diff --git a/Scripts_for_preprocessing/README b/Scripts_for_preprocessing/README
new file mode 100644
index 0000000000000000000000000000000000000000..6866f77b58dd5a8463d9e82c9d89e49cd5eb927e
--- /dev/null
+++ b/Scripts_for_preprocessing/README
@@ -0,0 +1 @@
+!! Preprocessing, i.e. preparation of initial and boundary conditions was performed on Mistral !!
diff --git a/Scripts_for_preprocessing/calc_tropopause_initial_conditions.ncl b/Scripts_for_preprocessing/calc_tropopause_initial_conditions.ncl
new file mode 100644
index 0000000000000000000000000000000000000000..eeb575e394b76f392545aa8c6b89e7ad20f8cf10
--- /dev/null
+++ b/Scripts_for_preprocessing/calc_tropopause_initial_conditions.ncl
@@ -0,0 +1,54 @@
+begin
+;************************************************
+; open file and read in data
+;************************************************
+; prepare input with cdo:
+; cdo ml2pl,100,500,1000,2000,3000,5000,7000,8000,8500,9000,9500,10000,12500,15000,17500,20000,22500,25000,27500,30000,40000,50000,60000,70000,80000,85000,92500,100000 lc1_initialcondition_CTL_r10x360_with_ps.nc lc1_initialcondition_CTL.pl.nc
+
+ DataFileName = "lc1_initialcondition_CTL.pl.nc"
+ in = addfile(DataFileName,"r")
+ t = in->T(0,:,:,0)
+ lev = in->plev(:)
+ lat = in->lat(:)
+
+ printVarSummary(t)
+
+;************************************************
+; calculate tropopause
+;************************************************
+ ; Invert variables, pressure must be monotonically *increasing*
+ ; which is not the case in MPI-ESM1-2-LR
+ ;t_trans = transpose(t(::-1,:))
+ ;lev_trans = lev(::-1)
+ ;printVarSummary(t_trans)
+
+ ; The order of the dimensions is different from what trop_wmo is
+ ; expecting -> change the dimension to (lat,lev) instead of
+ ; (lev,lat)
+ ;ptrop = trop_wmo(lev_trans, t_trans(lat|:,plev|:), 1, False)
+ ptrop = trop_wmo(lev, t(lat|:,plev|:), 1, False)
+
+ ;print(ptrop)
+
+ ptrop!0 ="lat"
+; ptrop!1 ="lon"
+ ptrop&lat = t&lat
+; ptrop&lon = t&lon
+ printVarSummary(ptrop)
+;************************************************
+; save to netcdf file
+;************************************************
+ system("/bin/rm -f lc1_initialcondition_CTL_tropopause_zm.nc") ; remove any pre-existing file
+ ncdf = addfile("lc1_initialcondition_CTL_tropopause_zm.nc" ,"c") ; open output netCDF file
+
+ ;===================================================================
+ ; make time an UNLIMITED dimension; recommended for most applications
+ ;===================================================================
+ filedimdef(ncdf,"time",-1,True)
+
+ ;===================================================================
+ ; output variables directly; NCL will call appropriate functions
+ ; to write the meta data associated with each variable
+ ;===================================================================
+ ncdf->ptrop = ptrop
+end
diff --git a/Scripts_for_preprocessing/calc_tropopause_mpi-esm1-2-lr.ncl b/Scripts_for_preprocessing/calc_tropopause_mpi-esm1-2-lr.ncl
new file mode 100644
index 0000000000000000000000000000000000000000..5d1dddbbbb13afa613ddafad8b0bfc937c6c68f8
--- /dev/null
+++ b/Scripts_for_preprocessing/calc_tropopause_mpi-esm1-2-lr.ncl
@@ -0,0 +1,61 @@
+begin
+;************************************************
+; open file and read in data
+;************************************************
+; prepare the input file with get_Tanom_from_MPI-ESM_plev.sh
+
+ ;DataFileName = "clim3D_Amon_MPI-ESM1-2-LR_historical_r1i1p1f1_1980-81to2009-10.DJFmean.90w40e20n80n.zonmean.nc"
+ ;DataFileName = "clim3D_Amon_MPI-ESM1-2-LR_ssp585_r1i1p1f1_2020-21to2049-50.DJFmean.90w40e20n80n.zonmean.nc"
+ DataFileName = "clim3D_Amon_MPI-ESM1-2-LR_ssp585_r1i1p1f1_2070-71to2099-00.DJFmean.90w40e20n80n.zonmean.nc"
+ in = addfile(DataFileName,"r")
+ t = in->ta(0,:,:,0)
+ lev = in->plev(:)
+ lat = in->lat(:)
+
+ printVarSummary(t)
+
+;************************************************
+; calculate tropopause
+;************************************************
+ ; Invert variables, pressure must be monotonically *increasing*
+ ; which is not the case in MPI-ESM1-2-LR
+ t_trans = transpose(t(::-1,:))
+ lev_trans = lev(::-1)
+ printVarSummary(t_trans)
+
+ ; The order of the dimensions is different from what trop_wmo is
+ ; expecting -> change the dimension to (lat,lev) instead of
+ ; (lev,lat)
+ ptrop = trop_wmo(lev_trans, t_trans(lat|:,plev|:), 1, False)
+
+ ;print(ptrop)
+
+ ptrop!0 ="lat"
+; ptrop!1 ="lon"
+ ptrop&lat = t&lat
+; ptrop&lon = t&lon
+ printVarSummary(ptrop)
+;************************************************
+; save to netcdf file
+;************************************************
+ ; historical
+ ;system("/bin/rm -f clim3D_Amon_MPI-ESM1-2-LR_historical_tropopause_zonmean.nc") ; remove any pre-existing file
+ ;ncdf = addfile("clim3D_Amon_MPI-ESM1-2-LR_historical_tropopause_zonmean.nc" ,"c") ; open output netCDF file
+ ; near future
+ ;system("/bin/rm -f clim3D_Amon_MPI-ESM1-2-LR_ssp585_nearfuture_tropopause_zonmean.nc") ; remove any pre-existing file
+ ;ncdf = addfile("clim3D_Amon_MPI-ESM1-2-LR_ssp585_nearfuture_tropopause_zonmean.nc" ,"c") ; open output netCDF file
+ ; far future
+ system("/bin/rm -f clim3D_Amon_MPI-ESM1-2-LR_ssp585_farfuture_tropopause_zonmean.nc") ; remove any pre-existing file
+ ncdf = addfile("clim3D_Amon_MPI-ESM1-2-LR_ssp585_farfuture_tropopause_zonmean.nc" ,"c") ; open output netCDF file
+
+ ;===================================================================
+ ; make time an UNLIMITED dimension; recommended for most applications
+ ;===================================================================
+ filedimdef(ncdf,"time",-1,True)
+
+ ;===================================================================
+ ; output variables directly; NCL will call appropriate functions
+ ; to write the meta data associated with each variable
+ ;===================================================================
+ ncdf->ptrop = ptrop
+end
diff --git a/Scripts_for_preprocessing/generate_initialdata/01a_limited_channel_setup_2km_initialconditions.sh b/Scripts_for_preprocessing/generate_initialdata/01a_limited_channel_setup_2km_initialconditions.sh
new file mode 100644
index 0000000000000000000000000000000000000000..bee3d3bd93ed47886b9a5b81a110681239b22fd2
--- /dev/null
+++ b/Scripts_for_preprocessing/generate_initialdata/01a_limited_channel_setup_2km_initialconditions.sh
@@ -0,0 +1,422 @@
+#!/bin/bash
+#=============================================================================
+#SBATCH --account=bb1152
+#SBATCH --job-name=remap_ini
+#SBATCH --partition=compute
+#SBATCH --nodes=2
+#SBATCH --threads-per-core=2
+#SBATCH --mem=120000
+#SBATCH --output=/work/bb1152/Module_A/A6_CyclEx/b380490_Albern/input_data/LOG.remap_initial_conditions.%j.o
+#SBATCH --error=/work/bb1152/Module_A/A6_CyclEx/b380490_Albern/input_data/LOG.remap_initial_conditions.%j.o
+#SBATCH --exclusive
+#SBATCH --time=01:00:00
+#=========================================================================================
+# This script prepares the initial files for a baroclinic life cycle simulation using
+# DWD ICONTOOLS.
+#=========================================================================================
+#-----------------------------------------------------------------------------------------
+# Introduce switches to decide what the script will do.
+# Switches can be set to "yes" or "no".
+
+# create new folder "inputs_planar_channel_51x81_2km"
+makefolder=yes
+
+# run python scripts to generate initial files for control simulation and/or +4K simulation.
+# control simulation
+ini_python_ctl=no#yes
+# +4K uniform temperature increase, qv consistent with temperature
+ini_python_4k=no#yes
+# +4K uniform temperature increase, qv from control simulation
+ini_python_4k_qvctl=no#yes
+
+# run python script to generate the temperature perturbation
+tpert_python=no#yes
+
+# remap initial files from python scripts to channel grid
+remap_ini_python_ctl=yes
+remap_ini_python_4k=yes
+remap_ini_python_4k_qvctl=yes
+
+# remap initial data from IFS to channel grid
+remap_ifs=yes
+
+# merge remapped initial files
+merge_python_ifs_ctl=yes
+merge_python_ifs_4k=yes
+merge_python_ifs_4k_qvctl=yes
+
+#-----------------------------------------------------------------------------------------
+# Load modules
+
+module purge
+module load anaconda3/bleeding_edge
+module load nco/4.7.5-gcc64
+module load ncl/6.5.0-gccsys
+
+#--------------------------------------------------------------------------------------
+# ICONTOOLS directory
+
+#ICONTOOLS_DIR=/work/bb1018/b380490/icontools-2.1.0/icontools
+ICONTOOLS_DIR=/work/bb1152/Module_A/A6_CyclEx/b380490_Albern/icontools-2.1.0/icontools
+
+BINARY_ICONSUB=iconsub_mpi
+BINARY_REMAP=iconremap_mpi
+BINARY_GRIDGEN=icongridgen
+
+#--------------------------------------------------------------------------------------
+# move to folder "input_data"
+cd ../../../input_data
+
+#--------------------------------------------------------------------------------------
+# file with channel grid
+
+gridfile=Channel_4000x9000_2500m_with_boundary.nc
+
+#cp /work/bb1135/b381185/tools/GridGenerator_master/grids/$gridfile ./
+
+#--------------------------------------------------------------------------------------
+# make folder in which the interpolated data will be stored
+if [[ "$makefolder" == "yes" ]]; then
+ mkdir inputs_planar_channel_51x81_2km
+fi
+
+#--------------------------------------------------------------------------------------
+#--------------------------------------------------------------------------------------
+# 1- Generate initial conditions for baroclinic life cycle 1
+
+# path to python scripts
+pypath='../scripts_gitrepo/preprocessing_scripts'
+
+# 1.1 initial conditions
+if [[ "$ini_python_ctl" == "yes" ]]; then
+ echo "#####################################################"
+ echo "Generate initial conditions (CTL)"
+ echo "#####################################################"
+ # run python script
+ python $pypath/lc1_initial_condition_fixedCoriolisParameter_CTL.py
+ # extend initial data to 720x360 grid
+ cdo remapbil,r720x360 -setgrid,r10x360 lc1_initialcondition_CTL_r10x360.nc lc1_initialcondition_CTL_r720x360.nc
+ # remove output from python script
+ rm lc1_initialcondition_CTL_r10x360.nc
+fi
+
+if [[ "$ini_python_4k" == "yes" ]]; then
+ echo ""
+ echo "#####################################################"
+ echo "Generate initial conditions (4K)"
+ echo "#####################################################"
+ # run python script
+ python $pypath/lc1_initial_condition_fixedCoriolisParameter_4K.py
+ # extend initial data to 720x360 grid
+ cdo remapbil,r720x360 -setgrid,r10x360 lc1_initialcondition_4K_r10x360.nc lc1_initialcondition_4K_r720x360.nc
+ # remove output from python script
+ rm lc1_initialcondition_4K_r10x360.nc
+fi
+
+if [[ "$ini_python_4k_qvctl" == "yes" ]]; then
+ echo ""
+ echo "#####################################################"
+ echo "Generate initial conditions (4K, qv from CTL)"
+ echo "#####################################################"
+ # run python script
+ python $pypath/lc1_initial_condition_fixedCoriolisParameter_4K_qvfromcontrol.py
+ # extend initial data to 720x360 grid
+ cdo remapbil,r720x360 -setgrid,r10x360 lc1_initialcondition_4K_qvCTL_r10x360.nc lc1_initialcondition_4K_qvCTL_r720x360.nc
+ # remove output from python script
+ rm lc1_initialcondition_4K_qvCTL_r10x360.nc
+fi
+
+# 1.2 temperature perturbation
+if [[ "$tpert_python" == "yes" ]]; then
+ echo ""
+ echo "#####################################################"
+ echo "Generate temperature perturbation"
+ echo "#####################################################"
+ python $pypath/lc1_perturbation_condition_wavenumber7.py
+fi
+
+# 1.3 add temperature perturbation to temperature field
+if [[ "$ini_python_ctl" == "yes" ]]; then
+ echo ""
+ echo "#####################################################"
+ echo "Merge initial file and temperature perturbation (CTL)"
+ echo "#####################################################"
+ cdo merge -delvar,T lc1_initialcondition_CTL_r720x360.nc -add -selvar,T lc1_initialcondition_CTL_r720x360.nc lc1_tperturb_720x360.nc lc1_CTL_r720x360.nc
+ # remove temporary data
+ rm lc1_initialcondition_CTL_r720x360.nc
+fi
+
+if [[ "$ini_python_4k" == "yes" ]]; then
+ echo ""
+ echo "#####################################################"
+ echo "Merge initial file and temperature perturbation (4K)"
+ echo "#####################################################"
+ cdo merge -delvar,T lc1_initialcondition_4K_r720x360.nc -add -selvar,T lc1_initialcondition_4K_r720x360.nc lc1_tperturb_720x360.nc lc1_4K_r720x360.nc
+ # remove temporary data
+ rm lc1_initialcondition_4K_r720x360.nc
+fi
+
+if [[ "$ini_python_4k_qvctl" == "yes" ]]; then
+ echo ""
+ echo "##################################################################"
+ echo "Merge initial file and temperature perturbation (4K, qv from CTL)"
+ echo "##################################################################"
+ cdo merge -delvar,T lc1_initialcondition_4K_qvCTL_r720x360.nc -add -selvar,T lc1_initialcondition_4K_qvCTL_r720x360.nc lc1_tperturb_720x360.nc lc1_4K_qvCTL_r720x360.nc
+ # remove temporary data
+ rm lc1_initialcondition_4K_qvCTL_r720x360.nc
+fi
+
+## remove temperature perturbation file
+#rm lc1_tperturb_720x360.nc
+
+#--------------------------------------------------------------------------------------
+#--------------------------------------------------------------------------------------
+# 2- Remap the lc1 initial file onto the channel grid using DWD ICONTOOLS
+
+cd inputs_planar_channel_51x81_2km
+
+# NOTE: do not use indent for "EOF"; otherwise the calculation terminates with an error
+if [[ "$remap_ini_python_ctl" == "yes" ]]; then
+ echo ""
+ echo "#####################################################"
+ echo "Remap initial file (CTL) onto channel grid"
+ echo "#####################################################"
+
+ for field in U V W QV QC QI SST LNPS GEOP_SFC GEOP_ML T ; do
+
+ cat >> NAMELIST_ICONREMAP_FIELDS << EOF
+ !
+ &input_field_nml
+ inputname = "${field}"
+ outputname = "${field}"
+ intp_method = 3
+ /
+EOF
+
+ done
+
+ #cat NAMELIST_ICONREMAP_FIELDS
+
+ cat > NAMELIST_ICONREMAP << EOF
+ &remap_nml
+ in_grid_filename = ''
+ in_filename = '../lc1_CTL_r720x360.nc'
+ in_type = 1
+ out_grid_filename = '../${gridfile}'
+ out_filename = 'lc1_CTL_remapped.nc'
+ out_type = 2
+ out_filetype = 4
+ l_have3dbuffer = .false.
+ ncstorage_file = "ncstorage.tmp"
+ /
+EOF
+
+ # run DWD ICONTOOLS
+ ${ICONTOOLS_DIR}/${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
+ #rm -f ../lc1_CTL_r720x360.nc
+fi
+
+if [[ "$remap_ini_python_4k" == "yes" ]]; then
+ echo ""
+ echo "#####################################################"
+ echo "Remap initial file (4K) onto channel grid"
+ echo "#####################################################"
+
+ for field in U V W QV QC QI SST LNPS GEOP_SFC GEOP_ML T ; do
+
+ cat >> NAMELIST_ICONREMAP_FIELDS << EOF
+ !
+ &input_field_nml
+ inputname = "${field}"
+ outputname = "${field}"
+ intp_method = 3
+ /
+EOF
+
+ done
+
+ #cat NAMELIST_ICONREMAP_FIELDS
+
+ cat > NAMELIST_ICONREMAP << EOF
+ &remap_nml
+ in_grid_filename = ''
+ in_filename = '../lc1_4K_r720x360.nc'
+ in_type = 1
+ out_grid_filename = '../${gridfile}'
+ out_filename = 'lc1_4K_remapped.nc'
+ out_type = 2
+ out_filetype = 4
+ l_have3dbuffer = .false.
+ ncstorage_file = "ncstorage.tmp"
+ /
+EOF
+
+ # run DWD ICONTOOLS
+ ${ICONTOOLS_DIR}/${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
+ #rm -f ../lc1_4K_r720x360.nc
+fi
+
+if [[ "$remap_ini_python_4k_qvctl" == "yes" ]]; then
+ echo ""
+ echo "#######################################################"
+ echo "Remap initial file (4K, qv from CTL) onto channel grid"
+ echo "#######################################################"
+
+ for field in U V W QV QC QI SST LNPS GEOP_SFC GEOP_ML T ; do
+
+ cat >> NAMELIST_ICONREMAP_FIELDS << EOF
+ !
+ &input_field_nml
+ inputname = "${field}"
+ outputname = "${field}"
+ intp_method = 3
+ /
+EOF
+
+ done
+
+ #cat NAMELIST_ICONREMAP_FIELDS
+
+ cat > NAMELIST_ICONREMAP << EOF
+ &remap_nml
+ in_grid_filename = ''
+ in_filename = '../lc1_4K_qvCTL_r720x360.nc'
+ in_type = 1
+ out_grid_filename = '../${gridfile}'
+ out_filename = 'lc1_4K_qvCTL_remapped.nc'
+ out_type = 2
+ out_filetype = 4
+ l_have3dbuffer = .false.
+ ncstorage_file = "ncstorage.tmp"
+ /
+EOF
+
+ # run DWD ICONTOOLS
+ ${ICONTOOLS_DIR}/${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
+ #rm -f ../lc1_4K_qvCTL_r720x360.nc
+fi
+
+#----------------------------------------------------------------------------
+#----------------------------------------------------------------------------
+# 3- Remap the initial data from IFS for a zonally-symmetric aquaplanet to
+# the channel grid.
+
+if [[ "$remap_ifs" == "yes" ]]; then
+ echo ""
+ echo "#####################################################"
+ echo "Remap ifs initial file onto channel grid"
+ echo "#####################################################"
+
+ for field in T_SNOW W_SNOW RHO_SNOW ALB_SNOW SKT STL1 STL2 STL3 STL4 CI W_I Z0 LSM SMIL1 SMIL2 SMIL3 SMIL4 ; do
+
+ cat >> NAMELIST_ICONREMAP_FIELDS << EOF
+ !
+ &input_field_nml
+ inputname = "${field}"
+ outputname = "${field}"
+ intp_method = 3
+ /
+EOF
+
+ done
+
+ cat > NAMELIST_ICONREMAP << EOF
+ &remap_nml
+ in_grid_filename = '../inputs_originalgrid/icon_grid_0002_R02B06_G.nc'
+ in_filename = '../inputs_originalgrid/ifs2icon_0002_R02B06_aquaplanet.nc'
+ in_type = 2
+ out_grid_filename = '../${gridfile}'
+ out_filename = 'ifs2icon_remapped.nc'
+ out_type = 2
+ out_filetype = 4
+ l_have3dbuffer = .false.
+ ncstorage_file = "ncstorage.tmp"
+ /
+EOF
+
+ # run DWD ICONTOOLS
+ ${ICONTOOLS_DIR}/${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
+fi
+
+#----------------------------------------------------------------------------
+#----------------------------------------------------------------------------
+# 4- Construct the complete initial file by adding the remapped output from
+# the python script and the remapped IFS data.
+
+echo ""
+echo "#####################################################"
+
+if [[ "$merge_python_ifs_ctl" == "yes" ]]; then
+ echo "Merge remapped initial files (CTL)"
+
+ ncks -v T_SNOW,W_SNOW,RHO_SNOW,ALB_SNOW,SKT,STL1,STL2,STL3,STL4,CI,W_I,Z0,LSM,SMIL1,SMIL2,SMIL3,SMIL4 ifs2icon_remapped.nc temp1.nc
+
+ ncks -A lc1_CTL_remapped.nc temp1.nc
+
+ mv temp1.nc lc1_ifs_CTL_remapped.nc
+fi
+
+if [[ "$merge_python_ifs_4k" == "yes" ]]; then
+ echo "Merge remapped initial files (4K)"
+
+ ncks -v T_SNOW,W_SNOW,RHO_SNOW,ALB_SNOW,SKT,STL1,STL2,STL3,STL4,CI,W_I,Z0,LSM,SMIL1,SMIL2,SMIL3,SMIL4 ifs2icon_remapped.nc temp1.nc
+
+ ncks -A lc1_4K_remapped.nc temp1.nc
+
+ mv temp1.nc lc1_ifs_4K_remapped.nc
+fi
+
+if [[ "$merge_python_ifs_4k_qvctl" == "yes" ]]; then
+ echo "Merge remapped initial files (4K, qv from CTL)"
+
+ ncks -v T_SNOW,W_SNOW,RHO_SNOW,ALB_SNOW,SKT,STL1,STL2,STL3,STL4,CI,W_I,Z0,LSM,SMIL1,SMIL2,SMIL3,SMIL4 ifs2icon_remapped.nc temp1.nc
+
+ ncks -A lc1_4K_qvCTL_remapped.nc temp1.nc
+
+ mv temp1.nc lc1_ifs_4K_qvCTL_remapped.nc
+fi
+
+#----------------------------------------------------------------------------
+#----------------------------------------------------------------------------
+# 5- Clean-up: remove temporary files
+#if [[ "$remap_ini_python_ctl" == "yes" ]]; then
+# rm lc1_CTL_remapped.nc
+#fi
+#
+#if [[ "$remap_ini_python_4k" == "yes" ]]; then
+# rm lc1_4K_remapped.nc
+#fi
+#
+#if [[ "$remap_ini_python_4k_qvctl" == "yes" ]]; then
+# rm lc1_4K_qvCTL_remapped.nc
+#fi
+#
+#if [[ "$remap_ifs" == "yes" ]]; then
+# rm ifs2icon_remapped.nc
+#fi
+
+#
diff --git a/Scripts_for_preprocessing/generate_initialdata/01a_limited_channel_setup_80km_initialconditions.sh b/Scripts_for_preprocessing/generate_initialdata/01a_limited_channel_setup_80km_initialconditions.sh
new file mode 100644
index 0000000000000000000000000000000000000000..dc4ec6161fd778300d4a9e46830013f7528bc35d
--- /dev/null
+++ b/Scripts_for_preprocessing/generate_initialdata/01a_limited_channel_setup_80km_initialconditions.sh
@@ -0,0 +1,422 @@
+#!/bin/bash
+#=============================================================================
+#SBATCH --account=bb1152
+#SBATCH --job-name=remap_ini
+#SBATCH --partition=compute
+#SBATCH --nodes=2
+#SBATCH --threads-per-core=2
+#SBATCH --mem=120000
+#SBATCH --output=/work/bb1152/Module_A/A6_CyclEx/b380490_Albern/input_data/LOG.remap_initial_conditions.%j.o
+#SBATCH --error=/work/bb1152/Module_A/A6_CyclEx/b380490_Albern/input_data/LOG.remap_initial_conditions.%j.o
+#SBATCH --exclusive
+#SBATCH --time=01:00:00
+#=========================================================================================
+# This script prepares the initial files for a baroclinic life cycle simulation using
+# DWD ICONTOOLS.
+#=========================================================================================
+#-----------------------------------------------------------------------------------------
+# Introduce switches to decide what the script will do.
+# Switches can be set to "yes" or "no".
+
+# create new folder "inputs_planar_channel_51x81_80km"
+makefolder=yes
+
+# run python scripts to generate initial files for control simulation and/or +4K simulation.
