ! setting for forecast runs; use '2' for assimilation cycle
+ divdamp_type   = 32    ! ** new setting for assimilation and forecast runs **
+ divdamp_fac    = 0.004 ! use 0.032 in conjunction with divdamp_order = 2 in assimilation cycle
+ l_open_ubc     = .false.
+ igradp_method  = 3
+ l_zdiffu_t     = .true.
+ thslp_zdiffu   = 0.02
+ thhgtd_zdiffu  = 125.
+ htop_moist_proc= 22500.
+ hbot_qvsubstep = 22500. ! use 22500. with R2B6
+/
+&nudging_nml
+ nudge_type  = 0
+ max_nudge_coeff_vn = 0.00000
+/
+&sleve_nml
+ min_lay_thckn   = 20.
+ max_lay_thckn   = 400.   ! maximum layer thickness below htop_thcknlimit
+ htop_thcknlimit = 14000. ! this implies that the upcoming COSMO-EU nest will have 60 levels
+ top_height      = 30000.
+ stretch_fac     = 0.9
+ decay_scale_1   = 4000.
+ decay_scale_2   = 2500.
+ decay_exp       = 1.2
+ flat_height     = 16000.
+/
+&dynamics_nml
+ iequations     = 3
+ idiv_method    = 1
+ divavg_cntrwgt = 0.50
+ !lcoriolis      = .TRUE.
+/
+&transport_nml
+ ctracer_list  = '12345'
+ ivadv_tracer  = 3,3,3,3,3
+ itype_hlimit  = 3,4,4,4,4
+ ihadv_tracer  = 52,2,2,2,2
+ iadv_tke      = 0
+/
+&diffusion_nml
+ hdiff_order      = 5
+ itype_vn_diffu   = 1
+ itype_t_diffu    = 2
+ hdiff_efdt_ratio = 24.0
+ hdiff_smag_fac   = 0.025
+ lhdiff_vn        = .TRUE.
+ lhdiff_temp      = .TRUE.
+/
+&interpol_nml
+ nudge_zone_width  = 0.0  ! deactivating nudging as needed for mass conservation for this setup 
+ nudge_efold_width = 0.0
+ nudge_max_coeff   = 0.00000
+ lsq_high_ord      = 3
+ l_intp_c2l        = .true.
+ l_mono_c2l        = .true.
+ support_baryctr_intp = .false.
+ rbf_vec_scale_c              = 0.3,  0.1,  0.03
+ rbf_vec_scale_v              = 0.4,  0.25, 0.07
+ rbf_vec_scale_e              = 0.45, 0.37, 0.25
+/
+&extpar_nml
+ itopo          = 1
+ extpar_filename             = 'extpar_DOM01.nc'
+ n_iter_smooth_topo = 1         
+ heightdiff_threshold = 3000.
+/
+&io_nml
+ itype_pres_msl = 5  ! 4 New extrapolation method to circumvent Ninjo problem with surface inversions
+ itype_rh       = 1  ! RH w.r.t. water
+ restart_file_type = 5       ! 4: netcdf2, 5: netcdf4
+/
+&nh_pzlev_nml
+ nplev             = 18     ! number of p level output (attention: levels in [Pa] and top-down)
+ nzlev             = 20     ! number of z level output (attention: levels in [m] and top-down)
+ plevels           = 5000,7000,10000,15000,20000,25000,30000,40000,50000,60000,70000,
+                     80000,85000,90000,92500,95000,97500,100000
+ zlevels           = 25000,20000,18000,16000,14000,12000,10000,8000,6000,5000,4000,3000,2000,
+                     1000,800,600,400,200,100,10
+/
+&output_nml
+ filetype                     =  5                      ! output format: 2=GRIB2, 4=NETCDFv2
+ dom                          =  1         
+ output_start     = "${start_date_out}"
+ output_end       = "${end_date_out}"
+ output_interval  = "${output_interval}"
+ file_interval    = "${file_interval}"
+ include_last                 =  .FALSE.