+# control simulation
+ini_python_ctl=yes
+# +4K uniform temperature increase, qv consistent with temperature
+ini_python_4k=yes
+# +4K uniform temperature increase, qv from control simulation
+ini_python_4k_qvctl=yes
+
+# run python script to generate the temperature perturbation
+tpert_python=yes
+
+# remap initial files from python scripts to channel grid
+remap_ini_python_ctl=yes
+remap_ini_python_4k=yes
+remap_ini_python_4k_qvctl=yes
+
+# remap initial data from IFS to channel grid
+remap_ifs=yes
+
+# merge remapped initial files
+merge_python_ifs_ctl=yes
+merge_python_ifs_4k=yes
+merge_python_ifs_4k_qvctl=yes
+
+#-----------------------------------------------------------------------------------------
+# Load modules
+
+module purge
+module load anaconda3/bleeding_edge
+module load nco/4.7.5-gcc64
+module load ncl/6.5.0-gccsys
+
+#--------------------------------------------------------------------------------------
+# ICONTOOLS directory
+
+#ICONTOOLS_DIR=/work/bb1018/b380490/icontools-2.1.0/icontools
+ICONTOOLS_DIR=/work/bb1152/Module_A/A6_CyclEx/b380490_Albern/icontools-2.1.0/icontools
+
+BINARY_ICONSUB=iconsub_mpi
+BINARY_REMAP=iconremap_mpi
+BINARY_GRIDGEN=icongridgen
+
+#--------------------------------------------------------------------------------------
+# move to folder "input_data"
+cd ../../../input_data
+
+#--------------------------------------------------------------------------------------
+# file with channel grid
+
+gridfile=Channel_4000x9000_80000m_with_boundary.nc
+
+#cp /work/bb1135/b381185/tools/GridGenerator_master/grids/$gridfile ./
+
+#--------------------------------------------------------------------------------------
+# make folder in which the interpolated data will be stored
+if [[ "$makefolder" == "yes" ]]; then
+ mkdir inputs_planar_channel_51x81_80km
+fi
+
+#--------------------------------------------------------------------------------------
+#--------------------------------------------------------------------------------------
+# 1- Generate initial conditions for baroclinic life cycle 1
+
+# path to python scripts
+pypath='../scripts_gitrepo/preprocessing_scripts'
+
+# 1.1 initial conditions
+if [[ "$ini_python_ctl" == "yes" ]]; then
+ echo "#####################################################"
+ echo "Generate initial conditions (CTL)"
+ echo "#####################################################"
+ # run python script
+ python $pypath/lc1_initial_condition_fixedCoriolisParameter_CTL.py
+ # extend initial data to 720x360 grid
+ cdo remapbil,r720x360 -setgrid,r10x360 lc1_initialcondition_CTL_r10x360.nc lc1_initialcondition_CTL_r720x360.nc
+ # remove output from python script
+ rm lc1_initialcondition_CTL_r10x360.nc
+fi
+
+if [[ "$ini_python_4k" == "yes" ]]; then
+ echo ""
+ echo "#####################################################"
+ echo "Generate initial conditions (4K)"
+ echo "#####################################################"
+ # run python script
+ python $pypath/lc1_initial_condition_fixedCoriolisParameter_4K.py
+ # extend initial data to 720x360 grid
+ cdo remapbil,r720x360 -setgrid,r10x360 lc1_initialcondition_4K_r10x360.nc lc1_initialcondition_4K_r720x360.nc
+ # remove output from python script
+ rm lc1_initialcondition_4K_r10x360.nc
+fi
+
+if [[ "$ini_python_4k_qvctl" == "yes" ]]; then
+ echo ""
+ echo "#####################################################"
+ echo "Generate initial conditions (4K, qv from CTL)"
+ echo "#####################################################"
+ # run python script
+ python $pypath/lc1_initial_condition_fixedCoriolisParameter_4K_qvfromcontrol.py
+ # extend initial data to 720x360 grid
+ cdo remapbil,r720x360 -setgrid,r10x360 lc1_initialcondition_4K_qvCTL_r10x360.nc lc1_initialcondition_4K_qvCTL_r720x360.nc
+ # remove output from python script
+ rm lc1_initialcondition_4K_qvCTL_r10x360.nc
+fi
+
+# 1.2 temperature perturbation
+if [[ "$tpert_python" == "yes" ]]; then
+ echo ""
+ echo "#####################################################"
+ echo "Generate temperature perturbation"
+ echo "#####################################################"
+ python $pypath/lc1_perturbation_condition_wavenumber7.py
+fi
+
+# 1.3 add temperature perturbation to temperature field
+if [[ "$ini_python_ctl" == "yes" ]]; then
+ echo ""
+ echo "#####################################################"
+ echo "Merge initial file and temperature perturbation (CTL)"
+ echo "#####################################################"
+ cdo merge -delvar,T lc1_initialcondition_CTL_r720x360.nc -add -selvar,T lc1_initialcondition_CTL_r720x360.nc lc1_tperturb_720x360.nc lc1_CTL_r720x360.nc
+ # remove temporary data
+ rm lc1_initialcondition_CTL_r720x360.nc
+fi
+
+if [[ "$ini_python_4k" == "yes" ]]; then
+ echo ""
+ echo "#####################################################"
+ echo "Merge initial file and temperature perturbation (4K)"
+ echo "#####################################################"
+ cdo merge -delvar,T lc1_initialcondition_4K_r720x360.nc -add -selvar,T lc1_initialcondition_4K_r720x360.nc lc1_tperturb_720x360.nc lc1_4K_r720x360.nc
+ # remove temporary data
+ rm lc1_initialcondition_4K_r720x360.nc
+fi
+
+if [[ "$ini_python_4k_qvctl" == "yes" ]]; then
+ echo ""
+ echo "##################################################################"
+ echo "Merge initial file and temperature perturbation (4K, qv from CTL)"
+ echo "##################################################################"
+ cdo merge -delvar,T lc1_initialcondition_4K_qvCTL_r720x360.nc -add -selvar,T lc1_initialcondition_4K_qvCTL_r720x360.nc lc1_tperturb_720x360.nc lc1_4K_qvCTL_r720x360.nc
+ # remove temporary data
+ rm lc1_initialcondition_4K_qvCTL_r720x360.nc
+fi
+
+## remove temperature perturbation file
+#rm lc1_tperturb_720x360.nc
+
+#--------------------------------------------------------------------------------------
+#--------------------------------------------------------------------------------------
+# 2- Remap the lc1 initial file onto the channel grid using DWD ICONTOOLS
+
+cd inputs_planar_channel_51x81_80km
+
+# NOTE: do not use indent for "EOF"; otherwise the calculation terminates with an error
+if [[ "$remap_ini_python_ctl" == "yes" ]]; then
+ echo ""
+ echo "#####################################################"
+ echo "Remap initial file (CTL) onto channel grid"
+ echo "#####################################################"
+
+ for field in U V W QV QC QI SST LNPS GEOP_SFC GEOP_ML T ; do
+
+ cat >> NAMELIST_ICONREMAP_FIELDS << EOF
+ !
+ &input_field_nml
+ inputname = "${field}"
+ outputname = "${field}"
+ intp_method = 3
+ /
+EOF
+
+ done
+
+ #cat NAMELIST_ICONREMAP_FIELDS
+
+ cat > NAMELIST_ICONREMAP << EOF
+ &remap_nml
+ in_grid_filename = ''
+ in_filename = '../lc1_CTL_r720x360.nc'
+ in_type = 1
+ out_grid_filename = '../${gridfile}'
+ out_filename = 'lc1_CTL_remapped.nc'
+ out_type = 2
+ out_filetype = 4
+ l_have3dbuffer = .false.
+ ncstorage_file = "ncstorage.tmp"
+ /
+EOF
+
+ # run DWD ICONTOOLS
+ ${ICONTOOLS_DIR}/${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
+ #rm -f ../lc1_CTL_r720x360.nc
+fi
+
+if [[ "$remap_ini_python_4k" == "yes" ]]; then
+ echo ""
+ echo "#####################################################"
+ echo "Remap initial file (4K) onto channel grid"
+ echo "#####################################################"
+
+ for field in U V W QV QC QI SST LNPS GEOP_SFC GEOP_ML T ; do
+
+ cat >> NAMELIST_ICONREMAP_FIELDS << EOF
+ !
+ &input_field_nml
+ inputname = "${field}"
+ outputname = "${field}"
+ intp_method = 3
+ /
+EOF
+
+ done
+
+ #cat NAMELIST_ICONREMAP_FIELDS
+
+ cat > NAMELIST_ICONREMAP << EOF
+ &remap_nml
+ in_grid_filename = ''
+ in_filename = '../lc1_4K_r720x360.nc'
+ in_type = 1
+ out_grid_filename = '../${gridfile}'
+ out_filename = 'lc1_4K_remapped.nc'
+ out_type = 2
+ out_filetype = 4
+ l_have3dbuffer = .false.
+ ncstorage_file = "ncstorage.tmp"
+ /
+EOF
+
+ # run DWD ICONTOOLS
+ ${ICONTOOLS_DIR}/${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
+ #rm -f ../lc1_4K_r720x360.nc
+fi
+
+if [[ "$remap_ini_python_4k_qvctl" == "yes" ]]; then
+ echo ""
+ echo "#######################################################"
+ echo "Remap initial file (4K, qv from CTL) onto channel grid"
+ echo "#######################################################"
+
+ for field in U V W QV QC QI SST LNPS GEOP_SFC GEOP_ML T ; do
+
+ cat >> NAMELIST_ICONREMAP_FIELDS << EOF
+ !
+ &input_field_nml
+ inputname = "${field}"
+ outputname = "${field}"
+ intp_method = 3
+ /
+EOF
+
+ done
+
+ #cat NAMELIST_ICONREMAP_FIELDS
+
+ cat > NAMELIST_ICONREMAP << EOF
+ &remap_nml
+ in_grid_filename = ''
+ in_filename = '../lc1_4K_qvCTL_r720x360.nc'
+ in_type = 1
+ out_grid_filename = '../${gridfile}'
+ out_filename = 'lc1_4K_qvCTL_remapped.nc'
+ out_type = 2
+ out_filetype = 4
+ l_have3dbuffer = .false.
+ ncstorage_file = "ncstorage.tmp"
+ /
+EOF
+
+ # run DWD ICONTOOLS
+ ${ICONTOOLS_DIR}/${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
+ #rm -f ../lc1_4K_qvCTL_r720x360.nc
+fi
+
+#----------------------------------------------------------------------------
+#----------------------------------------------------------------------------
+# 3- Remap the initial data from IFS for a zonally-symmetric aquaplanet to
+# the channel grid.
+
+if [[ "$remap_ifs" == "yes" ]]; then
+ echo ""
+ echo "#####################################################"
+ echo "Remap ifs initial file onto channel grid"
+ echo "#####################################################"
+
+ for field in T_SNOW W_SNOW RHO_SNOW ALB_SNOW SKT STL1 STL2 STL3 STL4 CI W_I Z0 LSM SMIL1 SMIL2 SMIL3 SMIL4 ; do
+
+ cat >> NAMELIST_ICONREMAP_FIELDS << EOF
+ !
+ &input_field_nml
+ inputname = "${field}"
+ outputname = "${field}"
+ intp_method = 3
+ /
+EOF
+
+ done
+
+ cat > NAMELIST_ICONREMAP << EOF
+ &remap_nml
+ in_grid_filename = '../inputs_originalgrid/icon_grid_0002_R02B06_G.nc'
+ in_filename = '../inputs_originalgrid/ifs2icon_0002_R02B06_aquaplanet.nc'
+ in_type = 2
+ out_grid_filename = '../${gridfile}'
+ out_filename = 'ifs2icon_remapped.nc'
+ out_type = 2
+ out_filetype = 4
+ l_have3dbuffer = .false.
+ ncstorage_file = "ncstorage.tmp"
+ /
+EOF
+
+ # run DWD ICONTOOLS
+ ${ICONTOOLS_DIR}/${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
+fi
+
+#----------------------------------------------------------------------------
+#----------------------------------------------------------------------------
+# 4- Construct the complete initial file by adding the remapped output from
+# the python script and the remapped IFS data.
+
+echo ""
+echo "#####################################################"
+
+if [[ "$merge_python_ifs_ctl" == "yes" ]]; then
+ echo "Merge remapped initial files (CTL)"
+
+ ncks -v T_SNOW,W_SNOW,RHO_SNOW,ALB_SNOW,SKT,STL1,STL2,STL3,STL4,CI,W_I,Z0,LSM,SMIL1,SMIL2,SMIL3,SMIL4 ifs2icon_remapped.nc temp1.nc
+
+ ncks -A lc1_CTL_remapped.nc temp1.nc
+
+ mv temp1.nc lc1_ifs_CTL_remapped.nc
+fi
+
+if [[ "$merge_python_ifs_4k" == "yes" ]]; then
+ echo "Merge remapped initial files (4K)"
+
+ ncks -v T_SNOW,W_SNOW,RHO_SNOW,ALB_SNOW,SKT,STL1,STL2,STL3,STL4,CI,W_I,Z0,LSM,SMIL1,SMIL2,SMIL3,SMIL4 ifs2icon_remapped.nc temp1.nc
+
+ ncks -A lc1_4K_remapped.nc temp1.nc
+
+ mv temp1.nc lc1_ifs_4K_remapped.nc
+fi
+
+if [[ "$merge_python_ifs_4k_qvctl" == "yes" ]]; then
+ echo "Merge remapped initial files (4K, qv from CTL)"
+
+ ncks -v T_SNOW,W_SNOW,RHO_SNOW,ALB_SNOW,SKT,STL1,STL2,STL3,STL4,CI,W_I,Z0,LSM,SMIL1,SMIL2,SMIL3,SMIL4 ifs2icon_remapped.nc temp1.nc
+
+ ncks -A lc1_4K_qvCTL_remapped.nc temp1.nc
+
+ mv temp1.nc lc1_ifs_4K_qvCTL_remapped.nc
+fi
+
+#----------------------------------------------------------------------------
+#----------------------------------------------------------------------------
+# 5- Clean-up: remove temporary files
+#if [[ "$remap_ini_python_ctl" == "yes" ]]; then
+# rm lc1_CTL_remapped.nc
+#fi
+#
+#if [[ "$remap_ini_python_4k" == "yes" ]]; then
+# rm lc1_4K_remapped.nc
+#fi
+#
+#if [[ "$remap_ini_python_4k_qvctl" == "yes" ]]; then
+# rm lc1_4K_qvCTL_remapped.nc
+#fi
+#
+#if [[ "$remap_ifs" == "yes" ]]; then
+# rm ifs2icon_remapped.nc
+#fi
+
+#
diff --git a/Scripts_for_preprocessing/generate_initialdata/01b_limited_channel_setup_2km_initialconditions_Tanom.sh b/Scripts_for_preprocessing/generate_initialdata/01b_limited_channel_setup_2km_initialconditions_Tanom.sh
new file mode 100644
index 0000000000000000000000000000000000000000..a7e8cddafd5066eb3d1527436d247c613f24c4a4
--- /dev/null
+++ b/Scripts_for_preprocessing/generate_initialdata/01b_limited_channel_setup_2km_initialconditions_Tanom.sh
@@ -0,0 +1,416 @@
+#!/bin/bash
+#=============================================================================
+#SBATCH --account=bb1152
+#SBATCH --job-name=remap_ini
+#SBATCH --partition=compute
+#SBATCH --nodes=2
+#SBATCH --threads-per-core=2
+#SBATCH --mem=120000
+#SBATCH --output=/work/bb1152/Module_A/A6_CyclEx/b380490_Albern/input_data/LOG.remap_initial_conditions_tanom.%j.o
+#SBATCH --error=/work/bb1152/Module_A/A6_CyclEx/b380490_Albern/input_data/LOG.remap_initial_conditions_tanom.%j.o
+#SBATCH --exclusive
+#SBATCH --time=01:00:00
+#=========================================================================================
+# This script prepares the initial files for a baroclinic life cycle simulation using
+# DWD ICONTOOLS.
+#=========================================================================================
+#-----------------------------------------------------------------------------------------
+# Introduce switches to decide what the script will do.
+# Switches can be set to "yes" or "no".
+
+# run python scripts to generate initial files with temperature anomalies from CMIP6 data
+# "global" temperature anomaly
+ini_python_tanom=no#yes
+# temperature anomaly in tropics
+ini_python_tropic=no#yes
+# temperature anomaly in polar region
+ini_python_polar=no#yes
+
+# path to files with temperature anomalies
+ipath_tanom='/work/bb1152/Module_A/A6_CyclEx/b380490_Albern/CMIP6_anomalies_plev/'
+# file that contains the temperature anomalies
+ifile_tanom='MPI-ESM1-2-LR_farfuture-historical_zm.nc'
+
+# run python script to generate the temperature perturbation
+tpert_python=no#yes
+
+# remap initial files from python scripts to channel grid
+remap_ini_python_tanom=yes
+remap_ini_python_tropic=yes
+remap_ini_python_polar=yes
+
+# remap initial data from IFS to channel grid
+remap_ifs=no#yes
+
+# merge remapped initial files
+merge_python_ifs_tanom=yes
+merge_python_ifs_tropic=yes
+merge_python_ifs_polar=yes
+
+#-----------------------------------------------------------------------------------------
+# Load modules
+
+module purge
+module load anaconda3/bleeding_edge
+module load nco/4.7.5-gcc64
+module load ncl/6.5.0-gccsys
+
+#--------------------------------------------------------------------------------------
+# ICONTOOLS directory
+
+#ICONTOOLS_DIR=/work/bb1018/b380490/icontools-2.1.0/icontools
+ICONTOOLS_DIR=/work/bb1152/Module_A/A6_CyclEx/b380490_Albern/icontools-2.1.0/icontools
+
+BINARY_ICONSUB=iconsub_mpi
+BINARY_REMAP=iconremap_mpi
+BINARY_GRIDGEN=icongridgen
+
+#--------------------------------------------------------------------------------------
+# move to folder "input_data"
+cd ../../../input_data
+
+#--------------------------------------------------------------------------------------
+# file with channel grid
+
+gridfile=Channel_4000x9000_2500m_with_boundary.nc
+
+#--------------------------------------------------------------------------------------
+#--------------------------------------------------------------------------------------
+# 1- Generate initial conditions for baroclinic life cycle 1
+
+# path to python scripts
+pypath='../scripts_gitrepo/preprocessing_scripts'
+
+# 1.1 initial conditions
+if [[ "$ini_python_tanom" == "yes" ]]; then
+ echo "#####################################################"
+ echo "Generate initial conditions (tanom)"
+ echo "#####################################################"
+ # run python script
+ python $pypath/lc1_initial_condition_fixedCoriolisParameter_Tanom.py $ipath_tanom $ifile_tanom
+ # extend initial data to 720x360 grid
+ cdo remapbil,r720x360 -setgrid,r10x360 lc1_initialcondition_Tanom_r10x360.nc lc1_initialcondition_Tanom_r720x360.nc
+ # remove output from python script
+ rm lc1_initialcondition_Tanom_r10x360.nc
+fi
+
+if [[ "$ini_python_tropic" == "yes" ]]; then
+ echo ""
+ echo "#####################################################"
+ echo "Generate initial conditions (tanom tropics)"
+ echo "#####################################################"
+ # run python script
+ python $pypath/lc1_initial_condition_fixedCoriolisParameter_Tanom_TR_PO.py $ipath_tanom $ifile_tanom $"tropical"
+ # extend initial data to 720x360 grid
+ cdo remapbil,r720x360 -setgrid,r10x360 lc1_initialcondition_Tanom_tropics_r10x360.nc lc1_initialcondition_Tanom_tropics_r720x360.nc
+ # remove output from python script
+ rm lc1_initialcondition_Tanom_tropics_r10x360.nc
+fi
+
+if [[ "$ini_python_polar" == "yes" ]]; then
+ echo ""
+ echo "#####################################################"
+ echo "Generate initial conditions (tanom polar)"
+ echo "#####################################################"
+ # run python script
+ python $pypath/lc1_initial_condition_fixedCoriolisParameter_Tanom_TR_PO.py $ipath_tanom $ifile_tanom $"polar"
+ # extend initial data to 720x360 grid
+ cdo remapbil,r720x360 -setgrid,r10x360 lc1_initialcondition_Tanom_polar_r10x360.nc lc1_initialcondition_Tanom_polar_r720x360.nc
+ # remove output from python script
+ rm lc1_initialcondition_Tanom_polar_r10x360.nc
+fi
+
+# 1.2 temperature perturbation
+if [[ "$tpert_python" == "yes" ]]; then
+ echo ""
+ echo "#####################################################"
+ echo "Generate temperature perturbation"
+ echo "#####################################################"
+ python $pypath/lc1_perturbation_condition_wavenumber7.py
+fi
+
+# 1.3 add temperature perturbation to temperature field
+if [[ "$ini_python_tanom" == "yes" ]]; then
+ echo ""
+ echo "###############################################################"
+ echo "Merge initial file and temperature perturbation (tanom)"
+ echo "###############################################################"
+ cdo merge -delvar,T lc1_initialcondition_Tanom_r720x360.nc -add -selvar,T lc1_initialcondition_Tanom_r720x360.nc lc1_tperturb_720x360.nc lc1_Tanom_r720x360.nc
+ # remove temporary data
+ rm lc1_initialcondition_Tanom_r720x360.nc
+fi
+
+if [[ "$ini_python_tropic" == "yes" ]]; then
+ echo ""
+ echo "###############################################################"
+ echo "Merge initial file and temperature perturbation (tanom tropic)"
+ echo "###############################################################"
+ cdo merge -delvar,T lc1_initialcondition_Tanom_tropics_r720x360.nc -add -selvar,T lc1_initialcondition_Tanom_tropics_r720x360.nc lc1_tperturb_720x360.nc lc1_Tanom_tropics_r720x360.nc
+ # remove temporary data
+ rm lc1_initialcondition_Tanom_tropics_r720x360.nc
+fi
+
+if [[ "$ini_python_polar" == "yes" ]]; then
+ echo ""
+ echo "###############################################################"
+ echo "Merge initial file and temperature perturbation (tanom polar)"
+ echo "###############################################################"
+ cdo merge -delvar,T lc1_initialcondition_Tanom_polar_r720x360.nc -add -selvar,T lc1_initialcondition_Tanom_polar_r720x360.nc lc1_tperturb_720x360.nc lc1_Tanom_polar_r720x360.nc
+ # remove temporary data
+ rm lc1_initialcondition_Tanom_polar_r720x360.nc
+fi
+
+## remove temperature perturbation file
+#rm lc1_tperturb_720x360.nc
+
+#--------------------------------------------------------------------------------------
+#--------------------------------------------------------------------------------------
+# 2- Remap the lc1 initial file onto the channel grid using DWD ICONTOOLS
+
+cd inputs_planar_channel_51x81_2km
+
+# NOTE: do not use indent for "EOF"; otherwise the calculation terminates with an error
+if [[ "$remap_ini_python_tanom" == "yes" ]]; then
+ echo ""
+ echo "#####################################################"
+ echo "Remap initial file (tanom) onto channel grid"
+ echo "#####################################################"
+
+ for field in U V W QV QC QI SST LNPS GEOP_SFC GEOP_ML T ; do
+
+ cat >> NAMELIST_ICONREMAP_FIELDS << EOF
+ !
+ &input_field_nml
+ inputname = "${field}"
+ outputname = "${field}"
+ intp_method = 3
+ /
+EOF
+
+ done
+
+ #cat NAMELIST_ICONREMAP_FIELDS
+
+ cat > NAMELIST_ICONREMAP << EOF
+ &remap_nml
+ in_grid_filename = ''
+ in_filename = '../lc1_Tanom_r720x360.nc'
+ in_type = 1
+ out_grid_filename = '../${gridfile}'
+ out_filename = 'lc1_Tanom_remapped.nc'
+ out_type = 2
+ out_filetype = 4
+ l_have3dbuffer = .false.
+ ncstorage_file = "ncstorage.tmp"
+ /
+EOF
+
+ # run DWD ICONTOOLS
+ ${ICONTOOLS_DIR}/${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
+ #rm -f ../lc1_Tanom_r720x360.nc
+fi
+
+if [[ "$remap_ini_python_tropic" == "yes" ]]; then
+ echo ""
+ echo "#####################################################"
+ echo "Remap initial file (tanom tropic) onto channel grid"
+ echo "#####################################################"
+
+ for field in U V W QV QC QI SST LNPS GEOP_SFC GEOP_ML T ; do
+
+ cat >> NAMELIST_ICONREMAP_FIELDS << EOF
+ !
+ &input_field_nml
+ inputname = "${field}"
+ outputname = "${field}"
+ intp_method = 3
+ /
+EOF
+
+ done
+
+ #cat NAMELIST_ICONREMAP_FIELDS
+
+ cat > NAMELIST_ICONREMAP << EOF
+ &remap_nml
+ in_grid_filename = ''
+ in_filename = '../lc1_Tanom_tropics_r720x360.nc'
+ in_type = 1
+ out_grid_filename = '../${gridfile}'
+ out_filename = 'lc1_Tanom_tropics_remapped.nc'
+ out_type = 2
+ out_filetype = 4
+ l_have3dbuffer = .false.
+ ncstorage_file = "ncstorage.tmp"
+ /
+EOF
+
+ # run DWD ICONTOOLS
+ ${ICONTOOLS_DIR}/${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
+ #rm -f ../lc1_Tanom_tropics_r720x360.nc
+fi
+
+if [[ "$remap_ini_python_polar" == "yes" ]]; then
+ echo ""
+ echo "#######################################################"
+ echo "Remap initial file (tanom polar) onto channel grid"
+ echo "#######################################################"
+
+ for field in U V W QV QC QI SST LNPS GEOP_SFC GEOP_ML T ; do
+
+ cat >> NAMELIST_ICONREMAP_FIELDS << EOF
+ !
+ &input_field_nml
+ inputname = "${field}"
+ outputname = "${field}"
+ intp_method = 3
+ /
+EOF
+
+ done
+
+ #cat NAMELIST_ICONREMAP_FIELDS
+
+ cat > NAMELIST_ICONREMAP << EOF
+ &remap_nml
+ in_grid_filename = ''
+ in_filename = '../lc1_Tanom_polar_r720x360.nc'
+ in_type = 1
+ out_grid_filename = '../${gridfile}'
+ out_filename = 'lc1_Tanom_polar_remapped.nc'
+ out_type = 2
+ out_filetype = 4
+ l_have3dbuffer = .false.
+ ncstorage_file = "ncstorage.tmp"
+ /
+EOF
+
+ # run DWD ICONTOOLS
+ ${ICONTOOLS_DIR}/${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
+ #rm -f ../lc1_Tanom_polar_r720x360.nc
+fi
+
+#----------------------------------------------------------------------------
+#----------------------------------------------------------------------------
+# 3- Remap the initial data from IFS for a zonally-symmetric aquaplanet to
+# the channel grid.
+
+if [[ "$remap_ifs" == "yes" ]]; then
+ echo ""
+ echo "#####################################################"
+ echo "Remap ifs initial file onto channel grid"
+ echo "#####################################################"
+
+ for field in T_SNOW W_SNOW RHO_SNOW ALB_SNOW SKT STL1 STL2 STL3 STL4 CI W_I Z0 LSM SMIL1 SMIL2 SMIL3 SMIL4 ; do
+
+ cat >> NAMELIST_ICONREMAP_FIELDS << EOF
+ !
+ &input_field_nml
+ inputname = "${field}"
+ outputname = "${field}"
+ intp_method = 3
+ /
+EOF
+
+ done
+
+ cat > NAMELIST_ICONREMAP << EOF
+ &remap_nml
+ in_grid_filename = '../inputs_originalgrid/icon_grid_0002_R02B06_G.nc'
+ in_filename = '../inputs_originalgrid/ifs2icon_0002_R02B06_aquaplanet.nc'
+ in_type = 2
+ out_grid_filename = '../${gridfile}'
+ out_filename = 'ifs2icon_remapped.nc'
+ out_type = 2
+ out_filetype = 4
+ l_have3dbuffer = .false.
+ ncstorage_file = "ncstorage.tmp"
+ /
+EOF
+
+ # run DWD ICONTOOLS
+ ${ICONTOOLS_DIR}/${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
+fi
+
+#----------------------------------------------------------------------------
+#----------------------------------------------------------------------------
+# 4- Construct the complete initial file by adding the remapped output from
+# the python script and the remapped IFS data.
+
+echo ""
+echo "#####################################################"
+
+if [[ "$merge_python_ifs_tanom" == "yes" ]]; then
+ echo "Merge remapped initial files (tanom)"
+
+ ncks -v T_SNOW,W_SNOW,RHO_SNOW,ALB_SNOW,SKT,STL1,STL2,STL3,STL4,CI,W_I,Z0,LSM,SMIL1,SMIL2,SMIL3,SMIL4 ifs2icon_remapped.nc temp1.nc
+
+ ncks -A lc1_Tanom_remapped.nc temp1.nc
+
+ mv temp1.nc lc1_ifs_Tanom_remapped.nc
+fi
+
+if [[ "$merge_python_ifs_tropic" == "yes" ]]; then
+ echo "Merge remapped initial files (tanom tropic)"
+
+ ncks -v T_SNOW,W_SNOW,RHO_SNOW,ALB_SNOW,SKT,STL1,STL2,STL3,STL4,CI,W_I,Z0,LSM,SMIL1,SMIL2,SMIL3,SMIL4 ifs2icon_remapped.nc temp1.nc
+
+ ncks -A lc1_Tanom_tropics_remapped.nc temp1.nc
+
+ mv temp1.nc lc1_ifs_Tanom_tropics_remapped.nc
+fi
+
+if [[ "$merge_python_ifs_polar" == "yes" ]]; then
+ echo "Merge remapped initial files (tanom polar)"
+
+ ncks -v T_SNOW,W_SNOW,RHO_SNOW,ALB_SNOW,SKT,STL1,STL2,STL3,STL4,CI,W_I,Z0,LSM,SMIL1,SMIL2,SMIL3,SMIL4 ifs2icon_remapped.nc temp1.nc
+
+ ncks -A lc1_Tanom_polar_remapped.nc temp1.nc
+
+ mv temp1.nc lc1_ifs_Tanom_polar_remapped.nc
+fi
+
+#----------------------------------------------------------------------------
+#----------------------------------------------------------------------------
+# 5- Clean-up: remove temporary files
+#if [[ "$remap_ini_python_tanom" == "yes" ]]; then
+# rm lc1_Tanom_remapped.nc
+#fi
+#
+#if [[ "$remap_ini_python_tropic" == "yes" ]]; then
+# rm lc1_Tanom_tropics_remapped.nc
+#fi
+#
+#if [[ "$remap_ini_python_polar" == "yes" ]]; then
+# rm lc1_Tanom_polar_remapped.nc
+#fi
+#
+#if [[ "$remap_ifs" == "yes" ]]; then
+# rm ifs2icon_remapped.nc
+#fi
+
+#
diff --git a/Scripts_for_preprocessing/generate_initialdata/01b_limited_channel_setup_80km_initialconditions_Tanom.sh b/Scripts_for_preprocessing/generate_initialdata/01b_limited_channel_setup_80km_initialconditions_Tanom.sh
new file mode 100644
index 0000000000000000000000000000000000000000..c432673646f84af84c27d33b40b11accc495ea4d
--- /dev/null
+++ b/Scripts_for_preprocessing/generate_initialdata/01b_limited_channel_setup_80km_initialconditions_Tanom.sh
@@ -0,0 +1,416 @@
+#!/bin/bash
+#=============================================================================
+#SBATCH --account=bb1152
+#SBATCH --job-name=remap_ini
+#SBATCH --partition=compute
+#SBATCH --nodes=2
+#SBATCH --threads-per-core=2
+#SBATCH --mem=120000
+#SBATCH --output=/work/bb1152/Module_A/A6_CyclEx/b380490_Albern/input_data/LOG.remap_initial_conditions_tanom.%j.o
+#SBATCH --error=/work/bb1152/Module_A/A6_CyclEx/b380490_Albern/input_data/LOG.remap_initial_conditions_tanom.%j.o
+#SBATCH --exclusive
+#SBATCH --time=01:00:00
+#=========================================================================================
+# This script prepares the initial files for a baroclinic life cycle simulation using
+# DWD ICONTOOLS.
+#=========================================================================================
+#-----------------------------------------------------------------------------------------
+# Introduce switches to decide what the script will do.
+# Switches can be set to "yes" or "no".
+
+# run python scripts to generate initial files with temperature anomalies from CMIP6 data
+# "global" temperature anomaly
+ini_python_tanom=no#yes
+# temperature anomaly in tropics
+ini_python_tropic=yes
+# temperature anomaly in polar region
+ini_python_polar=yes
+
+# path to files with temperature anomalies
+ipath_tanom='/work/bb1152/Module_A/A6_CyclEx/b380490_Albern/CMIP6_anomalies_plev/'
+# file that contains the temperature anomalies
+ifile_tanom='MPI-ESM1-2-LR_farfuture-historical_zm.nc'
+
+# run python script to generate the temperature perturbation
+tpert_python=no#yes
+
+# remap initial files from python scripts to channel grid
+remap_ini_python_tanom=no
+remap_ini_python_tropic=yes
+remap_ini_python_polar=yes
+
+# remap initial data from IFS to channel grid
+remap_ifs=no#yes
+
+# merge remapped initial files
+merge_python_ifs_tanom=no#yes
+merge_python_ifs_tropic=yes
+merge_python_ifs_polar=yes
+
+#-----------------------------------------------------------------------------------------
+# Load modules
+
+module purge
+module load anaconda3/bleeding_edge
+module load nco/4.7.5-gcc64
+module load ncl/6.5.0-gccsys
+
+#--------------------------------------------------------------------------------------
+# ICONTOOLS directory
+
+#ICONTOOLS_DIR=/work/bb1018/b380490/icontools-2.1.0/icontools
+ICONTOOLS_DIR=/work/bb1152/Module_A/A6_CyclEx/b380490_Albern/icontools-2.1.0/icontools
+
+BINARY_ICONSUB=iconsub_mpi
+BINARY_REMAP=iconremap_mpi
+BINARY_GRIDGEN=icongridgen
+
+#--------------------------------------------------------------------------------------
+# move to folder "input_data"
+cd ../../../input_data
+
+#--------------------------------------------------------------------------------------
+# file with channel grid
+
+gridfile=Channel_4000x9000_80000m_with_boundary.nc
+
+#--------------------------------------------------------------------------------------
+#--------------------------------------------------------------------------------------
+# 1- Generate initial conditions for baroclinic life cycle 1
+
+# path to python scripts
+pypath='../scripts_gitrepo/preprocessing_scripts'
+
+# 1.1 initial conditions
+if [[ "$ini_python_tanom" == "yes" ]]; then
+ echo "#####################################################"
+ echo "Generate initial conditions (tanom)"
+ echo "#####################################################"
+ # run python script
+ python $pypath/lc1_initial_condition_fixedCoriolisParameter_Tanom.py $ipath_tanom $ifile_tanom
+ # extend initial data to 720x360 grid
+ cdo remapbil,r720x360 -setgrid,r10x360 lc1_initialcondition_Tanom_r10x360.nc lc1_initialcondition_Tanom_r720x360.nc
+ # remove output from python script
+ rm lc1_initialcondition_Tanom_r10x360.nc
+fi
+
+if [[ "$ini_python_tropic" == "yes" ]]; then
+ echo ""
+ echo "#####################################################"
+ echo "Generate initial conditions (tanom tropics)"
+ echo "#####################################################"
+ # run python script
+ python $pypath/lc1_initial_condition_fixedCoriolisParameter_Tanom_TR_PO.py $ipath_tanom $ifile_tanom $"tropical"
+ # extend initial data to 720x360 grid
+ cdo remapbil,r720x360 -setgrid,r10x360 lc1_initialcondition_Tanom_tropics_r10x360.nc lc1_initialcondition_Tanom_tropics_r720x360.nc
+ # remove output from python script
+ rm lc1_initialcondition_Tanom_tropics_r10x360.nc
+fi
+
+if [[ "$ini_python_polar" == "yes" ]]; then
+ echo ""
+ echo "#####################################################"
+ echo "Generate initial conditions (tanom polar)"
+ echo "#####################################################"
+ # run python script
+ python $pypath/lc1_initial_condition_fixedCoriolisParameter_Tanom_TR_PO.py $ipath_tanom $ifile_tanom $"polar"
+ # extend initial data to 720x360 grid
+ cdo remapbil,r720x360 -setgrid,r10x360 lc1_initialcondition_Tanom_polar_r10x360.nc lc1_initialcondition_Tanom_polar_r720x360.nc
+ # remove output from python script
+ rm lc1_initialcondition_Tanom_polar_r10x360.nc
+fi
+
+# 1.2 temperature perturbation
+if [[ "$tpert_python" == "yes" ]]; then
+ echo ""
+ echo "#####################################################"
+ echo "Generate temperature perturbation"
+ echo "#####################################################"
+ python $pypath/lc1_perturbation_condition_wavenumber7.py
+fi
+
+# 1.3 add temperature perturbation to temperature field
+if [[ "$ini_python_tanom" == "yes" ]]; then
+ echo ""
+ echo "###############################################################"
+ echo "Merge initial file and temperature perturbation (tanom)"
+ echo "###############################################################"
+ cdo merge -delvar,T lc1_initialcondition_Tanom_r720x360.nc -add -selvar,T lc1_initialcondition_Tanom_r720x360.nc lc1_tperturb_720x360.nc lc1_Tanom_r720x360.nc
+ # remove temporary data
+ rm lc1_initialcondition_Tanom_r720x360.nc
+fi
+
+if [[ "$ini_python_tropic" == "yes" ]]; then
+ echo ""
+ echo "###############################################################"
+ echo "Merge initial file and temperature perturbation (tanom tropic)"
+ echo "###############################################################"
+ cdo merge -delvar,T lc1_initialcondition_Tanom_tropics_r720x360.nc -add -selvar,T lc1_initialcondition_Tanom_tropics_r720x360.nc lc1_tperturb_720x360.nc lc1_Tanom_tropics_r720x360.nc
+ # remove temporary data
+ rm lc1_initialcondition_Tanom_tropics_r720x360.nc
+fi
+
+if [[ "$ini_python_polar" == "yes" ]]; then
+ echo ""
+ echo "###############################################################"
+ echo "Merge initial file and temperature perturbation (tanom polar)"
+ echo "###############################################################"
+ cdo merge -delvar,T lc1_initialcondition_Tanom_polar_r720x360.nc -add -selvar,T lc1_initialcondition_Tanom_polar_r720x360.nc lc1_tperturb_720x360.nc lc1_Tanom_polar_r720x360.nc
+ # remove temporary data
+ rm lc1_initialcondition_Tanom_polar_r720x360.nc
+fi
+
+## remove temperature perturbation file
+#rm lc1_tperturb_720x360.nc
+
+#--------------------------------------------------------------------------------------
+#--------------------------------------------------------------------------------------
+# 2- Remap the lc1 initial file onto the channel grid using DWD ICONTOOLS
+
+cd inputs_planar_channel_51x81_80km
+
+# NOTE: do not use indent for "EOF"; otherwise the calculation terminates with an error
+if [[ "$remap_ini_python_tanom" == "yes" ]]; then
+ echo ""
+ echo "#####################################################"
+ echo "Remap initial file (tanom) onto channel grid"
+ echo "#####################################################"
+
+ for field in U V W QV QC QI SST LNPS GEOP_SFC GEOP_ML T ; do
+
+ cat >> NAMELIST_ICONREMAP_FIELDS << EOF
+ !
+ &input_field_nml
+ inputname = "${field}"
+ outputname = "${field}"
+ intp_method = 3
+ /
+EOF
+
+ done
+
+ #cat NAMELIST_ICONREMAP_FIELDS
+
+ cat > NAMELIST_ICONREMAP << EOF
+ &remap_nml
+ in_grid_filename = ''
+ in_filename = '../lc1_Tanom_r720x360.nc'
+ in_type = 1
+ out_grid_filename = '../${gridfile}'
+ out_filename = 'lc1_Tanom_remapped.nc'
+ out_type = 2
+ out_filetype = 4
+ l_have3dbuffer = .false.
+ ncstorage_file = "ncstorage.tmp"
+ /
+EOF
+
+ # run DWD ICONTOOLS
+ ${ICONTOOLS_DIR}/${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
+ #rm -f ../lc1_Tanom_r720x360.nc
+fi
+
+if [[ "$remap_ini_python_tropic" == "yes" ]]; then
+ echo ""
+ echo "#####################################################"
+ echo "Remap initial file (tanom tropic) onto channel grid"
+ echo "#####################################################"
+
+ for field in U V W QV QC QI SST LNPS GEOP_SFC GEOP_ML T ; do
+
+ cat >> NAMELIST_ICONREMAP_FIELDS << EOF
+ !
+ &input_field_nml
+ inputname = "${field}"
+ outputname = "${field}"
+ intp_method = 3
+ /
+EOF
+
+ done
+
+ #cat NAMELIST_ICONREMAP_FIELDS
+
+ cat > NAMELIST_ICONREMAP << EOF
+ &remap_nml
+ in_grid_filename = ''
+ in_filename = '../lc1_Tanom_tropics_r720x360.nc'
+ in_type = 1
+ out_grid_filename = '../${gridfile}'
+ out_filename = 'lc1_Tanom_tropics_remapped.nc'
+ out_type = 2
+ out_filetype = 4
+ l_have3dbuffer = .false.
+ ncstorage_file = "ncstorage.tmp"
+ /
+EOF
+
+ # run DWD ICONTOOLS
+ ${ICONTOOLS_DIR}/${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
+ #rm -f ../lc1_Tanom_tropics_r720x360.nc
+fi
+
+if [[ "$remap_ini_python_polar" == "yes" ]]; then
+ echo ""
+ echo "#######################################################"
+ echo "Remap initial file (tanom polar) onto channel grid"
+ echo "#######################################################"
+
+ for field in U V W QV QC QI SST LNPS GEOP_SFC GEOP_ML T ; do
+
+ cat >> NAMELIST_ICONREMAP_FIELDS << EOF
+ !
+ &input_field_nml
+ inputname = "${field}"
+ outputname = "${field}"
+ intp_method = 3
+ /
+EOF
+
+ done
+
+ #cat NAMELIST_ICONREMAP_FIELDS
+
+ cat > NAMELIST_ICONREMAP << EOF
+ &remap_nml
+ in_grid_filename = ''
+ in_filename = '../lc1_Tanom_polar_r720x360.nc'
+ in_type = 1
+ out_grid_filename = '../${gridfile}'
+ out_filename = 'lc1_Tanom_polar_remapped.nc'
+ out_type = 2
+ out_filetype = 4
+ l_have3dbuffer = .false.
+ ncstorage_file = "ncstorage.tmp"
+ /
+EOF
+
+ # run DWD ICONTOOLS
+ ${ICONTOOLS_DIR}/${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
+ #rm -f ../lc1_Tanom_polar_r720x360.nc
+fi
+
+#----------------------------------------------------------------------------
+#----------------------------------------------------------------------------
+# 3- Remap the initial data from IFS for a zonally-symmetric aquaplanet to
+# the channel grid.
+
+if [[ "$remap_ifs" == "yes" ]]; then
+ echo ""
+ echo "#####################################################"
+ echo "Remap ifs initial file onto channel grid"
+ echo "#####################################################"
+
+ for field in T_SNOW W_SNOW RHO_SNOW ALB_SNOW SKT STL1 STL2 STL3 STL4 CI W_I Z0 LSM SMIL1 SMIL2 SMIL3 SMIL4 ; do
+
+ cat >> NAMELIST_ICONREMAP_FIELDS << EOF
+ !
+ &input_field_nml
+ inputname = "${field}"
+ outputname = "${field}"
+ intp_method = 3
+ /
+EOF
+
+ done
+
+ cat > NAMELIST_ICONREMAP << EOF
+ &remap_nml
+ in_grid_filename = '../inputs_originalgrid/icon_grid_0002_R02B06_G.nc'
+ in_filename = '../inputs_originalgrid/ifs2icon_0002_R02B06_aquaplanet.nc'
+ in_type = 2
+ out_grid_filename = '../${gridfile}'
+ out_filename = 'ifs2icon_remapped.nc'
+ out_type = 2
+ out_filetype = 4
+ l_have3dbuffer = .false.
+ ncstorage_file = "ncstorage.tmp"
+ /
+EOF
+
+ # run DWD ICONTOOLS
+ ${ICONTOOLS_DIR}/${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
+fi
+
+#----------------------------------------------------------------------------
+#----------------------------------------------------------------------------
+# 4- Construct the complete initial file by adding the remapped output from
+# the python script and the remapped IFS data.
+
+echo ""
+echo "#####################################################"
+
+if [[ "$merge_python_ifs_tanom" == "yes" ]]; then
+ echo "Merge remapped initial files (tanom)"
+
+ ncks -v T_SNOW,W_SNOW,RHO_SNOW,ALB_SNOW,SKT,STL1,STL2,STL3,STL4,CI,W_I,Z0,LSM,SMIL1,SMIL2,SMIL3,SMIL4 ifs2icon_remapped.nc temp1.nc
+
+ ncks -A lc1_Tanom_remapped.nc temp1.nc
+
+ mv temp1.nc lc1_ifs_Tanom_remapped.nc
+fi
+
+if [[ "$merge_python_ifs_tropic" == "yes" ]]; then
+ echo "Merge remapped initial files (tanom tropic)"
+
+ ncks -v T_SNOW,W_SNOW,RHO_SNOW,ALB_SNOW,SKT,STL1,STL2,STL3,STL4,CI,W_I,Z0,LSM,SMIL1,SMIL2,SMIL3,SMIL4 ifs2icon_remapped.nc temp1.nc
+
+ ncks -A lc1_Tanom_tropics_remapped.nc temp1.nc
+
+ mv temp1.nc lc1_ifs_Tanom_tropics_remapped.nc
+fi
+
+if [[ "$merge_python_ifs_polar" == "yes" ]]; then
+ echo "Merge remapped initial files (tanom polar)"
+
+ ncks -v T_SNOW,W_SNOW,RHO_SNOW,ALB_SNOW,SKT,STL1,STL2,STL3,STL4,CI,W_I,Z0,LSM,SMIL1,SMIL2,SMIL3,SMIL4 ifs2icon_remapped.nc temp1.nc
+
+ ncks -A lc1_Tanom_polar_remapped.nc temp1.nc
+
+ mv temp1.nc lc1_ifs_Tanom_polar_remapped.nc
+fi
+
+#----------------------------------------------------------------------------
+#----------------------------------------------------------------------------
+# 5- Clean-up: remove temporary files
+#if [[ "$remap_ini_python_tanom" == "yes" ]]; then
+# rm lc1_Tanom_remapped.nc
+#fi
+#
+#if [[ "$remap_ini_python_tropic" == "yes" ]]; then
+# rm lc1_Tanom_tropics_remapped.nc
+#fi
+#
+#if [[ "$remap_ini_python_polar" == "yes" ]]; then
+# rm lc1_Tanom_polar_remapped.nc
+#fi
+#
+#if [[ "$remap_ifs" == "yes" ]]; then
+# rm ifs2icon_remapped.nc
+#fi
+
+#
diff --git a/Scripts_for_preprocessing/generate_initialdata/02_limited_channel_setup_2km_extpar_and_ozone.sh b/Scripts_for_preprocessing/generate_initialdata/02_limited_channel_setup_2km_extpar_and_ozone.sh
new file mode 100644
index 0000000000000000000000000000000000000000..11b2c26ebd314891d60c0550003b75813c1cd90e
--- /dev/null
+++ b/Scripts_for_preprocessing/generate_initialdata/02_limited_channel_setup_2km_extpar_and_ozone.sh
@@ -0,0 +1,160 @@
+#!/bin/bash
+#=============================================================================
+#SBATCH --account=bb1152
+#SBATCH --job-name=remap_extpar
+#SBATCH --partition=compute
+#SBATCH --nodes=2
+#SBATCH --threads-per-core=2
+#SBATCH --mem=120000
+#SBATCH --output=/work/bb1152/Module_A/A6_CyclEx/b380490_Albern/input_data/LOG.remap_extpar.%j.o
+#SBATCH --error=/work/bb1152/Module_A/A6_CyclEx/b380490_Albern/input_data/LOG.remap_extpar.%j.o
+#SBATCH --exclusive
+#SBATCH --time=01:00:00
+#=========================================================================================
+# This script is for preparing the initial files needed for baroclinic life cycle
+# simulation using ICONTOOLS.
+#=========================================================================================
+#-----------------------------------------------------------------------------------------
+# Introduce switches to decide what the script will do.
+# Switches can be set to "yes" or "no".
+
+# remap extpar data
+remap_extpar=yes
+
+# remap ozone data
+remap_ozone=yes
+
+#-----------------------------------------------------------------------------------------
+# Load modules
+
+module purge
+module load anaconda3/bleeding_edge
+module load nco/4.7.5-gcc64
+module load ncl/6.5.0-gccsys
+
+#--------------------------------------------------------------------------------------
+# ICONTOOLS directory
+
+#ICONTOOLS_DIR=/work/bb1018/b380490/icontools-2.1.0/icontools
+ICONTOOLS_DIR=/work/bb1152/Module_A/A6_CyclEx/b380490_Albern/icontools-2.1.0/icontools
+
+BINARY_ICONSUB=iconsub_mpi
+BINARY_REMAP=iconremap_mpi
+BINARY_GRIDGEN=icongridgen
+
+#--------------------------------------------------------------------------------------
+# go to folder in which the interpolated data will be stored
+cd ../../../input_data/inputs_planar_channel_51x81_2km
+
+#--------------------------------------------------------------------------------------
+# file with channel grid
+
+gridfile=Channel_4000x9000_2500m_with_boundary.nc
+
+#--------------------------------------------------------------------------------------
+#--------------------------------------------------------------------------------------
+# 1- Remap the aquaplanet extpar file onto the channel grid
+
+# NOTE: do not use indent for "EOF"; otherwise the calculation terminates with an error
+if [[ "$remap_extpar" == "yes" ]]; then
+ echo "#####################################################"
+ echo "Remap extpar file onto channel grid"
+ echo "#####################################################"
+
+ 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
+ !
+ &input_field_nml
+ inputname = "${field}"
+ outputname = "${field}"
+ intp_method = 3
+ /
+EOF
+
+ done
+
+ #cat NAMELIST_ICONREMAP_FIELDS
+
+ cat > NAMELIST_ICONREMAP << EOF
+ &remap_nml
+ in_grid_filename = '../inputs_originalgrid/icon_grid_0010_R02B04_G.nc'
+ in_filename = '../inputs_originalgrid/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
+
+ # run DWD ICONTOOLS
+ ${ICONTOOLS_DIR}/${BINARY_REMAP} \
+ --remap_nml NAMELIST_ICONREMAP \
+ --input_field_nml NAMELIST_ICONREMAP_FIELDS 2>&1
+
+ # Correction of remapped extpar file, so that ICON can understand it
+ ncatted -a rawdata,global,c,c,"GLOBCOVER2009, FAO DSMW, GLOBE, Lake Database" extpar_remapped.nc
+
+ python ../../scripts_gitrepo/preprocessing_scripts/extpar_helper.py
+
+ # clean-up
+ rm -f ncstorage.tmp*
+ rm -f nml.log NAMELIST_SUB NAMELIST_ICONREMAP NAMELIST_ICONREMAP_FIELDS
+ rm extpar_remapped.nc
+fi
+
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+# 2- Remap the Ozone file onto the channel grid
+
+if [[ "$remap_ozone" == "yes" ]]; then
+ echo ""
+ echo "#####################################################"
+ echo "Remap ozone file onto channel grid"
+ echo "#####################################################"
+
+ for field in O3 ; do
+
+ cat >> NAMELIST_ICONREMAP_FIELDS << EOF
+ !
+ &input_field_nml
+ inputname = "${field}"
+ outputname = "${field}"
+ intp_method = 3
+ /
+EOF
+
+ done
+
+ cat > NAMELIST_ICONREMAP << EOF
+ &remap_nml
+ in_grid_filename = ''
+ in_filename = '../inputs_originalgrid/ape_o3_R2B04_1Pa_cell.t63grid.nc'
+ in_type = 1
+ out_grid_filename = '../${gridfile}'
+ out_filename = 'ape_O3_remapped.nc'
+ out_type = 2
+ out_filetype = 4
+ l_have3dbuffer = .false.
+ ncstorage_file = "ncstorage.tmp"
+ /
+EOF
+
+ # run DWD ICONTOOLS
+ ${ICONTOOLS_DIR}/${BINARY_REMAP} \
+ --remap_nml NAMELIST_ICONREMAP \
+ --input_field_nml NAMELIST_ICONREMAP_FIELDS 2>&1
+
+ # Correction of remapped ozone file
+ ncrename -d plev,level ape_O3_remapped.nc
+ ncrename -v plev,level ape_O3_remapped.nc
+
+ # clean-up
+ rm -f ncstorage.tmp*
+ rm -f nml.log NAMELIST_SUB NAMELIST_ICONREMAP NAMELIST_ICONREMAP_FIELDS
+fi
+
+#
diff --git a/Scripts_for_preprocessing/generate_initialdata/02_limited_channel_setup_80km_extpar_and_ozone.sh b/Scripts_for_preprocessing/generate_initialdata/02_limited_channel_setup_80km_extpar_and_ozone.sh
new file mode 100644
index 0000000000000000000000000000000000000000..653bf4981ee7e5a6e874d183feb4739f7bd4e966
--- /dev/null
+++ b/Scripts_for_preprocessing/generate_initialdata/02_limited_channel_setup_80km_extpar_and_ozone.sh
@@ -0,0 +1,160 @@
+#!/bin/bash
+#=============================================================================
+#SBATCH --account=bb1152
+#SBATCH --job-name=remap_extpar
+#SBATCH --partition=compute
+#SBATCH --nodes=2
+#SBATCH --threads-per-core=2
+#SBATCH --mem=120000
+#SBATCH --output=/work/bb1152/Module_A/A6_CyclEx/b380490_Albern/input_data/LOG.remap_extpar.%j.o
+#SBATCH --error=/work/bb1152/Module_A/A6_CyclEx/b380490_Albern/input_data/LOG.remap_extpar.%j.o
+#SBATCH --exclusive
+#SBATCH --time=01:00:00
+#=========================================================================================
+# This script is for preparing the initial files needed for baroclinic life cycle
+# simulation using ICONTOOLS.