+ output_filename              = 'icon-fg' ! file name base
+ filename_format  = "<output_filename>_ML_<datetime2>"
+ ml_varlist       = 'group:dwd_fg_atm_vars','group:dwd_fg_sfc_vars'
+ remap            = 0
+ !reg_lon_def                  = ${reg_lon_def_reg}              !-180.,1,179.5
+ !reg_lat_def                  = ${reg_lat_def_reg}              !89.5,-1, -89.5
+/
+&output_nml
+ filetype                     =  5                      ! output format: 2=GRIB2, 4=NETCDFv2
+ dom                          =  1         
+ output_start     = "${start_date_out}"
+ output_end       = "${end_date_out}"
+ output_interval  = "${output_interval}"
+ file_interval    = "${file_interval}"
+ include_last                 =  .FALSE.
+ output_filename              = 'icon-cld3d' ! file name base
+ filename_format  = "<output_filename>_ML_<datetime2>"
+ ml_varlist                   = 'clc','tot_qv_dia','tot_qc_dia','tot_qi_dia','acdnc','reff_qc_ecrad','reff_qi_ecrad'
+ remap            = 0
+ !reg_lon_def                  = ${reg_lon_def_reg}              !-180.,1,179.5
+ !reg_lat_def                  = ${reg_lat_def_reg}              !89.5,-1, -89.5
+/
+&output_nml
+ filetype                     =  5                      ! output format: 2=GRIB2, 4=NETCDFv2
+ dom                          =  1                      ! write all domains
+ output_start     = "${start_date_out}"
+ output_end       = "${end_date_out}"
+ output_interval  = "${output_interval}"
+ file_interval    = "${file_interval}"
+ include_last                 =  .FALSE.
+ output_filename              = 'icon-rad3d' ! file name base
+ filename_format  = "<output_filename>_ML_<datetime2>"
+ ml_varlist                   = 'lwflxall', 'lwflxclr','swflxall','swflxclr'
+ remap                        = 0
+ !reg_lon_def                  = ${reg_lon_def_reg}
+ !reg_lat_def                  = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype                     =  5                      ! output format: 2=GRIB2, 4=NETCDFv2
+ dom                          =  1                      ! write all domains
+ output_start     = "${start_date_out}"
+ output_end       = "${end_date_out}"
+ output_interval  = "${output_interval}"
+ file_interval    = "${file_interval}"
+ include_last                 =  .FALSE.
+ output_filename              = 'icon-atm3d' ! file name base
+ filename_format  = "<output_filename>_ML_<datetime2>"
+ ml_varlist                   = 'vor','pv','omega','geopot','ddt_tke','O3','pres_ifc','rho_ic'
+ remap                        = 0
+ !reg_lon_def                  = ${reg_lon_def_reg}
+ !reg_lat_def                  = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype                     =  5              ! output format: 2=GRIB2, 4=NETCDFv2
+ dom                          =  1                       ! write all domains
+ output_start     = "${start_date_out}"
+ output_end       = "${end_date_out}"
+ output_interval  = "${output_interval}"
+ file_interval    = "${file_interval}"
+ include_last                 = .FALSE.
+ output_filename              = 'icon-rad2d'   ! file name base
+ filename_format  = "<output_filename>_ML_<datetime2>"
+ ml_varlist                   = 'sob_t','thb_t','sobclr_t','thbclr_t','sob_s','thb_s','thbclr_s','sobclr_s','tsfctrad'
+ remap                        = 0
+ !reg_lon_def                  = ${reg_lon_def_reg}
+ !reg_lat_def                  = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype                     =  5                      ! output format: 2=GRIB2, 4=NETCDFv2
+ dom                          =  1                      ! write all domains
+ output_start     = "${start_date_out}"
+ output_end       = "${end_date_out}"
+ output_interval  = "${output_interval}"
+ file_interval    = "${file_interval}"
+ include_last                 =  .FALSE.