+#=========================================================================================
+#-----------------------------------------------------------------------------------------
+# Introduce switches to decide what the script will do.
+# Switches can be set to "yes" or "no".
+
+# remap extpar data
+remap_extpar=yes
+
+# remap ozone data
+remap_ozone=yes
+
+#-----------------------------------------------------------------------------------------
+# Load modules
+
+module purge
+module load anaconda3/bleeding_edge
+module load nco/4.7.5-gcc64
+module load ncl/6.5.0-gccsys
+
+#--------------------------------------------------------------------------------------
+# ICONTOOLS directory
+
+#ICONTOOLS_DIR=/work/bb1018/b380490/icontools-2.1.0/icontools
+ICONTOOLS_DIR=/work/bb1152/Module_A/A6_CyclEx/b380490_Albern/icontools-2.1.0/icontools
+
+BINARY_ICONSUB=iconsub_mpi
+BINARY_REMAP=iconremap_mpi
+BINARY_GRIDGEN=icongridgen
+
+#--------------------------------------------------------------------------------------
+# go to folder in which the interpolated data will be stored
+cd ../../../input_data/inputs_planar_channel_51x81_80km
+
+#--------------------------------------------------------------------------------------
+# file with channel grid
+
+gridfile=Channel_4000x9000_80000m_with_boundary.nc
+
+#--------------------------------------------------------------------------------------
+#--------------------------------------------------------------------------------------
+# 1- Remap the aquaplanet extpar file onto the channel grid
+
+# NOTE: do not use indent for "EOF"; otherwise the calculation terminates with an error
+if [[ "$remap_extpar" == "yes" ]]; then
+ echo "#####################################################"
+ echo "Remap extpar file onto channel grid"
+ echo "#####################################################"
+
+ for field in SOILTYP FR_LAND ICE PLCOV_MX LAI_MX RSMIN URBAN FOR_D FOR_E EMIS_RAD ROOTDP Z0 NDVI_MAX topography_c SSO_STDH SSO_THETA SSO_GAMMA SSO_SIGMA T_CL FR_LAKE DEPTH_LK topography_v LU_CLASS_FRACTION NDVI NDVI_MRAT AER_BC AER_DUST AER_ORG AER_SO4 AER_SS ALB ALNID ALUVD lon lat clon clat clon_vertices clat_vertices ; do
+
+ cat >> NAMELIST_ICONREMAP_FIELDS << EOF
+ !
+ &input_field_nml
+ inputname = "${field}"
+ outputname = "${field}"
+ intp_method = 3
+ /
+EOF
+
+ done
+
+ #cat NAMELIST_ICONREMAP_FIELDS
+
+ cat > NAMELIST_ICONREMAP << EOF
+ &remap_nml
+ in_grid_filename = '../inputs_originalgrid/icon_grid_0010_R02B04_G.nc'
+ in_filename = '../inputs_originalgrid/icon_extpar_0010_R02B04_G_aquaplanet.nc'
+ in_type = 2
+ out_grid_filename = '../${gridfile}'
+ out_filename = 'extpar_remapped.nc'
+ out_type = 2
+ out_filetype = 4
+ l_have3dbuffer = .false.
+ ncstorage_file = "ncstorage.tmp"
+ /
+EOF
+
+ # run DWD ICONTOOLS
+ ${ICONTOOLS_DIR}/${BINARY_REMAP} \
+ --remap_nml NAMELIST_ICONREMAP \
+ --input_field_nml NAMELIST_ICONREMAP_FIELDS 2>&1
+
+ # Correction of remapped extpar file, so that ICON can understand it
+ ncatted -a rawdata,global,c,c,"GLOBCOVER2009, FAO DSMW, GLOBE, Lake Database" extpar_remapped.nc
+
+ python ../../scripts_gitrepo/preprocessing_scripts/extpar_helper.py
+
+ # clean-up
+ rm -f ncstorage.tmp*
+ rm -f nml.log NAMELIST_SUB NAMELIST_ICONREMAP NAMELIST_ICONREMAP_FIELDS
+ rm extpar_remapped.nc
+fi
+
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+# 2- Remap the Ozone file onto the channel grid
+
+if [[ "$remap_ozone" == "yes" ]]; then
+ echo ""
+ echo "#####################################################"
+ echo "Remap ozone file onto channel grid"
+ echo "#####################################################"
+
+ for field in O3 ; do
+
+ cat >> NAMELIST_ICONREMAP_FIELDS << EOF
+ !
+ &input_field_nml
+ inputname = "${field}"
+ outputname = "${field}"
+ intp_method = 3
+ /
+EOF
+
+ done
+
+ cat > NAMELIST_ICONREMAP << EOF
+ &remap_nml
+ in_grid_filename = ''
+ in_filename = '../inputs_originalgrid/ape_o3_R2B04_1Pa_cell.t63grid.nc'
+ in_type = 1
+ out_grid_filename = '../${gridfile}'
+ out_filename = 'ape_O3_remapped.nc'
+ out_type = 2
+ out_filetype = 4
+ l_have3dbuffer = .false.
+ ncstorage_file = "ncstorage.tmp"
+ /
+EOF
+
+ # run DWD ICONTOOLS
+ ${ICONTOOLS_DIR}/${BINARY_REMAP} \
+ --remap_nml NAMELIST_ICONREMAP \
+ --input_field_nml NAMELIST_ICONREMAP_FIELDS 2>&1
+
+ # Correction of remapped ozone file
+ ncrename -d plev,level ape_O3_remapped.nc
+ ncrename -v plev,level ape_O3_remapped.nc
+
+ # clean-up
+ rm -f ncstorage.tmp*
+ rm -f nml.log NAMELIST_SUB NAMELIST_ICONREMAP NAMELIST_ICONREMAP_FIELDS
+fi
+
+#
diff --git a/Scripts_for_preprocessing/generate_initialdata/check_moisture_impact_lc1_initial_condition_fixedCoriolisParameter_4K.ipynb b/Scripts_for_preprocessing/generate_initialdata/check_moisture_impact_lc1_initial_condition_fixedCoriolisParameter_4K.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..9732d64b3be90d9b50dd1968eab29991683e7893
--- /dev/null
+++ b/Scripts_for_preprocessing/generate_initialdata/check_moisture_impact_lc1_initial_condition_fixedCoriolisParameter_4K.ipynb
@@ -0,0 +1,1067 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "49436791-5f4b-44e6-83ad-209241d426c7",
+ "metadata": {},
+ "source": [
+ "# Derive wind anomaly from temperature anomaly based on the thermal wind balance\n",
+ "\n",
+ "original python script developed by Nicole Albern, KIT, 2022 was set up for a dry atmosphere\n",
+ "\n",
+ "modifications by Christoph Braun, KIT, July 2023 to check the impact of using moist instead of dry atmosphere on the derived wind anomaly"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "52b34f26-53c9-4825-9c59-2241e8073838",
+ "metadata": {},
+ "source": [
+ "## First some steps that can be done w/o distinction between dry and moist atmosphere"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ca547b50-1782-4a29-a1dd-9ad72776ccff",
+ "metadata": {},
+ "source": [
+ "### import required libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "508c09ac-2fc3-4d40-b932-cc5fb2bc969c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "from netCDF4 import Dataset\n",
+ "\n",
+ "import scipy.integrate\n",
+ "\n",
+ "from numba import jit\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "import netCDF4 as nc"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f53ecf1a-b9b9-4b58-a4da-e027a26bf75a",
+ "metadata": {},
+ "source": [
+ "### 1) load information of vertical levels"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "e4d91893-e41b-49b3-9181-3203ff2d3134",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def load_levelinfo():\n",
+ " \n",
+ " fpath = '/work/bb1152/Module_A/A6_CyclEx/input_data/'\n",
+ " file = Dataset(fpath+'ifs2icon_verticalgridinfo_137levels.nc', 'r')\n",
+ " hyam = np.squeeze(np.array(file.variables['hyam']))\n",
+ " hybm = np.squeeze(np.array(file.variables['hybm'])) \n",
+ " hyai = np.squeeze(np.array(file.variables['hyai']))\n",
+ " hybi = np.squeeze(np.array(file.variables['hybi'])) \n",
+ " lev = np.squeeze(np.array(file.variables['lev' ]))\n",
+ " lev_2= np.squeeze(np.array(file.variables['lev_2'])) \n",
+ " return hyam, hybm, hyai, hybi, lev, lev_2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "1cff9154-9357-472e-ada0-4478058c68cf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "hyam, hybm, hyai, hybi, lev, lev_2 = load_levelinfo()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0c806e36-de39-4f68-8334-0827bd0380e4",
+ "metadata": {},
+ "source": [
+ "### 2) define constants\n",
+ "\n",
+ "physical constants are taken from\n",
+ "\n",
+ "- Polvani and Esler, 2007\n",
+ "- Booth et al., 2013 Climate Dynamics\n",
+ "- or set to the values used in icon-nwp-2.0.15/src/shared/mo_physical_constants.f90"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "3ce255cb-d263-48e3-99e3-a3506d3a4cd4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "u0 = 45.0 # in m/s (as in Polvani and Esler, 2007)\n",
+ "zT = 13.0e3 # in m (as in Polvani and Esler, 2007)\n",
+ "H = 7.5e3 # in m (as in Polvani and Esler, 2007)\n",
+ "R = 287.04 # dry gas constant in J/(kg K) (parameter rd in ICON)\n",
+ "a = 6.371229e6 # average Earth radius in m (parameter earth_radius in ICON)\n",
+ "Omega = 7.29212e-5 # angular velocity in 1/s (parameter earth_angular_velocity in ICON) \n",
+ "T0 = 300 # in K (as in Polvani and Esler, 2007)\n",
+ "Gamma0 = -6.5e-3 # in K/m (as in Polvani and Esler, 2007)\n",
+ "alpha = 10 # unitless (as in Polvani and Esler, 2007)\n",
+ "kappa = 2.0/7.0 # unitless (as in Polvani and Esler, 2007)\n",
+ "g = 9.80665 # av. gravitational acceleration in m/s2 (parameter grav in ICON)\n",
+ "p0 = 1.0e5 # globally-uniform surface pressure in Pa (as in Polvani and Esler, 2007)\n",
+ "# for relative humidity following Booth et al., 2013 Climate Dynamics\n",
+ "zTrh = 12.0e3 \n",
+ "rh0 = 0.80 # relative humidity scaling factor from 0..1"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d259d060-dddb-4cc0-bb7a-f36a09967790",
+ "metadata": {},
+ "source": [
+ "### 3) define vertical grid\n",
+ "\n",
+ "for computation of initial state we convert the ifs2icon hybrid levels to height levels assuming a globally-uniform surface pressure (defined above) and defining height according to Polvani and Elsner as z = H ln (p0/p)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "e544d293-6177-44ee-b238-7515c6a45da4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "p = hyam + hybm*p0\n",
+ "z = H*np.log(p0/p) # np.log is natural logarithm\n",
+ "nz = z.size"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4a54dd36-18a2-4cba-9414-0b534bac5b1a",
+ "metadata": {},
+ "source": [
+ "### 4) define latitude-longitude grid"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "c44793a9-87e9-4c14-b556-49d7394dd573",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "lat = np.linspace(-90, 90, 360)\n",
+ "lon = np.linspace(0.0,360,10)\n",
+ "nlat = lat.size\n",
+ "nlon = lon.size\n",
+ "\n",
+ "# latitude in radians\n",
+ "latrad = lat * np.pi/180.0"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8ffa207b-590d-449a-a3bf-74b45be619f2",
+ "metadata": {},
+ "source": [
+ "### 5) define wind field for lifecycle 1 (as in Polvani and Esler, 2007; eqns 6 and 7)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "911aab42-41b4-4e9e-9939-5574883ec008",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "u1 = np.zeros((nz,nlat))+np.nan\n",
+ "F1 = np.power(np.sin(np.pi*np.power(np.sin(latrad),2)),3); F1[lat<0] = 0.0\n",
+ "for i in range(0, nz):\n",
+ " for j in range(0, nlat):\n",
+ " u1[i,j] = u0*F1[j]*(z[i]/zT)*np.exp(-0.5*(np.power(z[i]/zT,2)-1))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ae8ff4b6-2c14-4f15-9733-0fed18cb2b51",
+ "metadata": {},
+ "source": [
+ "### 6) define latitude independent reference temperature profile (as in Polvani and Esler, 2007; eqn A5)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "afb44bf2-d262-4315-a1bd-3a9cb3489c1e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "Tr = np.zeros((nz, nlat)) + np.nan\n",
+ "for i in range(0, nz):\n",
+ " Tr[i, :] = T0 + Gamma0/np.power((np.power(zT,-alpha)+np.power(z[i],-alpha)),1/alpha)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2bc87d98-4965-49bc-b15e-f96ff61fd70c",
+ "metadata": {},
+ "source": [
+ "### 7) compute temperature profile in zonal wind balance with wind field (as in Polvani and Esler, 2007; eqn A4)\n",
+ "#### 7A) define integrand from eqn A4"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "6ab4912e-76ae-41d0-8366-f4ec7ce21b35",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "@jit\n",
+ "def Tintegrand(latrad, z, zT, U0, a, Omega):\n",
+ " f = 2*Omega*np.sin(np.deg2rad(45.0))\n",
+ " F = np.power(np.sin(np.pi*np.power(np.sin(latrad),2)),3)\n",
+ " if latrad<0: F=0.0\n",
+ " u1 = U0*F*(z/zT)*np.exp(-0.5*(np.power(z/zT,2)-1))\n",
+ " du1dz = u1*(1/z-z/np.power(zT,2))\n",
+ " return (a*f+2*u1*np.tan(latrad))*du1dz"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "dbe4016c-a3c0-468b-a88d-62b6967ceb49",
+ "metadata": {},
+ "source": [
+ "#### 7B) integrate"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "20c5c235-7515-4c5f-9316-fb016ae6f09c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "tmp = np.zeros((nz, nlat)) + np.nan\n",
+ "for i in range(0, nz):\n",
+ " for j in range(0, nlat):\n",
+ " tmp[i, j] =scipy.integrate.quad(Tintegrand, 0, latrad[j], args=(z[i], zT, u0, a, Omega))[0]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6496bfe1-a7e1-4618-8ce5-dc58e67f2b1c",
+ "metadata": {},
+ "source": [
+ "#### 7C) add integrand into eqn A4"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "2b446446-2934-4882-86b3-84d3d12258dd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "Tctl=Tr-H/R*tmp"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "49b02313-4ec7-49ce-8a4e-abe75838d792",
+ "metadata": {},
+ "source": [
+ "### 7) define relative humidity profile\n",
+ "\n",
+ "follows Booth et al. 2013, Climate Dynamics "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "c390bbcb-7f33-49d3-a1e3-c11dae7605b6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "rh = np.zeros((nz, nlat)) + np.nan\n",
+ "for i in range(0, nz):\n",
+ " if z[i]>14e3:\n",
+ " rh[i, :] = 0.0\n",
+ " else:\n",
+ " rh[i, :] = rh0*np.power(1-0.85*z[i]/zTrh, 1.25)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bd488fea-deb4-4643-a443-b1cf2312cde5",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "### 8) calculate qv for CTL\n",
+ "\n",
+ "follows calculation in icon \n",
+ "\n",
+ "/icon-nwp-2.0.15/src/atm_phy_schemes/mo_satad.f90 -> sat_pres_water,spec_humi"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "38ab9240-495c-402b-8c2d-5873cd459490",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_qv_0D(T,rh,p):\n",
+ " \n",
+ " # define constants\n",
+ " b1 = 610.78 # --> c1es in mo_convect_tables.f90 \n",
+ " b2w= 17.269 # --> c3les \n",
+ " b3 = 273.15 # --> tmelt; melting temperature in K\n",
+ " b4w= 35.86 # --> c4les\n",
+ "\n",
+ " Rdv = 287.04/461.51 # Rd/Rv; replace Rd by R as values are identical?\n",
+ " o_m_Rdv = 1-Rdv # 1-Rd/Rv\n",
+ " \n",
+ " sat_pres_water = b1*np.exp(b2w*(T-b3)/(T-b4w))\n",
+ "\n",
+ " qv = rh*Rdv*sat_pres_water/(p-o_m_Rdv*sat_pres_water) # Do I understand this equation?\n",
+ " \n",
+ " return qv"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "54958b80-59a2-4775-9b6e-f2ae60e1526f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "qvctl = np.zeros((nz, nlat))\n",
+ "for i in range(0, nz):\n",
+ " for j in range(0, nlat):\n",
+ " qvctl[i, j] = get_qv_0D(Tctl[i,j],rh[i,j],p[i])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5a8c20c0-beeb-4db9-a985-3dfc05f1b106",
+ "metadata": {},
+ "source": [
+ "### 9) calculate Tv for CTL\n",
+ "\n",
+ "based on https://glossary.ametsoc.org/wiki/Virtual_temperature"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "297ce222-4536-4b3e-b87e-1095be5306e9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_Tv(T,qv):\n",
+ " return (1+0.61*qv)*T"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "4a138227-0d93-481c-b909-c30d90c6e981",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "Tvctl = get_Tv(Tctl,qvctl)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "468a54d7-a4d8-45c4-a48b-47b98bc3b22d",
+ "metadata": {},
+ "source": [
+ "### 10) define temperature anomaly"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "8edf20ed-611e-49a4-a70c-dbd9404ec067",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "Tanom = np.ones((nz,nlat))*4.0"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d1d1ed68-71eb-4283-becf-5580f263c997",
+ "metadata": {},
+ "source": [
+ "### 11) calculate T for +4K"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "9ff4eb77-99d5-4e2e-93f6-8500c55bfc23",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "T4K = Tctl + Tanom"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8177f681-7c0e-4c55-a45c-b5b752ecb408",
+ "metadata": {},
+ "source": [
+ "### 12) calculate qv for +4K"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "3b0561da-b72d-4532-b190-8dfa472da843",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "qv4K = np.zeros((nz, nlat))\n",
+ "for i in range(0, nz):\n",
+ " for j in range(0, nlat):\n",
+ " qv4K[i, j] = get_qv_0D(T4K[i,j],rh[i,j],p[i])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d6fc03d6-f36f-473b-9640-7bb832e6b351",
+ "metadata": {},
+ "source": [
+ "### 13) calculate Tv for +4K"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "eb02a8b8-af48-44a5-9904-45b2ffbea6d5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "Tv4K = get_Tv(T4K,qv4K)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0734b53a-85fa-48e1-911b-117e34b91839",
+ "metadata": {},
+ "source": [
+ "### 14) Calculate Tv anomaly"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "865aa06b-eebb-4d2f-8652-c69bdd731cac",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dTv = Tv4K - Tvctl"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "dd5684aa-3a14-41e6-b8fe-3568c8202347",
+ "metadata": {},
+ "source": [
+ "### 15) calculate zonal wind anomaly based on temperature anomaly"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b34361e7-e768-442b-95ff-04490a7ab50a",
+ "metadata": {},
+ "source": [
+ "#### 15A) define function to calculate the meridional gradient of variable \"var\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "id": "08e4686e-8d9e-48c8-a094-67ce5118df21",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_dxdlat(var, lats, levs):\n",
+ " nlat = lats.size # length of latitude vector\n",
+ " # calculate distance between entries of latitude vector\n",
+ " dlat = np.full(lats.shape, np.nan, dtype=float)\n",
+ " dlat[0] = np.abs(lats[1] - lats[0])\n",
+ " # distance between lat[2]-lat[0], lat[3]-lat[1], etc.\n",
+ " dlat[1:nlat-1] = np.abs(lats[2:nlat] - lats[0:nlat-2])\n",
+ " dlat[nlat-1] = np.abs(lats[nlat-1] - lats[nlat-2])\n",
+ "\n",
+ " # check that first dimension is levs and second dimension is lats\n",
+ " if (var.shape[0] == levs.size) and (var.shape[1] == lats.size):\n",
+ " # check that latitudes go from south to north\n",
+ " if lats[0] > lats[1]:\n",
+ " var = var[:, ::-1]\n",
+ " lats = lats[::-1]\n",
+ " print('changed lats and var')\n",
+ "\n",
+ " # centered finite differences\n",
+ " var_grad = np.full(var.shape, np.nan, dtype=float)\n",
+ " var_grad[:, 0] = (var[:, 1] - var[:, 0]) / dlat[0]\n",
+ " for la in range(1, nlat-1):\n",
+ " var_grad[:, la] = (var[:, la+1] - var[:, la-1]) / dlat[la]\n",
+ " del la\n",
+ " var_grad[:, nlat-1] = (var[:, nlat-1] - var[:, nlat-2]) / dlat[nlat-1]\n",
+ " else:\n",
+ " print('ERROR: Dimensions are not lat and lev. ' + \\\n",
+ " 'Exit function get_dxdlat')\n",
+ " return\n",
+ "\n",
+ " return var_grad"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "565b091a-aabe-4d1c-bb1c-2b4c85bf4cd5",
+ "metadata": {},
+ "source": [
+ "#### 15B) define function to calculate the zonal wind from atm. temperature following thermal wind balance"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "id": "3b0cf4fe-daa3-43fa-9ef8-42642e2f7469",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_u_from_t(tanom, z, lat, latrad, bsmooth=False, bgradout=False):\n",
+ "# input: tanom: atmospheric temperature anomaly, dimensions (zin-lat)\n",
+ "# tropo: tropopause height in m\n",
+ "# zin: vertical levels of temperature field in m\n",
+ "# zout: vertical levels of zonal wind field in m\n",
+ "# lat: latitudes of temperature field in degree\n",
+ "# latrad: latitudes in radian\n",
+ "\n",
+ " # 1. prepare the temperature field\n",
+ " # 1.1 We want to derive the zonal wind from the surface to TOA.\n",
+ " # If levels do not go from surface to TOA, change them.\n",
+ " if z[0] > z[1]:\n",
+ " print('change order of z to go from surface to TOA')\n",
+ " tanom_calc = tanom[::-1, :]\n",
+ " z_calc = z[::-1]\n",
+ " else:\n",
+ " tanom_calc = tanom.copy()\n",
+ " z_calc = z.copy()\n",
+ " del tanom, z\n",
+ "\n",
+ " ################################################################### \n",
+ " # 2. calculate the zonal wind field\n",
+ " # 2.1 helper variables\n",
+ " f = 2*Omega*np.sin(np.deg2rad(45.0))\n",
+ " var_a = -1*H*a*f/R\n",
+ " var_b = -2*H*np.tan(latrad)/R\n",
+ "\n",
+ " # 2.2 meridional temperature gradient\n",
+ " dTdlat = get_dxdlat(tanom_calc, latrad, z_calc)\n",
+ " \n",
+ " # smooth the meridional temperature gradient; cehck whether needed\n",
+ " if bsmooth:\n",
+ " window = np.ones(20)/20\n",
+ " dTdlat_runmean = np.full(dTdlat.shape, np.nan, dtype=float)\n",
+ " for le in range(len(zin_int)):\n",
+ " dTdlat_runmean[le, :] = np.convolve(dTdlat[le, :], window, 'same')\n",
+ " del le, window\n",
+ " del dTdlat\n",
+ " dTdlat = dTdlat_runmean\n",
+ " \n",
+ " # 2.3 difference between levels\n",
+ " # array has one entry less than z: first entry is\n",
+ " # the difference between the first and second level\n",
+ " dz = np.diff(z_calc)\n",
+ "\n",
+ " # 2.4 calculate zonal wind\n",
+ " uanom_int = np.full(tanom_calc.shape, np.nan, dtype=float)\n",
+ "\n",
+ " # no wind at the surface\n",
+ " uanom_int[0, :] = 0.0\n",
+ "\n",
+ " # other levels\n",
+ " for le in range(1, len(z_calc)): # loop over levels\n",
+ " uanom_int[le, :] = (dTdlat[le-1,:] + \\\n",
+ " var_a * uanom_int[le-1,:] / dz[le-1] + \\\n",
+ " var_b * uanom_int[le-1,:] * uanom_int[le-1,:] / dz[le-1]) * \\\n",
+ " dz[le-1] / (var_a + var_b * uanom_int[le-1,:])\n",
+ " del le\n",
+ " del f, var_a, var_b, dz\n",
+ " \n",
+ " # 3 invert vertical order of wind field again (similar for gradient below)\n",
+ " uanom = uanom_int[::-1, :]\n",
+ " \n",
+ " if bgradout:\n",
+ " dTdlat_flipped = dTdlat[::-1, :]\n",
+ " return uanom, dTdlat_flipped\n",
+ " else:\n",
+ " return uanom"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f3f1a20a-94fc-4b89-8660-820d63e70a47",
+ "metadata": {},
+ "source": [
+ "#### 15C) Check procedure by recalculating u1 for CTL"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "id": "7b4bcfd3-1351-4592-8ce4-4818f71b124b",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "change order of z to go from surface to TOA\n"
+ ]
+ }
+ ],
+ "source": [
+ "[u1ctl, dTdlat] = get_u_from_t(Tctl, z, lat, latrad, bgradout=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 68,
+ "id": "88eb6ae2-7b3b-4df9-953b-8f276af2c0b8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plot_zonfield(var,clabel,vmin=None,vmax=None,levels=None,cmap='viridis',ymax=50,alpha=1):\n",
+ " \n",
+ " if (vmin or vmax or levels):\n",
+ " levels = np.linspace(vmin,vmax,levels)\n",
+ " cbar = plt.contourf(lat,z/1e3,var,levels=levels,cmap=cmap,alpha=alpha)\n",
+ " else:\n",
+ " cbar = plt.contourf(lat,z/1e3,var,cmap=cmap,alpha=alpha)\n",
+ " \n",
+ " plt.xlim(0,90)\n",
+ " plt.ylim(0,ymax)\n",
+ " plt.xlabel('latitude [°N]')\n",
+ " plt.ylabel('height [km]')\n",
+ " \n",
+ " plt.colorbar(cbar,label=clabel)\n",
+ " \n",
+ " return"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 84,
+ "id": "d69270cf-407b-4ca3-a41f-41bd107cc34a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plot_zonfield_contour(var,color='k',vmin=None,vmax=None,levels=None,cmap='viridis',ymax=50):\n",
+ " \n",
+ " lw=1\n",
+ " \n",
+ " if (vmin or vmax or levels):\n",
+ " levels = np.linspace(vmin,vmax,levels)\n",
+ " plt.contour(lat,z/1e3,var,levels=levels,colors=color,linewidths=lw)\n",
+ " else:\n",
+ " plt.contour(lat,z/1e3,var,colors=color,linewidths=lw)\n",
+ " \n",
+ " plt.xlim(0,90)\n",
+ " plt.ylim(0,ymax)\n",
+ " plt.xlabel('latitude [°N]')\n",
+ " plt.ylabel('height [km]')\n",
+ " \n",
+ " return"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 106,
+ "id": "ec29fed6-8218-461c-845c-a3cb4b635adf",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_zonfield(Tctl,'T for CTL [K]',alpha=1)\n",
+ "plot_zonfield_contour(u1ctl,vmin=0,vmax=50,levels=11)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 107,
+ "id": "647fffe8-9c07-43f0-8604-951823bb4fe9",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_zonfield(dTdlat,'meridional temperature gradient [K/°]',vmin=-100,vmax=100,levels=21)\n",
+ "plot_zonfield_contour(u1ctl,vmin=0,vmax=50,levels=11)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 108,
+ "id": "d8ae6f18-cc48-403b-b9af-bf2a00fafe0e",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_zonfield(u1ctl-u1,'zonal-wind for CTL (recalc.) - defined LC1 wind field [m/s]',vmin=-1.5,vmax=1.5,levels=21,cmap='RdBu_r')\n",
+ "plot_zonfield_contour(u1ctl,vmin=0,vmax=50,levels=11)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 109,
+ "id": "42285b1a-d32d-4bc7-ae6e-fbd0f7911ebe",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/tmp/ipykernel_903761/1758597521.py:2: RuntimeWarning: invalid value encountered in divide\n",
+ " rel_diff_u1ctl = np.where(u1>0.1,(u1ctl-u1)/u1*100,np.nan)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# mask values with very low u1\n",
+ "rel_diff_u1ctl = np.where(u1>0.1,(u1ctl-u1)/u1*100,np.nan)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 110,
+ "id": "497b8297-57a2-4f7b-9f93-67b9b1e6fc82",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_zonfield(rel_diff_u1ctl,'zonal-wind for CTL (recalc.) - defined LC1 wind field [%]',vmin=-20,vmax=20,levels=20,cmap='RdBu_r')\n",
+ "plot_zonfield_contour(u1ctl,vmin=0,vmax=50,levels=11)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8326a03a-c7bb-46bd-8a4b-4c494ed5d30d",
+ "metadata": {},
+ "source": [
+ "Seems to be roughly ok. Or do we expect exact matching?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b0193996-98a9-4efd-ba98-9939709a3cec",
+ "metadata": {},
+ "source": [
+ "#### 15D) Calculate u-anomaly and u+4K"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "id": "647ecc90-82af-4e9d-8d24-de0ff8d95eec",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "change order of z to go from surface to TOA\n"
+ ]
+ }
+ ],
+ "source": [
+ "uanom = get_u_from_t(dTv, z, lat, latrad)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "id": "58932122-f553-4945-9513-38ba7cd54abc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "u4K = u1 + uanom"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "84177111-d7fb-4c6d-b05c-44f98cee4165",
+ "metadata": {},
+ "source": [
+ "#### 15E) calculate absolute wind field from Tv+4K"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "id": "d5e48c56-cf2d-4737-8933-3c4f06599a42",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "change order of z to go from surface to TOA\n"
+ ]
+ }
+ ],
+ "source": [
+ "u4K_direct = get_u_from_t(Tv4K, z, lat, latrad)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6e200153-494c-4879-ba39-8011ba6f3178",
+ "metadata": {},
+ "source": [
+ "### 16) Plot results"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 114,
+ "id": "871de04f-4d2f-42dd-b3d3-2f48a0221a3b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_zonfield(dTv,'delta Tv [K]',vmin=4,vmax=5,levels=21,ymax=50)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "id": "d0547c5c-e58a-4aa5-b182-26cb3f8cdaf8",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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eHo7S0tJG+3fq1Anz5s3D0aNHcf78eSxZsgRLlizBpk2bVH2OHz+OiIgIREZG4tSpU4iMjMSUKVNw4sSJ1josDQw3RERE7UR6ejpmzZqF2bNno2/fvsjIyICLiwsyMzMb7e/v74+pU6eiX79+cHd3xxtvvIFx48apzfZkZGQgNDQU8fHx8PHxQXx8PMaMGYOMjIxWOipNDDdERETtQE1NDQoLCxEWFqbWHhYWhoKCAq3GKCoqQkFBAUaNGqVqO378uMaY48aN03pMYzCrd0sRERGRbpRKpdp3qVQKqVSq0a+yshJ1dXWQy+Vq7XK5HAqFotl99OrVCzdu3EBtbS2SkpIwe/Zs1TaFQqHXmMbEcENERGQCh68/DasqzRCirdq71QAOwsXFRa09MTGx2RdOSyQSte+CIGi0Pe7YsWOoqqrCt99+i8WLF6N3796YOnVqi8Y0JoYbIiKiNqysrAwymUz1vbFZGwDo0aMHLC0tNWZUKioqNGZeHufh4QEA6N+/P65fv46kpCRVuHF0dNRrTGPimhsiIqI2TCaTqX2aCjfW1tYICAhAbm6uWntubi6Cg4O13p8gCKiurlZ9DwoK0hjz4MGDOo1paJy5ISIiaifi4uIQGRmJwMBABAUFYdOmTSgtLUV0dDQAID4+HteuXcPWrVsBAOvWrYOrqyt8fHwAPHzuzQcffID58+erxlywYAFGjhyJFStWYMKECdizZw8OHTqE/Pz81j/A/4fhhoiIqJ2IiIjAzZs3kZKSgvLycvj5+WH//v1wc3MDAJSXl6s986a+vh7x8fEoKSmBlZUVvLy8sHz5crz11luqPsHBwdixYweWLFmC9957D15eXsjJycGwYcNa/fgaMNwQERG1IzExMYiJiWl0W1ZWltr3+fPnq83SNGXy5MmYPHmyIcozCK65ISIiIlFhuCEiIiJRYbghIiIiUWG4ISIiIlFhuCEiIiJRYbghIiIiUWG4ISIiIlFhuCEiIiJRYbghIiIiUWG4ISIiIlFhuCEiIiJRYbghIiIiUWG4ISIiIlFhuCEiIiJRYbghIiIiUWG4ISIiIlFhuCEiIiJRYbghIiIiUWG4ISIiIlFhuCEiIiJRYbghIiIiUWG4ISIiIlFhuCEiIiJRYbghIiIiUWG4ISIiIlFhuCEiIiJRYbghIiIiUWG4ISIiIlFhuCEiIiJRYbghIiIiUWG4ISIiIlFhuCEiIiJRYbghIiIiUWG4ISIiIlFhuCEiIiJRYbghIiIiUWG4ISIiakfWr18PDw8P2NjYICAgAMeOHWuy765duxAaGgoHBwfIZDIEBQXhwIEDan2ysrIgkUg0Pvfv3zf2oTSJ4YaIiKidyMnJQWxsLBISElBUVIQRI0YgPDwcpaWljfY/evQoQkNDsX//fhQWFuLZZ5/FSy+9hKKiIrV+MpkM5eXlah8bG5vWOKRGWZlsz0RERNSq0tPTMWvWLMyePRsAkJGRgQMHDiAzMxNpaWka/TMyMtS+p6amYs+ePdi3bx/8/f1V7RKJBI6OjkatXRecuSEiImrDlEql2qe6urrRfjU1NSgsLERYWJhae1hYGAoKCrTaV319Pe7cuYNu3bqptVdVVcHNzQ29evXCiy++qDGz09o4c0NERGQCpf/tAQtb/S/d1N97uKbFxcVFrT0xMRFJSUka/SsrK1FXVwe5XK7WLpfLoVAotNrnhx9+iLt372LKlCmqNh8fH2RlZaF///5QKpVYvXo1QkJCcOrUKXh7e+t4VIbBcENERNSGlZWVQSaTqb5LpdJm+0skErXvgiBotDVm+/btSEpKwp49e9CzZ09V+/DhwzF8+HDV95CQEAwePBgfffQR1qxZo+1hGBTDDRERURsmk8nUwk1TevToAUtLS41ZmoqKCo3ZnMfl5ORg1qxZ+PzzzzF27Nhm+1pYWGDIkCG4ePHik4s3Eq65ISIiagesra0REBCA3Nxctfbc3FwEBwc3+bvt27djxowZyM7Oxvjx45+4H0EQUFxcDCcnpxbXrC/O3BAREbUTcXFxiIyMRGBgIIKCgrBp0yaUlpYiOjoaABAfH49r165h69atAB4Gm+nTp2P16tUYPny4atbH1tYW9vb2AIDk5GQMHz4c3t7eUCqVWLNmDYqLi7Fu3bpm69DVkiVLNBYyN8Wk4SYzMxOZmZm4cuUKAKBfv354//33ER4eDuBh+ktOTsamTZvw22+/YdiwYVi3bh369etnwqqJiIjapoiICNy8eRMpKSkoLy+Hn58f9u/fDzc3NwBAeXm52jNvNm7ciNraWsydOxdz585VtUdFRSErKwsAcOvWLcyZMwcKhQL29vbw9/fH0aNHMXTo0CbryMjIQFBQEKytrbWqOz8/H/PmzWsb4aZXr15Yvnw5evfuDQD47LPPMGHCBBQVFaFfv35YuXIl0tPTkZWVhaeffhrLli1DaGgoLly4ADs7O1OWTkRE1CbFxMQgJiam0W0NgaXBkSNHnjjeqlWrsGrVKp3r2L17t9rC5Oboes7XKtwolUqdBgWg1eKml156Se37X/7yF2RmZuLbb7+Fr68vMjIykJCQgEmTJgF4GH7kcjmys7Px1ltv6VwTERERmd6WLVtUl7W0sXHjxicuen6UVuGmS5cuWt0m1kAikeDnn3+Gp6en1r+pq6vD559/jrt37yIoKAglJSVQKBRqDxuSSqUYNWoUCgoKmgw31dXVag8w0ieYERERkfFERUXp1H/atGk69df6stQ//vEPra51CYKAF154QesCTp8+jaCgINy/fx+dO3fG7t274evrq3paYmMPG7p69WqT46WlpSE5OVnr/RMREZHplJWVQSKRoFevXgCA7777DtnZ2fD19cWcOXP0GlOrcOPm5oaRI0eie/fuWg3q6emJDh06aNW3T58+KC4uxq1bt7Bz505ERUUhLy9PtV3Xhw3Fx8errcJWKpUaT28kIiIi8zBt2jTMmTMHkZGRUCgUCA0NRb9+/bBt2zYoFAq8//77Oo+p1XNuSkpKtA42AHDmzBmtA4W1tTV69+6NwMBApKWlYeDAgVi9erXqBVy6PmxIKpWqHmik7YONiIiIyDTOnDmjurPq73//O/z8/FBQUIDs7GyNBc7aMruH+AmCgOrqanh4eMDR0VHtYUM1NTXIy8tr9mFDRERE1HY8ePBA9cqIQ4cO4Q9/+AOAh++sKi8v12tMvW4F/+6773DkyBFUVFSgvr5ebVt6errW47z77rsIDw+Hi4sL7ty5gx07duDIkSP46quvIJFIEBsbi9TUVHh7e8Pb2xupqano2LGjzguLiIiIyDz169cPGzZswPjx45Gbm4ulS5cCAP773//qdNXoUTqHm9TUVCxZsgR9+vSBXC5XW/+iyx1VAHD9+nVERkaivLwc9vb2GDBgAL766iuEhoYCABYtWoR79+4hJiZG9RC/gwcP8hk3REREIrFixQq8/PLL+N///V9ERUVh4MCBAIC9e/c2+yDA5ugcblavXo1PP/0UM2bM0GuHj9q8eXOz2yUSCZKSkhp9dTsRERG1XVVVVejcuTNGjx6NyspKKJVKdO3aVbV9zpw56Nixo15j67zmxsLCAiEhIXrtjIiIiAh4+Jby8PBwZGZm4vr162rBBgDc3d21foLx43QONwsXLmz2ZVhERERET3LhwgW88MIL2LlzJzw8PDBkyBAsXboUP/74Y4vH1vmy1Ntvv43x48fDy8sLvr6+Gs+z2bVrV4uLIiIiInFzc3PD/PnzMX/+fNy+fRv79+/Hnj178OGHH6Jr1674wx/+gAkTJmDUqFGwtLTUaWydZ27mz5+Pw4cP4+mnn0b37t1hb2+v9iEiIiLShb29PaZOnYodO3agsrISGzduRH19Pf74xz/CwcEBf/vb33QaT+eZm61bt2Lnzp0YP368rj8lIiIiapaVlRXCwsIQFhaGjz76CEVFRaitrdVtDF132q1bN3h5een6MyIiIqJG3b9/Hz/++KPG8/MkEgleeuklncfTOdwkJSUhMTERW7Zs0fsWLSIiIiIA+OqrrzB9+nRUVlZqbJNIJKirq9N5TJ3DzZo1a3Dp0iXI5XK4u7trLCj+4YcfdC6CiIiI2qd58+bh1Vdfxfvvv9/suyN1oXO4mThxokF2TERERFRRUYG4uDiDBRtAj3CTmJjY5DZBEFpUDBEREbUvkydPxpEjRwy6nlfncJOWlob4+HiN9rq6OrzxxhvYvn27QQojIiIi8Vu7di1effVVHDt2DP3799dY7vKnP/1J5zF1DjcZGRno3r075syZo2qrq6vDa6+9hjNnzuhcABEREbVf2dnZOHDgAGxtbXHkyBGNF3K3SrjZv38/xo4diy5dumDKlCl48OABIiIi8NNPP+Hw4cM6F0BERETt15IlS5CSkoLFixfDwkLnZws3SudwExAQgN27d2PChAmQSqXYvHkzLl26hMOHDxt0MRARERGJX01NDSIiIgwWbAA9Xr8AAKNHj8Zf//pXTJ48GVeuXEFeXh6DDREREeksKioKOTk5Bh1Tq5mbSZMmNdru4OCALl26qK2/4YsziYiISFt1dXVYuXIlDhw4gAEDBmgsKE5PT9d5TK3CTVMvxBw3bpzOOyQiIiJqcPr0afj7+wOAxo1Jjy4u1oVW4WbLli16DU5ERETUHGPcjGS41TtEREREZkCrcDN48GD89ttvWg/6zDPP4Nq1a3oXRUREROI1adIkKJVKrfu//vrrqKio0Lq/VpeliouLcerUKXTr1k2rQYuLi1FdXa11EURERNR+7NmzBzdu3NCqryAI2LdvH5YuXYqePXtq9Rutn3MzZswYrd8dpe8CICIiIhI/QRDw9NNPG218rcJNSUmJzgP36tVL598QERGR+OmziPipp57Suq9W4cbNzU3nIoiIiIgaM2rUKKOOz7uliIiISFQYboiIiEhUGG6IiIjakfXr18PDwwM2NjYICAjAsWPHmuy7a9cuhIaGwsHBATKZDEFBQThw4IBGv507d8LX1xdSqRS+vr7YvXu3MQ/hiRhuiIiI2omcnBzExsYiISEBRUVFGDFiBMLDw1FaWtpo/6NHjyI0NBT79+9HYWEhnn32Wbz00ksoKipS9Tl+/DgiIiIQGRmJU6dOITIyElOmTMGJEyda67A06BxuPD09cfPmTY32W7duwdPT0yBFERERkeGlp6dj1qxZmD17Nvr27YuMjAy4uLggMzOz0f4ZGRlYtGgRhgwZAm9vb6SmpsLb2xv79u1T6xMaGor4+Hj4+PggPj4eY8aMQUZGRisdlSadw82VK1dQV1en0V5dXc2nEhMREbUypVKp9mnqIbo1NTUoLCxEWFiYWntYWBgKCgq02ld9fT3u3Lmj9lDf48ePa4w5btw4rce8efMm5s6dC19fX/To0QPdunVT++hD64f47d27V/XnAwcOqL0pvK6uDl9//TXc3d31KoKIiKi9sS6zhqWNtd6/r7tfDwBwcXFRa09MTERSUpJG/8rKStTV1UEul6u1y+VyKBQKrfb54Ycf4u7du5gyZYqqTaFQtGjMN954A5cuXcKsWbMgl8sN8iBgrcPNxIkTATx8+nBUVJTatg4dOsDd3R0ffvhhiwsiIiIi7ZWVlUEmk6m+S6XSZvs/Hh4EQdAqUGzfvh1JSUnYs2ePxmsQ9B0TAPLz85Gfn4+BAwdq1V8bWoeb+vqHCdHDwwMnT55Ejx49DFYEERER6Ucmk6mFm6b06NEDlpaWGjMqFRUVGjMvj8vJycGsWbPw+eefY+zYsWrbHB0d9RqzgY+PD+7du6dVX23pvOampKSEwYaIiKiNsba2RkBAAHJzc9Xac3NzERwc3OTvtm/fjhkzZiA7Oxvjx4/X2B4UFKQx5sGDB5sd81Hr169HQkIC8vLycPPmTY01RPrQeubmUV9//TW+/vprVFRUqGZ0Gnz66ad6FUJERETGFRcXh8jISAQGBiIoKAibNm1CaWkpoqOjAQDx8fG4du0atm7dCuBhsJk+fTpWr16N4cOHq2ZobG1tVWtvFyxYgJEjR2LFihWYMGEC9uzZg0OHDiE/P1+rmrp06YLbt2/jueeeU2tvuLTV2E1MT6JzuElOTkZKSgoCAwPh5OTEN4ATERG1EREREbh58yZSUlJQXl4OPz8/7N+/X/UOyfLycrVn3mzcuBG1tbWYO3cu5s6dq2qPiopCVlYWACA4OBg7duzAkiVL8N5778HLyws5OTkYNmyYVjW9/vrrsLa2RnZ2dusvKG6wYcMGZGVlITIyssU7JyIiotYVExODmJiYRrc1BJYGR44c0WrMyZMnY/LkyXrVc+bMGRQVFaFPnz56/b4xOq+5qamp0fo6GhEREVFzAgMDUVZWZtAxdQ43s2fPRnZ2tkGLICIiovZp/vz5WLBgAbKyslBYWIgff/xR7aMPrS5LxcXFqf5cX1+PTZs24dChQxgwYAA6dOig1jc9PV2vQoiIiKj9iYiIAADMnDlT1SaRSIy/oPjRF2QBwKBBgwA8vE72KC4uJiIiIl2UlJQYfEytws3hw4cNvmMiIiKihju1DEmv59wQERERGcqlS5eQkZGB8+fPQyKRoG/fvliwYAG8vLz0Gk/ncPPyyy83evlJIpHAxsYGvXv3xrRp0wx6SxcRERGJ04EDB/CHP/wBgwYNQkhICARBQEFBAfr164d9+/YhNDRU5zF1vlvK3t4e33zzDX744QdVyCkqKsI333yD2tpa5OTkYODAgfj3v/+tczFERETUvixevBgLFy7EiRMnkJ6ejlWrVuHEiROIjY3FO++8o9eYOocbR0dHTJs2DZcvX8bOnTuxa9cuXLp0CW+88Qa8vLxw/vx5REVF6V0QERERtR/nz5/HrFmzNNpnzpyJc+fO6TWmzuFm8+bNiI2NhYXF//+phYUF5s+fj02bNkEikWDevHkad1IRERERPc7BwQHFxcUa7cXFxejZs6deY+q85qa2thY//fQTnn76abX2n376SXUvuo2NDW8LJyIioid68803MWfOHFy+fBnBwcGQSCTIz8/HihUr8Oc//1mvMXUON5GRkZg1axbeffddDBkyBBKJBN999x1SU1Mxffp0AEBeXh769eunV0FERETUfrz33nuws7PDhx9+iPj4eACAs7MzkpKS8Kc//UmvMXUON6tWrYJcLsfKlStx/fp1AIBcLsfChQtV62zCwsLw/PPP61UQERERtR8SiQQLFy7EwoULcefOHQCAnZ1di8bUOdxYWloiISEBCQkJUCqVAACZTKbWx9XVtUVFERERUfvT0lDTQOcFxY+SyWQawYaIiIhIW9evX0dkZCScnZ1hZWUFS0tLtY8+tJq5GTx4ML7++mt07doV/v7+zS4W/uGHH/QqhIiIiNqfGTNmoLS0FO+99x6cnJwMckOSVuFmwoQJkEqlAICJEye2eKdEREREAJCfn49jx46pXsptCFqFm8TExEb/TERERNQSLi4uEATBoGPqtebm1q1b+OSTTxAfH49ff/0VwMPLUdeuXTNocURERCRuGRkZWLx4Ma5cuWKwMXW+W+rHH3/E2LFjYW9vjytXruDNN99Et27dsHv3bly9ehVbt241WHFEREQkbhEREfj999/h5eWFjh07okOHDmrbGyZRdKFzuImLi8OMGTOwcuVKtVu2wsPDMW3aNJ0LICIiovYrIyPD4GPqHG5OnjyJjRs3arQ/9dRTUCgUBimKiIiI2oeoqCiDj6lzuLGxsVE9vO9RFy5cgIODg0GKIiIiovbn3r17ePDggVqbPs/T03lB8YQJE5CSkqLauUQiQWlpKRYvXoxXXnlF5wKIiIio/bp79y7mzZuHnj17onPnzujatavaRx86h5sPPvgAN27cQM+ePXHv3j2MGjUKvXv3hp2dHf7yl7/oVQQRERG1T4sWLcI333yD9evXQyqV4pNPPkFycjKcnZ31vklJ58tSMpkM+fn5+Oabb/DDDz+gvr4egwcPxtixY/UqgIiIiNqvffv2YevWrRg9ejRmzpyJESNGoHfv3nBzc8Pf/vY3vP766zqPqfe7pZ577jm8/fbbWLRokd7BJi0tDUOGDIGdnR169uyJiRMn4sKFC2p9BEFAUlISnJ2dYWtri9GjR+Ps2bP6lk1ERERm5Ndff4WHhweAhxMoDbd+P/PMMzh69KheY+o8cwMAX3/9Nb7++mtUVFSgvr5ebdunn36q9Th5eXmYO3cuhgwZgtraWiQkJCAsLAznzp1Dp06dAAArV65Eeno6srKy8PTTT2PZsmUIDQ3FhQsXDPb2UCIiIjINT09PXLlyBW5ubvD19cXf//53DB06FPv27UOXLl30GlPncJOcnIyUlBQEBga2+AVXX331ldr3LVu2oGfPnigsLMTIkSMhCAIyMjKQkJCASZMmAQA+++wzyOVyZGdn46233tJ730RERGR6f/zjH3Hq1CmMGjUK8fHxGD9+PD766CPU1tYiPT1drzF1DjcbNmxAVlYWIiMj9dphc27fvg0A6NatGwCgpKQECoUCYWFhqj5SqRSjRo1CQUFBo+Gmuroa1dXVqu+N3bZORERE5mHhwoWqPz/77LP46aef8P3338PLywsDBw7Ua0ydw01NTQ2Cg4P12llzBEFAXFwcnnnmGfj5+QGA6qGAcrlcra9cLsfVq1cbHSctLQ3JyckGr4+IiIiMz9XVFa6uri0aQ+dwM3v2bGRnZ+O9995r0Y4fN2/ePPz444/Iz8/X2Pb4pS9BEJq8HBYfH4+4uDjVd6VSCRcXF4PWSkRERIaRkpLS7Pb3339f5zG1CjePhoX6+nps2rQJhw4dwoABAzRecKXP9bH58+dj7969OHr0KHr16qVqd3R0BPBwBsfJyUnVXlFRoTGb00AqlUIqlepcAxEREbW+3bt3q31/8OABSkpKYGVlBS8vL+OFm6KiIrXvgwYNAgCcOXNGrV3XxcWCIGD+/PnYvXs3jhw5oroVrIGHhwccHR2Rm5sLf39/AA8vi+Xl5WHFihU67YuIiIjMz+MZA3h41WXGjBl4+eWX9RpTq3Bz+PBhvQZ/krlz5yI7Oxt79uyBnZ2dao2Nvb09bG1tIZFIEBsbi9TUVHh7e8Pb2xupqano2LEj30BOREQkUjKZDCkpKXjxxRf1uoFJ74f4GUJmZiZu376N0aNHw8nJSfXJyclR9Vm0aBFiY2MRExODwMBAXLt2DQcPHuQzboiIiPSwfv16eHh4wMbGBgEBATh27FiTfcvLyzFt2jT06dMHFhYWiI2N1eiTlZUFiUSi8bl//36L6rx165bqLmpd6fUQP0MRBOGJfSQSCZKSkpCUlGT8goiIiEQsJycHsbGxWL9+PUJCQrBx40aEh4fj3Llzjd6hVF1dDQcHByQkJGDVqlVNjiuTyTTeMGBjY6NVTWvWrFH7LggCysvL8de//hXPP/+8VmM8zqThhoiIiFpPeno6Zs2ahdmzZwMAMjIycODAAWRmZiItLU2jv7u7O1avXg2g+TcQSCQS1U1Auno8NFlYWMDBwQFRUVGIj4/Xa0yGGyIiojbs8YfVNnXXcE1NDQoLC7F48WK19rCwMBQUFLSohqqqKri5uaGurg6DBg3C0qVLVTcCPUlJSUmL9t0YhhsiIiITsCsTYGn95OUZTamrefjbx5/llpiY2OhSjsrKStTV1TX6YNyGG3r04ePjg6ysLPTv3x9KpRKrV69GSEgITp06BW9vb73HbQmGGyIiojasrKwMMplM9f1Jz3rT5cG42hg+fDiGDx+u+h4SEoLBgwfjo48+0lhP01oYboiIiNowmUymFm6a0qNHD1haWmrM0jT3YFx9WFhYYMiQIbh48aLBxtS5BpPtmYiIiFqNtbU1AgICkJubq9aem5tr0HdGCoKA4uJitTcLtDbO3BAREbUTcXFxiIyMRGBgIIKCgrBp0yaUlpYiOjoawMP3M167dg1bt25V/aa4uBjAw0XDN27cQHFxMaytreHr6wsASE5OxvDhw+Ht7Q2lUok1a9aguLgY69ata/Xja8BwQ0RE1E5ERETg5s2bSElJQXl5Ofz8/LB//364ubkBePjQvtLSUrXfPHrXU2FhIbKzs+Hm5oYrV64AePiwvTlz5kChUMDe3h7+/v44evQohg4d2mrH9TiGGyIionYkJiYGMTExjW7LysrSaHvSA3dXrVrV7AP+TIFrboiIiEhUGG6IiIhIVBhuiIiISFQYboiIiEhUGG6IiIhIVBhuiIiISFQYboiIiEhUGG6IiIhIVBhuiIiISFQYboiIiEhUGG6IiIhIVBhuiIiISFQYboiIiEhUGG6IiIhIVBhuiIiISFQYboiIiEhUGG6IiIhIVBhuiIiISFQYboiIiEhUGG6IiIhIVBhuiIiISFQYboiIiEhUGG6IiIhIVBhuiIiISFQYboiIiEhUGG6IiIhIVBhuiIiISFQYboiIiEhUGG6IiIhIVBhuiIiISFQYboiIiEhUGG6IiIhIVBhuiIiISFQYboiIiEhUGG6IiIhIVBhuiIiI2pH169fDw8MDNjY2CAgIwLFjx5rsW15ejmnTpqFPnz6wsLBAbGxso/127twJX19fSKVS+Pr6Yvfu3UaqXjsMN0RERO1ETk4OYmNjkZCQgKKiIowYMQLh4eEoLS1ttH91dTUcHByQkJCAgQMHNtrn+PHjiIiIQGRkJE6dOoXIyEhMmTIFJ06cMOahNIvhhoiIqJ1IT0/HrFmzMHv2bPTt2xcZGRlwcXFBZmZmo/3d3d2xevVqTJ8+Hfb29o32ycjIQGhoKOLj4+Hj44P4+HiMGTMGGRkZRjyS5jHcEBERtWFKpVLtU11d3Wi/mpoaFBYWIiwsTK09LCwMBQUFeu//+PHjGmOOGzeuRWO2lJXJ9kxERNSOyUqqYWUl0fv3tbUPQ4yLi4tae2JiIpKSkjT6V1ZWoq6uDnK5XK1dLpdDoVDoXYdCoTD4mC3FcENERNSGlZWVQSaTqb5LpdJm+0sk6oFKEASNNl0ZY8yWYLghIiJqw2QymVq4aUqPHj1gaWmpMaNSUVGhMfOiC0dHR4OP2VJcc0NERNQOWFtbIyAgALm5uWrtubm5CA4O1nvcoKAgjTEPHjzYojFbijM3RERE7URcXBwiIyMRGBiIoKAgbNq0CaWlpYiOjgYAxMfH49q1a9i6davqN8XFxQCAqqoq3LhxA8XFxbC2toavry8AYMGCBRg5ciRWrFiBCRMmYM+ePTh06BDy8/Nb/fgaMNwQERG1ExEREbh58yZSUlJQXl4OPz8/7N+/H25ubgAePrTv8Wfe+Pv7q/5cWFiI7OxsuLm54cqVKwCA4OBg7NixA0uWLMF7770HLy8v5OTkYNiwYa12XI9juCEiImpHYmJiEBMT0+i2rKwsjTZBEJ445uTJkzF58uSWlmYwXHNDREREosJwQ0RERKLCcENERESiwnBDREREosJwQ0RERKLCcENERESiwnBDREREosJwQ0RERKJi0nBz9OhRvPTSS3B2doZEIsEXX3yhtl0QBCQlJcHZ2Rm2trYYPXo0zp49a5piiYiIqE0wabi5e/cuBg4ciLVr1za6feXKlUhPT8fatWtx8uRJODo6IjQ0FHfu3GnlSomIiKitMOnrF8LDwxEeHt7oNkEQkJGRgYSEBEyaNAkA8Nlnn0EulyM7OxtvvfVWa5ZKREREbYTZrrkpKSmBQqFAWFiYqk0qlWLUqFEoKCho8nfV1dVQKpVqHyIiImo/zDbcKBQKAIBcLldrl8vlqm2NSUtLg729verj4uJi1DqJWsM/lINNXUKT7K4++aV6REStyWzDTQOJRKL2XRAEjbZHxcfH4/bt26pPWVmZsUskIiIiM2LSNTfNcXR0BPBwBsfJyUnVXlFRoTGb8yipVAqpVGr0+oiIiMg8me3MjYeHBxwdHZGbm6tqq6mpQV5eHoKDg01YGZk7aam1qUsgIiITMunMTVVVFf7zn/+ovpeUlKC4uBjdunWDq6srYmNjkZqaCm9vb3h7eyM1NRUdO3bEtGnTTFg1EbWE/aVqU5dARCJn0nDz/fff49lnn1V9j4uLAwBERUUhKysLixYtwr179xATE4PffvsNw4YNw8GDB2FnZ2eqkkkErvziAPdeN0xdBhERGYlJw83o0aMhCE3faSGRSJCUlISkpKTWK4qIiIjaNLNdc0NERESkD4YbIiIiEhWGGyIiIhIVhhsiahRvqSeitorhhqiFzO3VCLkKH1OXQERkUgw3REREJCoMN0RmzhgzQ1d+cTD4mERE5oLhhswGn1xLRESGwHBDREREosJwQ9QILsolImq7GG6IDMDc7phqD6x/+sXUJRCRmWK4ITJjDE1ERLpjuKE2x+5q0y9bJSIiYrghEhGuFSIiYrghMhhDX0JqC5ekOItGROaI4YaIWg2fZURErYHhhtolc39CrzFnbbQ5dr40k0i81q9fDw8PD9jY2CAgIADHjh1rtn9eXh4CAgJgY2MDT09PbNiwQW17VlYWJBKJxuf+/fvGPIxmMdwQNUGf9Stt4VISEbVfOTk5iI2NRUJCAoqKijBixAiEh4ejtLS00f4lJSV44YUXMGLECBQVFeHdd9/Fn/70J+zcuVOtn0wmQ3l5udrHxsamNQ6pUQw3JEqmnHloacDR9/dcTExET5Keno5Zs2Zh9uzZ6Nu3LzIyMuDi4oLMzMxG+2/YsAGurq7IyMhA3759MXv2bMycORMffPCBWj+JRAJHR0e1jykx3BAREbUDNTU1KCwsRFhYmFp7WFgYCgoKGv3N8ePHNfqPGzcO33//PR48eKBqq6qqgpubG3r16oUXX3wRRUVFhj8AHTDcEBmBvrMvvKxFRLpSKpVqn+rqxhfuV1ZWoq6uDnK5XK1dLpdDoVA0+huFQtFo/9raWlRWVgIAfHx8kJWVhb1792L79u2wsbFBSEgILl68aICj04+VyfZM1AbkKnwQ6viTXr/9h3IwJst+0Km/sZn7Qmpt8dULJAbWP/8XVhb6X0K3qK8BALi4uKi1JyYmIikpqcnfSSQSte+CIGi0Pan/o+3Dhw/H8OHDVdtDQkIwePBgfPTRR1izZs2TD8QIGG7IKKx/+gU1Pr1MXUazrvziAPdeN4y6j4bA0lzIMUSoMeR6G23XK/EZN0TmoaysDDKZTPVdKpU22q9Hjx6wtLTUmKWpqKjQmJ1p4Ojo2Gh/KysrdO/evdHfWFhYYMiQIZy5IWpgf6kat70a/4v5KLurAu64Nf1fGuaGl5v4jBsiY5HJZGrhpinW1tYICAhAbm4uXn75ZVV7bm4uJkyY0OhvgoKCsG/fPrW2gwcPIjAwEB06dGj0N4IgoLi4GP3799fhKAyLa26InoB3IRGRWMTFxeGTTz7Bp59+ivPnz2PhwoUoLS1FdHQ0ACA+Ph7Tp09X9Y+OjsbVq1cRFxeH8+fP49NPP8XmzZvx9ttvq/okJyfjwIEDuHz5MoqLizFr1iwUFxerxjQFztyQaElLrVHtWmPqMoxO2/AllvU2RKS/iIgI3Lx5EykpKSgvL4efnx/2798PNzc3AEB5ebnaM288PDywf/9+LFy4EOvWrYOzszPWrFmDV155RdXn1q1bmDNnDhQKBezt7eHv74+jR49i6NChrX58DSRCw8ogkVIqlbC3t8fYnrNbtHCLdKfvmhttLksB0OqylDbhRpt1N/ouKm4Nhg43xlpzY8jLUlxQTMZSW1+DQxWf4Pbt21pd6tGHoc5LrVFrW8XLUkRaMNdLU4auqy0sJmawIaInYbghagd4SYqI2hOGG2qztJk90GYmQtsTv7nN3phbPc3hnVJE1JoYbsho9L18YM4nQnMJFLrUYei1NkRE5o7hhqiNMXXA4sP7iMjcMdyQ6Bny0hRg+nChC3NYa8O7pIiotTHcUJtmqlkEUwUcY+2Xl6SISEwYbsgsmWLdja6zHK0ZcHIVPjrvzxizNrwkRURtAcMNtQvazkzoE3CMHXKMPb4xZ23MeXE4EYkXww0ZVWuskTCH2QRjBJCWBCcxztpwvQ0RaYvvliKzpe0bwrWl7bumrvzioNUrGR73aBBpyesaWhqUdAk2XGtDRGLEcEOiYHdV0OpdU8YOOA0eDyjNhR1DzvoYK9joM2vDu6SIyFQYbsjorH/6Re+XaJpSSwPOo1pj8bE53PZNRGQOuOaGzJou//Wv7eyCLjMWbSUw6FpnW5q1ISLSFcMNtUu6BhxzDjnmFmwMjZekiEhXDDfUKlpygjLG7A2g+2Jacws4+oSu1lhAzFkbIjI1hhsSHWMHHFOHHH1r0PVYzeFyFGdtiEgfDDfUalpr9gYwbsABTBNyWrLP1gg2hsZgQ0T64t1S1Gbo+twbbW8PB7S/Rfxxj4YNQ91Z1dT4+tAnuOkbbHg5iojMBcMNtSpzvi28IQjoE3KAxoOILoHH0DNBbTnYcNaGiFqC4YbaFH1mbwBoPYMD6D+L0xhTrM/Rd9Ewgw0RiQXX3FCra+nJS5+Tqa4nbmmpdZt7NUFLajaXYENEZAgMN2QSbSHgAG0j5LQ01JhTsOGsDREZAi9Lkcm0dP2NPi/W1OcyFaB+qcdQl6xaoqWBq6V3QzHYEJE5Y7ihNk3fN4frG3KAxoOFsQOPoWaPGGqIqD1guCGTMsTdUw0n3NYOOY96UvhoLvy0xmUvQzy3hsGGiNoKhhsyuYYTnCFCjj4BBzBcyGmKqdbtmGuoARhsiMh4GG7IbJh6FgdQDwPGCjrGZMgnCzPUEFFbxXBDZsVQD/lracgBNIOCOYYdY7wmwZi3dzPYEFFrYLghs2Ooy1SA+om6JUEHaD5IGDv4GPtdT8Z+Xg1DDRG1JoYbMluGDDmAYYPO48zhRZO6aI2H7zHQEJGpMNyQ2TN0yAE0T+6GDjvmpjWfJMxQQ0SmxnBDbcajJ01Dv3yzqZN/Wws9pngdAsMMEZkbhhtqk4wZdB6lTVhorQBkLu9xYpghInPHcENtXmMnW2MGnseZS+gwBgYZImqLGG5IlJo6Kbdm6GkrGGCISGzaxFvB169fDw8PD9jY2CAgIADHjh0zdUnURln/9ItWn7ZO2+MUw7ESkW50Pafm5eUhICAANjY28PT0xIYNGzT67Ny5E76+vpBKpfD19cXu3buNVb5WzH7mJicnB7GxsVi/fj1CQkKwceNGhIeH49y5c3B1dTV1eSRSPOkTkRjpek4tKSnBCy+8gDfffBPbtm3Dv//9b8TExMDBwQGvvPIKAOD48eOIiIjA0qVL8fLLL2P37t2YMmUK8vPzMWzYsNY+RACARBAEs35Ax7BhwzB48GBkZmaq2vr27YuJEyciLS3tib9XKpWwt7fH2J6zYWVhmvf7EBFR21BbX4NDFZ/g9u3bkMlkRtmHoc5L+tSq6zn1nXfewd69e3H+/HlVW3R0NE6dOoXjx48DACIiIqBUKvHll1+q+jz//PPo2rUrtm/fru/htYhZz9zU1NSgsLAQixcvVmsPCwtDQUFBo7+prq5GdfUjD2u7fRvAw/8TEBERNafhXNEa/91fK9QA9S38PR6GpUdJpVJIpZp3cepzTj1+/DjCwsLU2saNG4fNmzfjwYMH6NChA44fP46FCxdq9MnIyND1kAzGrMNNZWUl6urqIJfL1drlcjkUCkWjv0lLS0NycrJG+5HKrUapkYiIxOfmzZuwt7c3ytjW1tZwdHTEEUXLz0udO3eGi4uLWltiYiKSkpI0+upzTlUoFI32r62tRWVlJZycnJrs09SYrcGsw00DiUT9vT2CIGi0NYiPj0dcXJzq+61bt+Dm5obS0lKj/R/VkJRKJVxcXFBWVma0KVFDYr3GxXqNr63VzHqN6/bt23B1dUW3bt2Mtg8bGxuUlJSgpqblVxQaOx82NmvzKF3OqU31f7xd1zGNzazDTY8ePWBpaamR/ioqKjRSYoOmpuPs7e3bxF+sBjKZjPUaEes1rrZWL9D2ama9xmVhYdybiW1sbGBjY2PUfTxOn3Oqo6Njo/2trKzQvXv3Zvs0NWZrMOtbwa2trREQEIDc3Fy19tzcXAQHB5uoKiIiorZHn3NqUFCQRv+DBw8iMDAQHTp0aLaPKc/TZj1zAwBxcXGIjIxEYGAggoKCsGnTJpSWliI6OtrUpREREbUpTzqnxsfH49q1a9i69eF6oOjoaKxduxZxcXF48803cfz4cWzevFntLqgFCxZg5MiRWLFiBSZMmIA9e/bg0KFDyM/PN8kxAgCENmDdunWCm5ubYG1tLQwePFjIy8vT+rf3798XEhMThfv37xuxQsNhvcbFeo2rrdUrCG2vZtZrXG2tXn00d06NiooSRo0apdb/yJEjgr+/v2BtbS24u7sLmZmZGmN+/vnnQp8+fYQOHToIPj4+ws6dO419GM0y++fcEBEREenCrNfcEBEREemK4YaIiIhEheGGiIiIRIXhhoiIiERF1OFG19e6t6ajR4/ipZdegrOzMyQSCb744gu17YIgICkpCc7OzrC1tcXo0aNx9uxZk9SalpaGIUOGwM7ODj179sTEiRNx4cIFs603MzMTAwYMUD00LCgoSO2FbuZUa2PS0tIgkUgQGxurajO3mpOSkiCRSNQ+jo6OZlsvAFy7dg1vvPEGunfvjo4dO2LQoEEoLCxUbTenmt3d3TX++UokEsydO9fsagWA2tpaLFmyBB4eHrC1tYWnpydSUlJQX///X5xkbjXfuXMHsbGxcHNzg62tLYKDg3Hy5EmzrZd0ZKK7tIxux44dQocOHYSPP/5YOHfunLBgwQKhU6dOwtWrV01dmiAIgrB//34hISFB2LlzpwBA2L17t9r25cuXC3Z2dsLOnTuF06dPCxEREYKTk5OgVCpbvdZx48YJW7ZsEc6cOSMUFxcL48ePF1xdXYWqqiqzrHfv3r3Cv/71L+HChQvChQsXhHfffVfo0KGDcObMGbOr9XHfffed4O7uLgwYMEBYsGCBqt3cak5MTBT69esnlJeXqz4VFRVmW++vv/4quLm5CTNmzBBOnDghlJSUCIcOHRL+85//mGXNFRUVav9sc3NzBQDC4cOHza5WQRCEZcuWCd27dxf++c9/CiUlJcLnn38udO7cWcjIyFD1Mbeap0yZIvj6+gp5eXnCxYsXhcTEREEmkwm//PKLWdZLuhFtuBk6dKgQHR2t1ubj4yMsXrzYRBU17fFwU19fLzg6OgrLly9Xtd2/f1+wt7cXNmzYYIIK1VVUVAgAVM9GMPd6BUEQunbtKnzyySdmXeudO3cEb29vITc3Vxg1apQq3JhjzYmJicLAgQMb3WaO9b7zzjvCM8880+R2c6z5UQsWLBC8vLyE+vp6s6x1/PjxwsyZM9XaJk2aJLzxxhuCIJjfP9/ff/9dsLS0FP75z3+qtQ8cOFBISEgwu3pJd6K8LNXwWvfHX9Pe3GvdzUlJSQkUCoVa/VKpFKNGjTKL+m/fvg0AqhfLmXO9dXV12LFjB+7evYugoCCzrnXu3LkYP348xo4dq9ZurjVfvHgRzs7O8PDwwGuvvYbLly8DMM969+7di8DAQLz66qvo2bMn/P398fHHH6u2m2PNDWpqarBt2zbMnDkTEonELGt95pln8PXXX+Pnn38GAJw6dQr5+fl44YUXAJjfP9/a2lrU1dVpvNvJ1tYW+fn5Zlcv6U6U4Uaf17qbk4YazbF+QRAQFxeHZ555Bn5+fgDMs97Tp0+jc+fOkEqliI6Oxu7du+Hr62uWtQLAjh078MMPPyAtLU1jmznWPGzYMGzduhUHDhzAxx9/DIVCgeDgYNy8edMs6718+TIyMzPh7e2NAwcOIDo6Gn/6059Uj5g3x5obfPHFF7h16xZmzJgBwDxrfeeddzB16lT4+PigQ4cO8Pf3R2xsLKZOnQrA/Gq2s7NDUFAQli5div/+97+oq6vDtm3bcOLECZSXl5tdvaQ7s3+3VEuY2yvYdWWO9c+bNw8//vhjo+8MMad6+/Tpg+LiYty6dQs7d+5EVFQU8vLyVNvNqdaysjIsWLAABw8ebPYtweZUc3h4uOrP/fv3R1BQELy8vPDZZ59h+PDhAMyr3vr6egQGBiI1NRUA4O/vj7NnzyIzMxPTp09X9TOnmhts3rwZ4eHhcHZ2Vms3p1pzcnKwbds2ZGdno1+/figuLkZsbCycnZ0RFRWl6mdONf/1r3/FzJkz8dRTT8HS0hKDBw/GtGnT8MMPP6j6mFO9pBtRztzo81p3c9Jw14m51T9//nzs3bsXhw8fRq9evVTt5livtbU1evfujcDAQKSlpWHgwIFYvXq1WdZaWFiIiooKBAQEwMrKClZWVsjLy8OaNWtgZWWlqsucan5cp06d0L9/f1y8eNEs/xk7OTnB19dXra1v374oLS0FYJ7/HwaAq1ev4tChQ5g9e7aqzRxr/T//5/9g8eLFeO2119C/f39ERkZi4cKFqplIc6zZy8sLeXl5qKqqQllZGb777js8ePAAHh4eZlkv6UaU4Uaf17qbk4a/XI/WX1NTg7y8PJPULwgC5s2bh127duGbb76Bh4eH2nZzq7cxgiCgurraLGsdM2YMTp8+jeLiYtUnMDAQr7/+OoqLi+Hp6Wl2NT+uuroa58+fh5OTk1n+Mw4JCdF4fMHPP/8MNzc3AOb7/+EtW7agZ8+eGD9+vKrNHGv9/fffYWGhfjqxtLRU3QpujjU36NSpE5ycnPDbb7/hwIEDmDBhglnXS1oyyTLmVtBwK/jmzZuFc+fOCbGxsUKnTp2EK1eumLo0QRAe3hlTVFQkFBUVCQCE9PR0oaioSHWr+vLlywV7e3th165dwunTp4WpU6ea7DbE//mf/xHs7e2FI0eOqN2e+vvvv6v6mFO98fHxwtGjR4WSkhLhxx9/FN59913BwsJCOHjwoNnV2pRH75YSBPOr+c9//rNw5MgR4fLly8K3334rvPjii4KdnZ3q75e51fvdd98JVlZWwl/+8hfh4sWLwt/+9jehY8eOwrZt21R9zK3muro6wdXVVXjnnXc0tplbrVFRUcJTTz2luhV8165dQo8ePYRFixaZbc1fffWV8OWXXwqXL18WDh48KAwcOFAYOnSoUFNTY5b1km5EG24EofnXupva4cOHBQAan6ioKEEQHt46mZiYKDg6OgpSqVQYOXKkcPr0aZPU2lidAIQtW7ao+phTvTNnzlT97+7g4CCMGTNGFWzMrdamPB5uzK3mhmd+dOjQQXB2dhYmTZoknD171mzrFQRB2Ldvn+Dn5ydIpVLBx8dH2LRpk9p2c6v5wIEDAgDhwoULGtvMrValUiksWLBAcHV1FWxsbARPT08hISFBqK6uNtuac3JyBE9PT8Ha2lpwdHQU5s6dK9y6dcts6yXdSARBEEwyZURERERkBKJcc0NERETtF8MNERERiQrDDREREYkKww0RERGJCsMNERERiQrDDREREYkKww0RERGJCsMNERERiQrDDZGBjR49GrGxsWYzTmNmzJiBiRMntmgMd3d3SCQSSCQS3Lp1yyB1tcbYRCR+DDdEJnbkyJFGT+K7du3C0qVLVd/d3d2RkZHRusU9QUpKCsrLy2Fvb69q+/jjj+Hm5oZBgwbh+PHjqvaG4/Tz80NdXZ3aOF26dEFWVpbq+8mTJ7Fz506j109E4sRwQ2SmunXrBjs7O1OX0Sw7Ozs4OjpCIpEAAEpLS7Fy5Urs2LEDS5YswaxZszR+c+nSJWzdurXZcR0cHNCtWzej1ExE4sdwQ2Rk27ZtQ2BgoCoITJs2DRUVFQCAK1eu4NlnnwUAdO3aFRKJBDNmzACgfllq9OjRuHr1KhYuXKi6XAMASUlJGDRokNr+MjIy4O7urvpeV1eHuLg4dOnSBd27d8eiRYvw+CvlBEHAypUr4enpCVtbWwwcOBD/+Mc/dD5WpVKJLl26YMCAAQgICMC9e/c0+syfPx+JiYm4f/++zuMTEWmD4YbIyGpqarB06VKcOnUKX3zxBUpKSlQBxsXFRXX55cKFCygvL8fq1as1xti1axd69eqlugxUXl6u9f4//PBDfPrpp9i8eTPy8/Px66+/Yvfu3Wp9lixZgi1btiAzMxNnz57FwoUL8cYbbyAvL0+nY/Xz88PAgQNhb2+Pfv36YdmyZRp9YmNjUVtbi7Vr1+o0NhGRtqxMXQCR2M2cOVP1Z09PT6xZswZDhw5FVVUVOnfurLr80rNnT3Tp0qXRMbp16wZLS0vV7I8uMjIyEB8fj1deeQUAsGHDBhw4cEC1/e7du0hPT8c333yDoKAgVZ35+fnYuHEjRo0apdP+PvnkE6xYsQIdO3aEra2txvaOHTsiMTER7777Lt5880219TpERIbAmRsiIysqKsKECRPg5uYGOzs7jB49GsDD9SnGdvv2bZSXl6tCCwBYWVkhMDBQ9f3cuXO4f/8+QkND0blzZ9Vn69atuHTpkl777d69e6PBpsGsWbPQo0cPrFixQq/xiYiaw5kbIiO6e/cuwsLCEBYWhm3btsHBwQGlpaUYN24campqWjy+hYWFxvqZBw8e6DRGfX09AOBf//oXnnrqKbVtUqm0ZQU2wcrKCsuWLcOMGTMwb948o+yDiNovztwQGdFPP/2EyspKLF++HCNGjICPj49qMXEDa2trANC4Pfpx1tbWGn0cHBygUCjUAk5xcbHqz/b29nBycsK3336raqutrUVhYaHqu6+vL6RSKUpLS9G7d2+1j4uLi87HrK1XX30V/fr1Q3JystH2QUTtE2duiIzI1dUV1tbW+OijjxAdHY0zZ86oPbsGANzc3CCRSPDPf/4TL7zwAmxtbdG5c2eNsdzd3XH06FG89tprkEql6NGjB0aPHo0bN25g5cqVmDx5Mr766it8+eWXkMlkqt8tWLAAy5cvh7e3N/r27Yv09HS1Z+rY2dnh7bffxsKFC1FfX49nnnkGSqUSBQUF6Ny5M6Kiooz2z2f58uUYN26c0cYnovaJMzdERuTg4ICsrCx8/vnn8PX1xfLly/HBBx+o9XnqqaeQnJyMxYsXQy6XN3mZJiUlBVeuXIGXlxccHBwAAH379sX69euxbt06DBw4EN999x3efvtttd/9+c9/xvTp0zFjxgwEBQXBzs4OL7/8slqfpUuX4v3330daWhr69u2LcePGYd++ffDw8DDgPw1Nzz33HJ577jnU1tYadT9E1L5IhMcv2BMRacHd3R2xsbFGe0XEkSNH8Oyzz+K3335r8i4yIqLGMNwQkV7c3d1RXl6ODh064Nq1awa9pbtfv364fPky7t+/z3BDRDpjuCEivVy9elV1Z5anpycsLAx3lduYYxOR+DHcEBERkajwP4eIiIhIVBhuiIiISFQYboiIiEhUGG6IiIhIVBhuiIiISFQYboiIiEhUGG6IiIhIVBhuiIiISFT+L/k4DMtKpOM/AAAAAElFTkSuQmCC",