+ output_filename              = 'icon-ddt_temp' ! file name base
+ filename_format  = "<output_filename>_ML_<datetime2>"
+ ml_varlist                   = 'ddt_temp_radswnw','ddt_temp_radlwnw','ddt_temp_radswcs','ddt_temp_radlwcs','ddt_temp_turb','ddt_temp_pconv','ddt_temp_dyn','ddt_temp_gscp','ddt_temp_mphy','ddt_temp_diff','ddt_temp_drag'
+ remap                        = 0
+ !reg_lon_def                  = ${reg_lon_def_reg}
+ !reg_lat_def                  = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype                     =  5                      ! output format: 2=GRIB2, 4=NETCDFv2
+ dom                          =  1                      ! write all domains
+ output_start     = "${start_date_out}"
+ output_end       = "${end_date_out}"
+ output_interval  = "${output_interval}"
+ file_interval    = "${file_interval}"
+ include_last                 =  .FALSE.
+ output_filename              = 'icon-ddt_wind' ! file name base
+ filename_format  = "<output_filename>_ML_<datetime2>"
+ ml_varlist                   = 'ddt_u_gwd','ddt_u_pconv','ddt_u_sso','ddt_u_turb','ddt_v_gwd','ddt_v_pconv','ddt_v_sso','ddt_v_turb','ddt_temp_dyn2'
+ remap                        = 0
+ !reg_lon_def                  = ${reg_lon_def_reg}
+ !reg_lat_def                  = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype                     =  5              ! output format: 2=GRIB2, 4=NETCDFv2
+ dom                          =  1                       ! write all domains
+ output_start     = "${start_date_out}"
+ output_end       = "${end_date_out}"
+ output_interval  = "${output_interval}"
+ file_interval    = "${file_interval}"
+ include_last                 = .FALSE.
+ output_filename              = 'icon-atm2d'   ! file name base
+ filename_format  = "<output_filename>_ML_<datetime2>"
+ ml_varlist                   = 'pres_msl','pres_sfc','group:precip_vars','rain_gsp_rate','rain_con_rate','cape_ml','cin_ml','clct','clch','clcm','clcl','tqv_dia','tqc_dia','tqi_dia','shfl_s','lhfl_s','w_so'
+ remap                        = 0
+ !reg_lon_def                  = ${reg_lon_def_reg}
+ !reg_lat_def                  = ${reg_lat_def_reg}
+/
+&output_nml
+ output_filename  = "icon-extra"
+ filename_format  = "<output_filename>_<levtype_l>"
+ filetype         = 5
+ remap            = 0
+ output_grid      = .TRUE.
+ output_start     = "${start_date}"          ! output_start = output_end
+ output_end       = "${start_date}"          ! --> write once only irrespective of
+ output_interval  = "PT1H"  !     the output interval and
+ file_interval    = "${file_interval}"    !     the file interval
+ ml_varlist       = 'z_mc'
+/
+EOF
+
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration. 
+#-----------------------------------------------------------------------------
+
+final_status_file=${EXPDIR}/${job_name}.final_status #!NICOLE
+rm -f ${final_status_file}
+#-----------------------------------------------------------------------------
+
+# set up the model lists if they do not exist
+# this works for single model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+#  minrank_list=(        0           )
+#  maxrank_list=(     65535          )
+#  incrank_list=(        1           )
+  minrank_list[0]=0
+  maxrank_list[0]=65535
+  incrank_list[0]=1
+  if [ x$atmo_namelist != x ]; then
+    # this is the atmo model
+    namelist_list[0]="$atmo_namelist"
+    modelname_list[0]="atmo"
+    modeltype_list[0]=1
+    run_atmo="true"
+  else
+    check_error 1 "No namelist is defined"
+  fi
+fi
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".true."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+  restart=.true.