+ "text/plain": [
+ "<Figure size 640x480 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_zonfield(uanom,'uanom [m/s]')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "id": "59822160-6c06-46e2-8c39-8503508240ac",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_zonfield(u4K,'u4K via anomaly [m/s]')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 104,
+ "id": "e2e95309-4403-4dac-a849-ccaabf29e3e1",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/tmp/ipykernel_903761/509274964.py:2: RuntimeWarning: invalid value encountered in divide\n",
+ " rel_diff_u4K = np.where(u1>0.1,(u4K-u1)/u1*100,np.nan)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# mask values with very low u1\n",
+ "rel_diff_u4K = np.where(u1>0.1,(u4K-u1)/u1*100,np.nan)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 105,
+ "id": "6a8bf45e-d5dc-42e3-9f85-a9ba66e0e9c3",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_zonfield(rel_diff_u4K,'rel. difference between u4K and u1 [%]',vmin=-10,vmax=10,levels=21,cmap='RdBu_r')\n",
+ "plot_zonfield_contour(u4K,vmin=0,vmax=50,levels=11)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4a9bdf7e-ebc0-42c0-87f8-34b2e602cd92",
+ "metadata": {},
+ "source": [
+ "## Investigate difference due to nonlinearity of calculation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "id": "e3131e96-7a1f-4e1a-8430-8690073a2601",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_zonfield(u4K_direct,'u4K direct [m/s]')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "id": "05e3ad66-3434-478e-b04b-09b8d8111fbb",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_zonfield(u4K-u4K_direct,'u4K (anomaly - direct) [m/s]')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7d79add1-ad7f-4e5b-85ae-3bf397dac681",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (based on the module python3/2023.01)",
+ "language": "python",
+ "name": "python3_2023_01"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Scripts_for_preprocessing/generate_initialdata/check_moisture_impact_lc1_initial_condition_fixedCoriolisParameter_CTL.ipynb b/Scripts_for_preprocessing/generate_initialdata/check_moisture_impact_lc1_initial_condition_fixedCoriolisParameter_CTL.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..fce4a1e19fe16937761ee3cb4915a59e02205c2f
--- /dev/null
+++ b/Scripts_for_preprocessing/generate_initialdata/check_moisture_impact_lc1_initial_condition_fixedCoriolisParameter_CTL.ipynb
@@ -0,0 +1,1094 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "49436791-5f4b-44e6-83ad-209241d426c7",
+ "metadata": {},
+ "source": [
+ "# Derive temperature field from wind field based on the thermal wind balance\n",
+ "\n",
+ "original python script developed by Nicole Albern, KIT, 2022 was set up for a dry atmosphere\n",
+ "\n",
+ "modifications by Christoph Braun, KIT, July 2023 to check the impact of using moist instead of dry atmosphere on the derived temperature field"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "52b34f26-53c9-4825-9c59-2241e8073838",
+ "metadata": {},
+ "source": [
+ "## First some steps that can be done w/o distinction between dry and moist atmosphere"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ca547b50-1782-4a29-a1dd-9ad72776ccff",
+ "metadata": {},
+ "source": [
+ "### import required libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "508c09ac-2fc3-4d40-b932-cc5fb2bc969c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "from netCDF4 import Dataset\n",
+ "\n",
+ "import scipy.integrate\n",
+ "\n",
+ "from numba import jit\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "import netCDF4 as nc"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f53ecf1a-b9b9-4b58-a4da-e027a26bf75a",
+ "metadata": {},
+ "source": [
+ "### 1) load information of vertical levels"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "e4d91893-e41b-49b3-9181-3203ff2d3134",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def load_levelinfo():\n",
+ " \n",
+ " fpath = '/work/bb1152/Module_A/A6_CyclEx/input_data/'\n",
+ " file = Dataset(fpath+'ifs2icon_verticalgridinfo_137levels.nc', 'r')\n",
+ " hyam = np.squeeze(np.array(file.variables['hyam']))\n",
+ " hybm = np.squeeze(np.array(file.variables['hybm'])) \n",
+ " hyai = np.squeeze(np.array(file.variables['hyai']))\n",
+ " hybi = np.squeeze(np.array(file.variables['hybi'])) \n",
+ " lev = np.squeeze(np.array(file.variables['lev' ]))\n",
+ " lev_2= np.squeeze(np.array(file.variables['lev_2'])) \n",
+ " return hyam, hybm, hyai, hybi, lev, lev_2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "1cff9154-9357-472e-ada0-4478058c68cf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "hyam, hybm, hyai, hybi, lev, lev_2 = load_levelinfo()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0c806e36-de39-4f68-8334-0827bd0380e4",
+ "metadata": {},
+ "source": [
+ "### 2) define constants\n",
+ "\n",
+ "physical constants are taken from\n",
+ "\n",
+ "- Polvani and Esler, 2007\n",
+ "- Booth et al., 2013 Climate Dynamics\n",
+ "- or set to the values used in icon-nwp-2.0.15/src/shared/mo_physical_constants.f90"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "3ce255cb-d263-48e3-99e3-a3506d3a4cd4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "u0 = 45.0 # in m/s (as in Polvani and Esler, 2007)\n",
+ "zT = 13.0e3 # in m (as in Polvani and Esler, 2007)\n",
+ "H = 7.5e3 # in m (as in Polvani and Esler, 2007)\n",
+ "R = 287.04 # dry gas constant in J/(kg K) (parameter rd in ICON)\n",
+ "a = 6.371229e6 # average Earth radius in m (parameter earth_radius in ICON)\n",
+ "Omega = 7.29212e-5 # angular velocity in 1/s (parameter earth_angular_velocity in ICON) \n",
+ "T0 = 300 # in K (as in Polvani and Esler, 2007)\n",
+ "Gamma0 = -6.5e-3 # in K/m (as in Polvani and Esler, 2007)\n",
+ "alpha = 10 # unitless (as in Polvani and Esler, 2007)\n",
+ "kappa = 2.0/7.0 # unitless (as in Polvani and Esler, 2007)\n",
+ "g = 9.80665 # av. gravitational acceleration in m/s2 (parameter grav in ICON)\n",
+ "p0 = 1.0e5 # globally-uniform surface pressure in Pa (as in Polvani and Esler, 2007)\n",
+ "# for relative humidity following Booth et al., 2013 Climate Dynamics\n",
+ "zTrh = 12.0e3 \n",
+ "rh0 = 0.80 # relative humidity scaling factor from 0..1"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d259d060-dddb-4cc0-bb7a-f36a09967790",
+ "metadata": {},
+ "source": [
+ "### 3) define vertical grid\n",
+ "\n",
+ "for computation of initial state we convert the ifs2icon hybrid levels to height levels assuming a globally-uniform surface pressure (defined above) and defining height according to Polvani and Elsner as z = H ln (p0/p)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "e544d293-6177-44ee-b238-7515c6a45da4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "p = hyam + hybm*p0\n",
+ "z = H*np.log(p0/p) # np.log is natural logarithm\n",
+ "nz = z.size"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4a54dd36-18a2-4cba-9414-0b534bac5b1a",
+ "metadata": {},
+ "source": [
+ "### 4) define latitude-longitude grid"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "c44793a9-87e9-4c14-b556-49d7394dd573",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "lat = np.linspace(-90, 90, 360)\n",
+ "lon = np.linspace(0.0,360,10)\n",
+ "nlat = lat.size\n",
+ "nlon = lon.size\n",
+ "\n",
+ "# latitude in radians\n",
+ "latrad = lat * np.pi/180.0"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8ffa207b-590d-449a-a3bf-74b45be619f2",
+ "metadata": {},
+ "source": [
+ "### 5) define wind field for lifecycle 1 (as in Polvani and Esler, 2007; eqns 6 and 7)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "911aab42-41b4-4e9e-9939-5574883ec008",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "u1 = np.zeros((nz,nlat))+np.nan\n",
+ "F1 = np.power(np.sin(np.pi*np.power(np.sin(latrad),2)),3); F1[lat<0] = 0.0\n",
+ "for i in range(0, nz):\n",
+ " for j in range(0, nlat):\n",
+ " u1[i,j] = u0*F1[j]*(z[i]/zT)*np.exp(-0.5*(np.power(z[i]/zT,2)-1))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8b3144c4-1a12-4076-aae8-434a168d7e79",
+ "metadata": {},
+ "source": [
+ "## Here we come to the step, where temperature and virtual temperature should be distinguished\n",
+ "\n",
+ "we first follow the original script by Nicole Albern here"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "417d0b27-7ce1-47f3-885a-a2c38030e919",
+ "metadata": {},
+ "source": [
+ "### 6) define latitude independent reference temperature profile (as in Polvani and Esler, 2007; eqn A5)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "80c97d63-aae0-414b-b1b4-034b17e85f60",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "Tr = np.zeros((nz, nlat)) + np.nan\n",
+ "for i in range(0, nz):\n",
+ " Tr[i, :] = T0 + Gamma0/np.power((np.power(zT,-alpha)+np.power(z[i],-alpha)),1/alpha)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "23b27ca3-1ade-44d8-8a05-7b16b055f0a1",
+ "metadata": {},
+ "source": [
+ "### 7) compute temperature profile in zonal wind balance with wind field (as in Polvani and Esler, 2007; eqn A4)\n",
+ "#### 7A) define integrand from eqn A4"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "b19e6398-eb58-4c77-a91c-16c375c7b583",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "@jit\n",
+ "def Tintegrand(latrad, z, zT, U0, a, Omega):\n",
+ " f = 2*Omega*np.sin(np.deg2rad(45.0))\n",
+ " F = np.power(np.sin(np.pi*np.power(np.sin(latrad),2)),3)\n",
+ " if latrad<0: F=0.0\n",
+ " u1 = U0*F*(z/zT)*np.exp(-0.5*(np.power(z/zT,2)-1))\n",
+ " du1dz = u1*(1/z-z/np.power(zT,2))\n",
+ " return (a*f+2*u1*np.tan(latrad))*du1dz"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "aeecefd7-f683-4ce8-9af3-54bddc7546d3",
+ "metadata": {},
+ "source": [
+ "#### 7B) integrate"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "98a73270-b8c3-421b-8bb8-f21b7282d819",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "tmp = np.zeros((nz, nlat)) + np.nan\n",
+ "for i in range(0, nz):\n",
+ " for j in range(0, nlat):\n",
+ " tmp[i, j] =scipy.integrate.quad(Tintegrand, 0, latrad[j], args=(z[i], zT, u0, a, Omega))[0]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0d12be38-f924-4ad7-a5cf-ab90dd5ea075",
+ "metadata": {},
+ "source": [
+ "#### 7C) add integrand into eqn A4"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "0cefbacb-a8cf-4f18-9034-bd8109f9d6fe",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "T=Tr-H/R*tmp"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f58b5c0e-7438-4385-98f8-ea93e3b9f0f0",
+ "metadata": {},
+ "source": [
+ "### 8) calculate potential temperature"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "fa96da60-fd71-462d-a2db-6948861f0017",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "theta=T*np.expand_dims(np.exp(kappa*z/H),axis=1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "62aefc76-10b1-434a-85ff-c602d9339048",
+ "metadata": {},
+ "source": [
+ "### 9) define relative humidity profile\n",
+ "\n",
+ "follows Booth et al. 2013, Climate Dynamics "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "1464049d-3514-4af0-9a1d-2aff6b69cecb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "rh = np.zeros((nz, nlat)) + np.nan\n",
+ "for i in range(0, nz):\n",
+ " if z[i]>14e3:\n",
+ " rh[i, :] = 0.0\n",
+ " else:\n",
+ " rh[i, :] = rh0*np.power(1-0.85*z[i]/zTrh, 1.25)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "91c1f45e-d3c2-4d59-9744-805b9f6108c5",
+ "metadata": {},
+ "source": [
+ "### 10) calculate specific humidty\n",
+ "\n",
+ "follows calculation in icon \n",
+ "\n",
+ "/icon-nwp-2.0.15/src/atm_phy_schemes/mo_satad.f90 -> sat_pres_water,spec_humi"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "e99206d1-0d98-4541-9442-0f693762aae0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "b1 = 610.78 # --> c1es in mo_convect_tables.f90 \n",
+ "b2w= 17.269 # --> c3les \n",
+ "b3 = 273.15 # --> tmelt; melting temperature in K\n",
+ "b4w= 35.86 # --> c4les\n",
+ "sat_pres_water = b1*np.exp(b2w*(T-b3)/(T-b4w))\n",
+ "Rdv = 287.04/461.51 # Rd/Rv; replace Rd by R as values are identical?\n",
+ "o_m_Rdv = 1-Rdv # 1-Rd/Rv\n",
+ "\n",
+ "qv = np.zeros((nz, nlat))\n",
+ "for i in range(0, nz):\n",
+ " for j in range(0, nlat):\n",
+ " qv[i, j] = rh[i,j]*Rdv*sat_pres_water[i,j]/(p[i]-o_m_Rdv*sat_pres_water[i,j]) # Do I understand this equation?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "79330c44-3f17-43e2-a5d8-8b6eb79b36b4",
+ "metadata": {},
+ "source": [
+ "### Plotting results"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "8e2fdca7-599d-4638-8ca4-19f21c7ccdc0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plot_zonfield(var,clabel,vmin=None,vmax=None,levels=None):\n",
+ " \n",
+ " fig = plt.figure()\n",
+ " \n",
+ " if (vmin or vmax or levels):\n",
+ " levels = np.linspace(vmin,vmax,levels)\n",
+ " cbar = plt.contourf(lat,z/1e3,var,levels=levels)\n",
+ " else:\n",
+ " cbar = plt.contourf(lat,z/1e3,var)\n",
+ " \n",
+ " plt.xlim(0,90)\n",
+ " plt.ylim(0,50)\n",
+ " plt.xlabel('latitude [°N]')\n",
+ " plt.ylabel('height [km]')\n",
+ " \n",
+ " fig.colorbar(cbar,label=clabel)\n",
+ " \n",