+  #  do not delete switch-file, to enable restart after unintended abort
+  #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+  if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+    sleep 10;
+  fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+  run_atmo="true"
+fi
+#-----------------------------------------------------------------------------
+# get restart files
+
+if  [ x$restart_atmo_from != "x" ] ; then
+  rm -f restart_atm_DOM01.nc
+#  ln -s ${basedir}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+  cp ${basedir}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+  ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+  restart=".true."
+fi
+if  [ x$restart_ocean_from != "x" ] ; then
+  rm -f restart_oce.nc
+#  ln -s ${basedir}/experiments/${restart_from_folder}/${restart_ocean_from} ${EXPDIR}/restart_oce.nc
+  cp ${basedir}/experiments/${restart_from_folder}/${restart_ocean_from} cp_restart_oce_DOM01.nc
+  ln -s cp_restart_oce_DOM01.nc restart_oce_DOM01.nc
+  restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+
+read_restart_namelists=${read_restart_namelists:=".true."}
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart            = $restart
+/
+&master_time_control_nml
+ experimentStartDate  = "$start_date"
+ restartTimeIntval    = "$restart_interval"
+ checkpointTimeIntval = "$checkpoint_interval"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+  calendar='proleptic gregorian'
+  calendar_type=1
+else
+  calendar=$calendar
+  calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart            = $restart
+ read_restart_namelists = $read_restart_namelists
+/
+&master_time_control_nml
+ calendar             = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval    = "$restart_interval"
+ experimentStartDate  = "$start_date"
+ experimentStopDate   = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+  model_namelist_filename="$1"
+  model_name=$2
+  model_type=$3
+  model_min_rank=$4
+  model_max_rank=$5
+  model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+  model_name="$model_name"
+  model_namelist_filename="$model_namelist_filename"
+  model_type=$model_type
+  model_min_rank=$model_min_rank
+  model_max_rank=$model_max_rank
+  model_inc_rank=$model_inc_rank
+/
+EOF
+
+}
+#-----------------------------------------------------------------------------
+
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+  add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+  j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+
+# start experiment
+
+rm -f finish.status
+date
+${START} ${MODEL}
+date
+#
+if [ -r finish.status ] ; then
+  check_final_status 0 "${START} ${MODEL}"
+else
+  check_final_status -1 "${START} ${MODEL}"
+fi
+#
+
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+#   Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ]; then
+  echo "restart next experiment..."
+  this_script="${RUNSCRIPTDIR}/exp.${EXPNAME}.run"
+  echo 'this_script: ' "$this_script"
+  # note that if ${restartSemaphoreFilename} does not exist yet, then touch will create it
+  touch ${restartSemaphoreFilename}
+  cd ${RUNSCRIPTDIR}
+  sbatch exp.${EXPNAME}.run
+else
+  [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+
+cd ${RUNSCRIPTDIR}
+
+#-----------------------------------------------------------------------------
diff --git a/sims/runscripts/README.md b/sims/runscripts/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..28fbf3289894f190b85484dfed21386f020e4684
--- /dev/null
+++ b/sims/runscripts/README.md
@@ -0,0 +1,13 @@
+** ICON simulations**
+
+*Baroclinic life cycle simulation with ICON-NWP**
+
+LC1-channel-4000x9000km-2km-0002        : Only with cloud radiaton, two-moment microphysics, and ecrad
+
+*Large eddy model simulations with ICON-LEM*
+
+LC1-LES-471x667km-lon40-lat44-300m-0003 : Only with cloud radiaton, two-moment microphysics, ecrad, LATBC: NWP_0002
+LC1-LES-471x667km-lon50-lat48-300m-0004 : Only with cloud radiaton, two-moment microphysics, ecrad, LATBC: NWP_0002
+LC1-LES-471x667km-lon30-lat53-300m-0005 : Only with cloud radiaton, two-moment microphysics, ecrad, LATBC: NWP_0002
+LC1-LES-471x667km-lon25-lat40-300m-0006 : Only with cloud radiaton, two-moment microphysics, ecrad, LATBC: NWP_0002
+