+ " return"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "c875d222-bf93-4821-9ebf-889a4be4ff74",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_zonfield(u1,'zonal wind [m/s]')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "3d10998d-d220-412e-a5b5-59688f218955",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_zonfield(Tr,'ref. temp. profile [K]')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "ac58fbfa-1d16-4ec2-bb76-7e7fc595b10b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_zonfield(T,'T [K]')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "17c198d9-d79a-445f-b312-7a759b91092b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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ejmnTpuHOnTvo0aMHDh8+DFtbW0OXRtQiSS18qYMBjYgkFXZOnDihsi2TyRAXF4e4uDiD1ENEzV9zDGhkODX3lIYugfTA4OvsEBEREekTww4REREZNYYdIiIiMmoMO0RERGTUGHaIiIjIqDHsEBERkVGT1K3nelVqCtSYGroKag5seOspEZEx4cgO0d+Vmkr3RUSkpZMnT2Lo0KHw8PCATCbD/v37Vfbv3bsXAwcOhJOTE2QyGTIyMur0kZ+fjwkTJsDNzQ02Njbo1q0bPv/8c5VjfvnlFwwfPhxOTk6ws7NDr169cPz4cT1+skdj2CFqThiqiEhLpaWlCA4ORkJCQoP7e/XqhVWrVjXYx4QJE5CZmYkDBw7g4sWLGDFiBEaPHo309HTxmCFDhqC6uhrHjh1DWloaunTpgqeffhr5+fk6/0zqajmXsYhIfU0deHjpkEjvoqKiEBUV1eD+CRMmAACuX7/e4DFnzpzBhg0b8I9//AMAsGTJErz77rs4f/48unbtips3b+LXX3/FJ598gs6dOwMAVq1ahQ8//BCXL1+Gm5ub7j6QBhh2iMjwOJqkGwyNpGe9e/fG7t27MWTIENjb2+O///0vKioqEBERAQBwdHREx44dsX37dnTr1g1yuRwfffQRXF1d0b17d4PVzbBDRGQsGBobr7z5/Q6Li4tVtuVyOeRyuV7ea/fu3Rg9ejQcHR1hZmYGa2tr7Nu3D35+fgDuP9MyKSkJw4cPh62tLUxMTODq6opDhw7B3t5eLzWpg2GHiIjIAAoqbGFuZqH1+VUVlQAAT09PlfbY2Fi9PUB7yZIluHPnDo4cOQInJyfs378fI0eOxKlTp9CpUycIgoBp06bBxcUFp06dgpWVFT7++GM8/fTTSE1Nhbu7u17qehSGHSIiomYsNzcXdnZ24ra+RnWysrKQkJCAS5cuITAwEAAQHByMU6dO4YMPPsDGjRtx7NgxfPXVV7hz545Y04cffoikpCQkJiZi4cKFeqntURh2iIiImjE7OzuVsKMvZWVlAAATE9UbuU1NTVFTU/PQY0xMTMRjDIFhh4iIqAUoKSnBr7/+Km5nZ2cjIyMDDg4OaNu2LW7fvo2cnBz88ccfAIDMzEwAgJubG9zc3PD444+jXbt2mDJlCt555x04Ojpi//79SEpKwldffQUACA0NRevWrTFx4kS8+eabsLKywubNm5GdnY0hQ4Y0/Yf+/xh2iIiIWoAffvgBffr0Ebfnzp0LAJg4cSK2bduGAwcO4MUXXxT3jxkzBsD/5gCZm5vj66+/xsKFCzF06FCUlJSgXbt2SExMxODBgwEATk5OOHToEBYvXoy+ffuiqqoKgYGB+OKLLxAcHNxgbRcuXND48wQEBMDMTL0YIxMEQdD4HZqR4uJiKBQKeL+1AiaWloYuhxpJaW24YVAiMn415feQO+8NFBUV6e3SUO330tDDk2Fu04gJyqWV+DJyi15rbSomJiaQyWRQN5KYmJjgl19+ga+vr1rHc2SHmhXTsua96DfDGhFR/c6dOwdnZ+dHHicIAoKCgjTqm2GHqAnpO6wxTBFRcxQeHo527dqpvRbPU089BSsrK7X7Z9ghMiK6DlMMT0TUFDR9UOjXX3+t0fHN+5oAEemVaZmJzl9ERPU5ceLEI4+ZNm2aVn3zLw8RNSl9BCgGLaLmb/jw4Th//nyD+6dPn45PP/1Uq755GYuIWgwGHnoU2T3+O2IoL730EqKionDq1Cm0b99eZd+MGTOwfft2jS9f1WLYISIiIoP717/+hTt37mDAgAFISUlBmzZtAACzZs3Ctm3bcPDgQTz55JNa9c0IS0RERJLw8ccfo3v37ujfvz9u3ryJOXPmYMuWLfjyyy8RHh6udb8c2SEiIiJJMDExwa5duzBkyBAEBASgtLQUBw4cUFn5WRsMO0RERAZwu9wKZibaP6G8utxUh9UY3vr168WfIyIicOrUKQwcOBCXL1/G5cuXxX2zZs3SuG+GHSIiIjK4d999V2Xb3d0dFy5cUHlulkwmY9h5GNNyGUwFmaHLIB2rtjLqR7sREbUY2dnZeuu7xYQdMk5m5dINsAxiRETSwLuxiPTErFym0xcRkbFav3497t27p/bxGzduxN27d9U+nmGHqJlgYCIiYzVnzhyNwsv8+fPx119/qX08L2MRtSCNCTy8LEdE+iIIAvr16wczM/ViSXl5uUb9M+wQkVoaOzLEsEREDYmNjdXo+OHDh8PBwUHt4xl2iKhJ6OMyGgMUkXHQNOxoimGHiJqt5jQPicGMyHAYdoiImkBzCmYtmfIe/zkZI96NRUREREaNYYeIiIiMGsMOERERSU5lZSUyMzNRXV3d6L4YdoiIiEgyysrKMHnyZFhbWyMwMBA5OTkA7j/tfNWqVVr12WImKJuVAaZKQ1dBhlZtY+gKiIjuK7xnBVMTudbnK+8Z53jFokWL8OOPP+LEiRMYNGiQ2N6/f3/ExsZi4cKFGvfZYsIOEQCYlTbdezFYERFpbv/+/di9ezd69uwJmex/d8cFBAQgKytLqz4Zdoj0pLHBimGJiFqiv/76Cy4uLnXaS0tLVcKPJoxzDIzICJiVavciImrOnnjiCRw8eFDcrg04mzdvRmhoqFZ9cmSHyMhoGng4gkREUhIfH49BgwbhypUrqK6uxnvvvYfLly/jzJkzSE5O1qpPjuwQtXAcOSIiKQkLC0NKSgrKysrg5+eHw4cPw9XVFWfOnEH37t216pMjO0SkEY4cEZG+VFVV4ZVXXsEbb7yBxMREnfXLkR0i0itt5x5xFImo5TE3N8e+fft03i/DDhFJVmOCEsMTUfP07LPPYv/+/Trtk5exiKjFYOChR5FVGLoCateuHd566y2kpKSge/fusLFRvRY+a9Ysjftk2CEiIiLJ+Pjjj2Fvb4+0tDSkpaWp7JPJZAw7RERE1LxlZ2frvE/O2SEiImoBTp48iaFDh8LDwwMymazOvBhBEBAXFwcPDw9YWVkhIiICly9fVjlmypQp8PPzg5WVFZydnTF8+HD8/PPPKsesWLECYWFhsLa2hr29vZ4/lXpazMiOWbn0HgRabW3oCoiIqKUoLS1FcHAwXnzxRTz33HN19q9ZswZr167Ftm3b0L59eyxfvhwDBgxAZmYmbG1tAQDdu3fH+PHj0bZtW9y+fRtxcXGIjIxEdnY2TE1NAQCVlZUYOXIkQkNDsWXLFo3rnDRp0kP3f/LJJxr32WLCjhSZlRm6AqoPQygRGaOoqChERUXVu08QBKxbtw6LFy/GiBEjAACJiYlwdXXFzp07MWXKFADAK6+8Ip7j7e2N5cuXIzg4GNevX4efnx8AYOnSpQCAbdu2aVXnnTt3VLarqqpw6dIlFBYWom/fvlr1ybBD9De6CqEMTUTUFIqLi1W25XI55HK5Rn1kZ2cjPz8fkZGRKv2Eh4cjJSVFDDsPKi0txdatW+Hj4wNPT0/tiq9Hfevs1NTUYNq0afD19dWqT87ZIdITszLtXkTUMpSVWaC0TK71q6zMAgDg6ekJhUIhvuLj4zWuJT8/HwDg6uqq0u7q6iruq/Xhhx+iVatWaNWqFQ4dOoSkpCRYWFho+VtQj4mJCebMmYN3331Xq/M5skMkMZoEHo4eEVFubi7s7OzEbU1HdR5U+4TxWoIg1GkbP348BgwYgLy8PLzzzjsYNWoUvvvuO1haWmr9vurIyspCdXW1Vucy7BA1Y+oGI4YiIuNlZ2enEna04ebmBuD+CI+7u7vYXlBQUGe0p3YEyd/fHz179kTr1q2xb98+jB07tlE11Jo7d67KtiAIyMvLw8GDBzFx4kSt+mTYIWoB1AlFDERELZePjw/c3NyQlJSErl27Arh/V1VycjJWr1790HMFQUBFhe6Wnk5PT1fZNjExgbOzM/71r3898k6thjDsEBEAjhIRGbuSkhL8+uuv4nZ2djYyMjLg4OCAtm3bIiYmBitXroS/vz/8/f2xcuVKWFtbY9y4cQCAa9euYffu3YiMjISzszN+//13rF69GlZWVhg8eLDYb05ODm7fvo2cnBwolUpkZGQAuP8YiFatWj2yzuPHj+v2g8PAE5Q3bNiAzp07i0NwoaGh+Oabb8T96ixwRERNixOtiZqnH374AV27dhVHbubOnYuuXbvizTffBADMnz8fMTExmDZtGkJCQvD777/j8OHD4ho7lpaWOHXqFAYPHox27dph1KhRsLGxQUpKClxcXMT3efPNN9G1a1fExsaipKREfM8ffvhBrTr79u2LwsLCOu3FxcVa33ouEwRB0OpMHfjyyy9hamqKdu3aAbh/T//bb7+N9PR0BAYGYvXq1VixYoXKAkcnT55UWeDoUYqLi6FQKBA4ZSVMLfQ7eYqINMeRIpISZcU9ZK57HUVFRY2eB9OQ2u8ln62LYWKt/fdSTdk9ZL+4Qq+1GoKJiQny8/NVAhRwf/5QmzZtUFVVpXGfal3G+vs9/OpQ5xc/dOhQle0VK1Zgw4YNOHv2LAICAtRa4IiImjdtRoEYkIiMz4ULF8Sfr1y5onLLu1KpxKFDh9CmTRut+lYr7Njb29e59exhZDIZfvnlF40W/1Eqlfjss89QWlqK0NBQrRY4AoCKigqViVLaBDUikjZdXiZjcCKShi5dukAmk0Emk9V7ucrKygrvv/++Vn2rPUH5888/h4ODwyOPEwRBZaLSo1y8eBGhoaG4d+8eWrVqhX379iEgIAApKSkA6l/g6MaNGw32Fx8fLy5VTUT0KJxfRA+SVRq6gpYrOzsbgiDA19cX33//PZydncV9FhYWcHFxEZ+/pSm1wo6XlxeeeuopODo6qtWpr68vzM3N1Tq2Q4cOyMjIQGFhIfbs2YOJEyciOTlZ3K/OAkcPWrRokco9+sXFxTpdxpqIiIh0z8vLC8D9R0PomlphJzs7W6NOL126pPaxFhYW4gTlkJAQpKam4r333sOCBQsAqLfA0YO0eSYIERERScuVK1eQk5ODykrV4bZhw4Zp3Jfk1tmpXZyoMQsc1ce8TIBptcFuPCMdqrJWf/4YERE1L9euXcOzzz6LixcvQiaTofam8dqrOkqlUuM+tQo733//PU6cOIGCgoI6w01r165Vu5/XX38dUVFR8PT0xN27d7Fr1y6cOHEChw4dgkwme+QCR9QymZc1XWhlsCIialqzZ8+Gj48Pjhw5Is7fuXXrFl599VW88847WvWpcdhZuXIllixZgg4dOsDV1VVl/owmd2wBwJ9//okJEyYgLy8PCoUCnTt3xqFDhzBgwAAA9xc4Ki8vx7Rp03Dnzh306NFDZYEjIn1rTLBiUCIi0tyZM2dw7NgxODs7w8TEBCYmJujduzfi4+Mxa9asOo+TUIfGYee9997DJ598gujoaI3f7O+2bNny0P0ymQxxcXGIi4tr9HsRNTVNghKDEVHLU33PHCYy9W7mqU/NPc0v5zQHSqVSfKyEk5MT/vjjD3To0AFeXl7IzMzUqk+Nw46JiQl69eql1ZsRUf3UCUYMRETUEgQFBeHChQvw9fVFjx49sGbNGlhYWGDTpk0ard/3II2fjTVnzhx88MEHWr0ZEWnPvEx45IuIqLlbsmSJOB94+fLluHHjBp588kl8/fXXWL9+vVZ9ajyyM2/ePAwZMgR+fn4ICAios57O3r17tSqEiBrvUYGHo0NEJHUDBw4Uf/b19cWVK1dw+/ZttG7dWuO5wbU0DjszZ87E8ePH0adPHzg6Omr9xkTU9BiGiEjKqqurYWlpiYyMDAQFBYnt6jzB4WE0Djvbt2/Hnj17MGTIkEa9MRFJz8PCEIMQEembmZkZvLy8tFpL52E0nrPj4OAAPz8/nRZBRNLH+UJE1BSWLFmCRYsW4fbt2zrrU+ORnbi4OMTGxmLr1q2wtubjgonoPt5RRkS6sH79evz666/w8PCAl5cXbGxsVPafP39e4z41Djvr169HVlYWXF1d4e3tXWeCsjZFEFHLwEBERI/yzDPP6LxPjcOOPoogIqql6SUxhiMi4xIbG6vzPjUOOw8rovZhXURETYWP9CAyPoWFhfj888+RlZWF1157DQ4ODjh//jxcXV3Rpk0bjfvTOOzEx8dj0aJFddqVSiWef/55/Oc//9G4CCIiQ5DSxGoGL6L7Lly4gP79+0OhUOD69et4+eWX4eDggH379uHGjRvYvn27xn1qHHbWrVsHR0dHvPLKK2KbUqnEmDFjcOnSJY0LICIiaQWvlsykkv8cDG3u3LmIjo7GmjVrVB78HRUVhXHjxmnVp8Zh5+uvv0b//v1hb2+PUaNGoaqqCqNHj8bPP/+M48ePa1VEUzArE2BWxX+JjVWVDf+vmIjIGKSmpuKjjz6q096mTRvk5+dr1afGYad79+7Yt28fhg8fDrlcji1btiArKwvHjx+Hq6urVkUQNZZ5qe6DLAMUEVHTs7S0RHFxcZ32zMxMODs7a9WnxosKAkBERAT+/e9/45///CeuX7+O5ORkBh0yOualgsYvIiJqnOHDh2PZsmWoqqoCAMhkMuTk5GDhwoV47rnntOpTrZGdESNG1Nvu7OwMe3t7lfk7fBAotWTqBh6OGhERSk2BGlPtzy9vxLkS9s4772Dw4MFwcXFBeXk5wsPDkZ+fj9DQUKxYsUKrPtUKOwqFot72B59MSkTqUycUMRARUUtkZ2eH06dP49ixYzh//jxqamrQrVs39O/fX+s+1Qo7W7du1foNiEg7DwtEDEJEZOz69u2Lvn376qQvrebsEJFhce4QERmzo0eP4umnn4afnx/atWuHp59+GkeOHNG6P7XCTrdu3XDnzh21O+3duzd+//13rYsiosZhCCKi5iohIQGDBg2Cra0tZs+ejVmzZsHOzg6DBw9GQkKCVn2qdRkrIyMDP/74IxwcHNTqNCMjAxUVFVoVRET6w0tjRCR18fHxePfddzFjxgyxbdasWejVqxdWrFih0q4utdfZ6devn9rPvpLJ+EeTqLlhECIiKSguLsagQYPqtEdGRmLBggVa9alW2MnOzta448cee0zjc4hImhoKQgxBRKRrw4YNw759+/Daa6+ptH/xxRcYOnSoVn2qFXa8vLy06pyIjNuj5gExDBGRpjp27IgVK1bgxIkTCA0NBQCcPXsW3333HV599VWsX79ePHbWrFlq9anx4yKIiNTFS2NEpKktW7agdevWuHLlCq5cuSK229vbY8uWLeK2TCZj2CEiaePCikRUH22mzjwK19khIsnic8mIdKe6uhpLliyBj48PrKys4Ovri2XLlqGmpkY8RiaT1ft6++23xWMiIiLq7B8zZowhPpLaOLJDREZB28DD0SNqKVavXo2NGzciMTERgYGB+OGHH/Diiy9CoVBg9uzZAIC8vDyVc7755htMnjy5zgM4X375ZSxbtkzctrKy0lmdgiDg888/x/Hjx1FQUKASxgDtnsGpcdjx9fVFamoqHB0dVdoLCwvRrVs3XLt2TeMiiIgMxdCjQgxb1FTOnDmD4cOHY8iQIQAAb29v/Oc//8EPP/wgHuPm5qZyzhdffIE+ffrA19dXpd3a2rrOsboye/ZsbNq0CX369IGrq6tOlrPROOxcv34dSqWyTntFRYWkV022KKmBmXnNow8kyalsxautZLwMHbZIlayq+f3zKC4uVtmWy+WQy+V1juvduzc2btyIX375Be3bt8ePP/6I06dPY926dfX2++eff+LgwYNITEyss+/TTz/Fjh074OrqiqioKMTGxsLW1lYnn2fHjh3Yu3cvBg8erJP+AA3CzoEDB8Sfv/32W5UnoSuVShw9ehTe3t46K4yolkWJfkMqwxQRGYJpuQlMBO3//sju3T/X09NTpT02NhZxcXF1jl+wYAGKiorw+OOPw9TUFEqlEitWrMDYsWPr7T8xMRG2trYYMWKESvv48ePh4+MDNzc3XLp0CYsWLcKPP/6IpKQkrT/LgxQKRZ2RpMZSO+w888wzAO5PXpo4caLKPnNzc3h7e+Nf//qXTosjagrahCkGJCKSitzcXNjZ2Ynb9Y3qAMDu3buxY8cO7Ny5E4GBgcjIyEBMTAw8PDzqfK8DwCeffILx48fD0tJSpf3ll18Wfw4KCoK/vz9CQkJw/vx5dOvWrdGfJy4uDkuXLsUnn3yis7lAaoed2glCPj4+SE1NhZOTk04KIGqONAlIDEZEpE92dnYqYachr732GhYuXCjeOdWpUyfcuHED8fHxdcLOqVOnkJmZid27dz+y327dusHc3BxXr17VSdgZOXIk/vOf/8DFxQXe3t4wNzdX2X/+/HmN+9R4zo4+7n8nMmaPCkYMQ0TUFMrKymBiovr3xtTUtM7dTsD9hf26d++O4ODgR/Z7+fJlVFVVwd3dXSd1RkdHIy0tDc8//7zhJigDwNGjR3H06NF6bwn75JNPGl0UUUvysDDEIEREujJ06FCsWLECbdu2RWBgINLT07F27VpMmjRJ5bji4mJ89tln9U5NycrKwqefforBgwfDyckJV65cwauvvoquXbuiV69eOqnz4MGD+Pbbb9G7d2+d9AdoEXaWLl2KZcuWISQkBO7u7nzCOZEeMQgRka68//77eOONNzBt2jQUFBTAw8MDU6ZMwZtvvqly3K5duyAIQr0Tly0sLHD06FG89957KCkpgaenJ4YMGYLY2FiYmprqpE5PT0+1LstpQiYIgkb32bm7u2PNmjWYMGGCTgvRl+LiYigUCoQNXAYzc8tHn0BkBBiEiLRTXXUPP+xZgqKiIp1/4daq/V7yfmsFTCy1/16quXcP199YrNdaDeHgwYN4//33sXHjRp3d5a3xyE5lZSXCwsJ08uZEpB8NjQgxBBGR1D3//PMoKyuDn58frK2t60xQvn37tsZ9ahx2XnrpJezcuRNvvPGGxm9GRIbFEEREUtfQIoeNoVbYmTt3rvhzTU0NNm3ahCNHjqBz5851EtfatWt1WyER6R1DEBFJRX1r/jSWWmEnPT1dZbtLly4AgEuXLqm0c7IykXHhBGkiMoSsrCxs3boVWVlZeO+99+Di4oJDhw7B09MTgYGBGvenVtg5fvy4xh0TkXHjaBAR6UNycjKioqLQq1cvnDx5EitWrICLiwsuXLiAjz/+GJ9//rnGffKvEhHplEVJTYMvIqJHWbhwIZYvX46kpCRYWFiI7X369MGZM2e06lPjCcrPPvtsvZerZDIZLC0t0a5dO4wbNw4dOnTQqiAiMl7qBB6ODBG1bBcvXsTOnTvrtDs7O+PWrVta9anxXxWFQoFjx47h/PnzYuhJT0/HsWPHUF1djd27dyM4OBjfffedVgURUcv2sJEhjhARGT97e3vk5eXVaU9PT0ebNm206lPjkR03NzeMGzcOCQkJ4jM2ampqMHv2bNja2mLXrl2YOnUqFixYgNOnT2tVFBHRw2gaeDhaRFJkWi6DqaD9jT3Ke8Z5U9C4ceOwYMECfPbZZ5DJZKipqcF3332HefPm4YUXXtCqT43DzpYtW/Ddd9+pPEzMxMQEM2fORFhYGFauXIkZM2bgySef1KogIiJd08doEAMUkX6sWLEC0dHRaNOmDQRBQEBAAJRKJcaNG4clS5Zo1afGYae6uho///wz2rdvr9L+888/Q6lUAgAsLS15GzoRGTVeTjNOJlX852po5ubm+PTTT/HWW2/h/PnzqKmpQdeuXeHv7691nxqHnQkTJmDy5Ml4/fXX8cQTT0Amk+H777/HypUrxeGl5ORkre6D1yez0mqYmVUbugxqpOpWGv8rS0REzciyZcswb948+Pr6wtfXV2wvLy/H22+/XefBperQ+EGgSqUSq1atQkJCAv78808AgKurK2bOnIkFCxbA1NQUOTk5MDExwWOPPaZxQbpW+8C1p3q/CTMzPgiU6scQRUTA/QeBpnz7ZpM8CNTv9ZUwbcSDQJX37iFr5etG9yBQU1NT5OXlwcXFRaX91q1bcHFxEa8iaULjv/CmpqZYvHgxFi9ejOLiYgCo80tu27atxoUQGZJZieajfgxIRES6JwhCvVNhfvzxRzg4OGjVZ6P+WhtTkiTSlDoBiYGIiEg9rVu3hkwmg0wmQ/v27VUCj1KpRElJCaZOnapV32r9Je7WrRuOHj2K1q1bo2vXrg+dfHz+/HmtCiEyRgxERETqWbduHQRBwKRJk7B06VIoFApxn4WFBby9vREaGqpV32r9lR0+fDjkcjkA4JlnntHqjYiofg8LRAxCRNRS1D7t3MfHB7169YKZme7+/qnVU2xsbL0/E5F+MQgRUUsTHh6u8z61WhWrsLAQH3/8MRYtWoTbt28DuH/56vfff9dpcUTUMLOS6npfRESkSuP/Nbxw4QL69+8PhUKB69ev4+WXX4aDgwP27duHGzduYPv27fqok4jUxNEgIiJVGo/szJ07F9HR0bh69SosH1gfICoqCidPntRpcUSkWxwNIqKWSOP/zUtNTcVHH31Up71NmzbIz8/XSVFE1LQaCjwcCSIiY6DxXzJLS0txMcEHZWZmwtnZWSdFEZE08JIYETWFESNGqH3s3r17Ne5f479Ww4cPx7Jly/Df//4XACCTyZCTk4OFCxfiueee07gAImqeGISISFceXFNHHzT+i/TOO+9g8ODBcHFxQXl5OcLDw5Gfn4/Q0FCsWLFCHzUSUTPzqHlADENEgFkZYKr5Y55Esgrd1WJoW7du1Wv/Gv/FsbOzw+nTp3Hs2DHx0evdunVD//799VEfERkhrixNRE1J678mffv2Rd++fRv15vHx8di7dy9+/vlnWFlZISwsDKtXr0aHDh3EYwRBwNKlS7Fp0ybcuXMHPXr0wAcffIDAwMBGvTcRSZumd4kxHBEZj88//xz//e9/kZOTg8rKSpV92jyWSqu/DkePHsXRo0dRUFCAmpoalX2ffPKJ2v0kJydj+vTpeOKJJ1BdXY3FixcjMjISV65cgY2NDQBgzZo1WLt2LbZt24b27dtj+fLlGDBgADIzM2Fra6tN+URkhHRxCz0DE5HhrV+/HosXL8bEiRPxxRdf4MUXX0RWVhZSU1Mxffp0rfrU+L/spUuXYtmyZQgJCYG7u/tDHwr6KIcOHVLZ3rp1K1xcXJCWloannnoKgiBg3bp1WLx4sThTOzExEa6urti5cyemTJmi9XsTEf0d1xwiVPPfAUP78MMPsWnTJowdOxaJiYmYP38+fH198eabb4pPbdCUxmFn48aN2LZtGyZMmKDVGz5MUVERAMDBwQEAkJ2djfz8fERGRorHyOVyhIeHIyUlpd6wU1FRgYqK/83aqu82eSIiIpKmnJwchIWFAQCsrKxw9+5dAMCECRPQs2dPJCQkaNynxisoV1ZWikXokiAImDt3Lnr37o2goCAAEBcpdHV1VTnW1dW1wQUM4+PjoVAoxJenp6fOayUiIiL9cHNzw61btwAAXl5eOHv2LID7AyCCIGjVp8Zh56WXXsLOnTu1erOHmTFjBi5cuID//Oc/dfb9/VKZIAgNXj5btGgRioqKxFdubq7OayUiIiL96Nu3L7788ksAwOTJkzFnzhwMGDAAo0ePxrPPPqtVn2pdxpo7d674c01NDTZt2oQjR46gc+fOMDc3Vzl27dq1Ghcxc+ZMHDhwACdPnsRjjz0mtru5uQG4P8Lj7u4uthcUFNQZ7akll8shl8vrtJuVVMLMVPv5RSQ91bZ1/zkTEVHztmnTJvHmp6lTp8LBwQGnT5/G0KFDMXXqVK36VCvspKenq2x36dIFAHDp0iWVdk0nKwuCgJkzZ2Lfvn04ceIEfHx8VPb7+PjAzc0NSUlJ6Nq1K4D7l9GSk5OxevVqjd6LjI/ZXd2vqMUARURkWCYmJjAx+d+Fp1GjRmHUqFGN6lOtsHP8+PFGvUlDpk+fjp07d+KLL76Ara2tOA9HoVDAysoKMpkMMTExWLlyJfz9/eHv74+VK1fC2toa48aN00tN1LJpE6AYkIiIGufChQsICgqCiYkJLly48NBjO3furHH/Bl1UYsOGDQCAiIgIlfatW7ciOjoaADB//nyUl5dj2rRp4qKChw8f5ho7JBmaBCQGIyIyFG9vb9y4caNO+7Rp0/DBBx9g7969+Oijj5CWloZbt24hPT1dvJJTKysrC/PmzcPp06dRUVGBQYMG4f33329waom6unTpgvz8fLi4uKBLly6QyWT1TkaWyWRQKjV/xoZBw446s6plMhni4uIQFxen/4KI9OxRwYhhiIj0JTU1VSUoXLp0CQMGDMDIkSMBAKWlpejVqxdGjhyJl19+uc75paWliIyMRHBwMI4dOwYAeOONNzB06FCcPXtW5dKTprKzs+Hs7Cz+rGtcLpRIQtQZJWIgIiJt1IaJWqtWrYKfnx/Cw8MBQFw/7/r16/We/9133+H69etIT0+HnZ0dgPtXYhwcHHDs2LFGPSPTy8tL/PnGjRsICwuDmZlqRKmurkZKSorKserSPoYRkUGY3a1o8EVELU9xcbHK68GFdRtSWVmJHTt2YNKkSWrfXFRRUQGZTKZyx7OlpSVMTExw+vRprev/uz59+tS7UnJRURH69OmjVZ8MO0RG5GFBiGGISFrMygGzska8yu/34+npqbKYbnx8/CPfe//+/SgsLBTnx6qjZ8+esLGxwYIFC1BWVobS0lK89tprqKmpQV5enpa/hboaWkvv1q1b4nMzNcXLWEQtyMMCDy+PETVPubm54mUlAPWuNfd3W7ZsQVRUFDw8PNR+H2dnZ3z22Wf4v//7P6xfvx4mJiYYO3YsunXrBlNTU61qf1DtMzBlMhmio6NVPodSqcSFCxe0foIDww4RAeB8IaLmys7OTiXsPMqNGzdw5MgR7N27V+P3ioyMRFZWFm7evAkzMzPY29vDzc2tzjp52lAoFADuj+zY2trCyspK3GdhYYGePXvWO3FaHQw7RKQ23k1G1Pxt3boVLi4uGDJkiNZ9ODk5AQCOHTuGgoICDBs2TCd1AfdvkZ83b57Wl6zqw7BDRDqj7rwghiIiw6ipqcHWrVsxceLEOnc73b59Gzk5Ofjjjz8AAJmZmQDuP7qp9vFNW7duRceOHeHs7IwzZ85g9uzZmDNnDjp06KCzGmNjY3XWVy2GHSJqcppOlmY4ItKNI0eOICcnB5MmTaqz78CBA3jxxRfF7TFjxgC4Hz5q17rLzMzEokWLcPv2bXh7e2Px4sWYM2eOTmv8888/MW/ePBw9ehQFBQV11uRrdosKEhGpQ1d3kjE0UUsXGRnZ4IK+0dHRj7w7a9WqVVi1apUeKlOtIycnB2+88Qbc3d01fu5mfRh2iKjF4O339EjKSkNX0OKdPn0ap06dqvOoisbgOjtEREQkGZ6enmo9TkoTDDtEREQkGevWrcPChQsbfGyFNngZi4iIiCRj9OjRKCsrg5+fH6ytrWFubq6yv75HSTwKww4RERFJxrp163TeJ8MOERERScbEiRN13ifn7BAREZGkZGVlYcmSJRg7diwKCgoAAIcOHcLly5e16o9hh4iIiCQjOTkZnTp1wrlz57B3716UlJQAAC5cuKD16soMO0RERCQZCxcuxPLly5GUlAQLCwuxvU+fPjhz5oxWfXLODhERkQGYlwkwrdZ+PRmTSt2uRSMVFy9exM6dO+u0Ozs749atW1r12WLCjklxOUxMawxdBjWxGjsrQ5dAREQasLe3R15eHnx8fFTa09PT0aZNG636bDFhh1omk+JyvfXNIEVEpHvjxo3DggUL8Nlnn0Emk6Gmpgbfffcd5s2bhxdeeEGrPhl2iLTU2CDFsEREVNeKFSsQHR2NNm3aQBAEBAQEQKlUYty4cViyZIlWfTLsEBmINmGJAYmIjJ25uTk+/fRTvPXWWzh//jxqamrQtWtX+Pv7a90nww5RM6JpQGI4IqLmZtmyZZg3bx58fX3h6+srtpeXl+Ptt9/Gm2++qXGfvPWcyIiZFJdr/CIiMqSlS5eKa+s8qKysDEuXLtWqT47sEJEKbQMPR5GISBcEQYBMJqvT/uOPP8LBwUGrPhl2iEgndDUqxNBE1DK1bt0aMpkMMpkM7du3Vwk8SqUSJSUlmDp1qlZ9M+wQkaRI4VIaAxdR01u3bh0EQcCkSZOwdOlSKBQKcZ+FhQW8vb0RGhqqVd8MO0REfyOFwEWGYaKsMHQJLVbt0859fHwQFhYGc3NznfXNsENERESSER4eDqVSiT179uCnn36CTCZDQEAAhg0bBlNTU636ZNghIiIiyfj1118xePBg/P777+jQoQMEQcAvv/wCT09PHDx4EH5+fhr3yVvPiYiISDJmzZoFPz8/5Obm4vz580hPT0dOTg58fHwwa9YsrfrkyA4RERFJRnJyMs6ePatym7mjoyNWrVqFXr16adUnww4REZEBmJUJMKsStD5f1ohzpUwul+Pu3bt12ktKSmBhYaFVn7yMRURERJLx9NNP45VXXsG5c+cgCAIEQcDZs2cxdepUDBs2TKs+GXaIiIhIMtavXw8/Pz+EhobC0tISlpaW6NWrF9q1a4f33ntPqz55GYuIiIgkw97eHl988QWuXr2Kn376CQAQEBCAdu3aad0nww4RERFJjr+/vxhw6ntWliZ4GYuIiIgkZcuWLQgKChIvYwUFBeHjjz/Wuj+O7BAREZFkvPHGG3j33Xcxc+ZM8VlYZ86cwZw5c3D9+nUsX75c4z4ZdoiIiEgyNmzYgM2bN2Ps2LFi27Bhw9C5c2fMnDlTq7DDy1hEREQkGUqlEiEhIXXau3fvjurqaq36ZNghIiIiyXj++eexYcOGOu2bNm3C+PHjteqTl7GIiIhIUrZs2YLDhw+jZ8+eAICzZ88iNzcXL7zwAubOnSset3btWrX648gOERFRCxAXFweZTKbycnNzUznmp59+wrBhw6BQKGBra4uePXsiJycHAHD79m3MnDkTHTp0gLW1Ndq2bYtZs2ahqKhIp3VeunQJ3bp1g7OzM7KyspCVlQVnZ2d069YNly5dQnp6OtLT05GRkaF2nxzZISIiaiECAwNx5MgRcdvU1FT8OSsrC71798bkyZOxdOlSKBQK/PTTT7C0tAQA/PHHH/jjjz/wzjvvICAgADdu3MDUqVPxxx9/4PPPP9dZjcePH9dZX7UYdoiIiFoIMzOzOqM5tRYvXozBgwdjzZo1Ypuvr6/4c1BQEPbs2SNu+/n5YcWKFXj++edRXV0NMzPpRgpexiIiImrGiouLVV4VFRUNHnv16lV4eHjAx8cHY8aMwbVr1wAANTU1OHjwINq3b4+BAwfCxcUFPXr0wP79+x/63kVFRbCzs5N00AEYdoiIiAzCoqSm0S8A8PT0hEKhEF/x8fH1vl+PHj2wfft2fPvtt9i8eTPy8/MRFhaGW7duoaCgACUlJVi1ahUGDRqEw4cP49lnn8WIESOQnJxcb3+3bt3CW2+9hSlTpujtd6Qr0o5iRETauFti6AqouaqpNHQFGsvNzYWdnZ24LZfL6z0uKipK/LlTp04IDQ2Fn58fEhMTMWbMGADA8OHDMWfOHABAly5dkJKSgo0bNyI8PFylr+LiYgwZMgQBAQGIjY3V9UfSOYYdIlLFoEDUrNjZ2amEHXXZ2NigU6dOuHr1KpycnGBmZoaAgACVYzp27IjTp0+rtN29exeDBg1Cq1atsG/fPpibmzeq/qbAsEPUHDCAEJGOVVRU4KeffsKTTz4JCwsLPPHEE8jMzFQ55pdffoGXl5e4XVxcjIEDB0Iul+PAgQPinVpSx7BD1FgMIkTUDMybNw9Dhw5F27ZtUVBQgOXLl6O4uBgTJ04EALz22msYPXo0nnrqKfTp0weHDh3Cl19+iRMnTgC4P6ITGRmJsrIy7NixQ5wQDQDOzs4qt7FLDcMOGRaDAhFRk/jtt98wduxY3Lx5E87OzujZsyfOnj0rjtw8++yz2LhxI+Lj4zFr1ix06NABe/bsQe/evQEAaWlpOHfuHACgXbt2Kn1nZ2fD29u7ST+PJlpO2CkpBUyqDF0FERGRQezateuRx0yaNAmTJk2qd19ERAQEQdB1WU2Ct54TERGRUWPYISIiIqPGsENERERGjWGHiIiIjBrDDhERERk1hh0iIiIyagw7REREZNQYdoiIiMioGTTsnDx5EkOHDoWHhwdkMhn279+vsl8QBMTFxcHDwwNWVlaIiIjA5cuXDVMsERERNUsGDTulpaUIDg5GQkJCvfvXrFmDtWvXIiEhAampqXBzc8OAAQNw9+7dJq6UiIhIt8xKq2FW0ohXabWhP0KzYdDHRURFRSEqKqrefYIgYN26dVi8eDFGjBgBAEhMTISrqyt27tyJKVOmNGWpRERE1ExJds5OdnY28vPzERkZKbbJ5XKEh4cjJSWlwfMqKirEJ7E++ERWIiIiapkkG3by8/MBAK6urirtrq6u4r76xMfHQ6FQiC9PT0+91klERETSJtmwU0smk6lsC4JQp+1BixYtQlFRkfjKzc3Vd4lEREQkYQads/Mwbm5uAO6P8Li7u4vtBQUFdUZ7HiSXyyGXy/VeHxERETUPkh3Z8fHxgZubG5KSksS2yspKJCcnIywszICVERERUXNi0JGdkpIS/Prrr+J2dnY2MjIy4ODggLZt2yImJgYrV66Ev78//P39sXLlSlhbW2PcuHEGrJqIiIiaE4OGnR9++AF9+vQRt+fOnQsAmDhxIrZt24b58+ejvLwc06ZNw507d9CjRw8cPnwYtra2hiqZiIiImhmDhp2IiAgIgtDgfplMhri4OMTFxTVdUURERGRUJDtnh4iIiEgXGHaIiIjIqDHsEBERkVFj2CEiIiKjxrBDRERERk2yKygTEREZM7OSSpiZNvz4o0dSVuquGCPHkR0iIiIyagw7REREZNQYdoiIiMiocc4OEZEO1RQWG7oEaoQagfNgjBHDDhHR/8egQmScGHaIqEVgkCFquRh2iMgoMMwQUUMYdoioWWGoISJNMewQkWQx2BCRLjDsEJFkMNwQkT5wnR0iMpiawmKVFxE1nfj4eMhkMsTExIhtgiAgLi4OHh4esLKyQkREBC5fvlzn3DNnzqBv376wsbGBvb09IiIiUF5e3oTVa4Zhh4iaFMMNkeGlpqZi06ZN6Ny5s0r7mjVrsHbtWiQkJCA1NRVubm4YMGAA7t69Kx5z5swZDBo0CJGRkfj++++RmpqKGTNmwMREupFCupURkdFgwCGSjpKSEowfPx6bN29G69atxXZBELBu3TosXrwYI0aMQFBQEBITE1FWVoadO3eKx82ZMwezZs3CwoULERgYCH9/f/zzn/+EXC43xMdRC8MOEekFAw6RNE2fPh1DhgxB//79Vdqzs7ORn5+PyMhIsU0ulyM8PBwpKSkAgIKCApw7dw4uLi4ICwuDq6srwsPDcfr06Sb9DJriBGUi0hkGG6KmV1ys+t+dXC5vcJRl165dOH/+PFJTU+vsy8/PBwC4urqqtLu6uuLGjRsAgGvXrgEA4uLi8M4776BLly7Yvn07+vXrh0uXLsHf37/Rn0cfGHaIqFEYcIi0Y1JcDhPTGu3PV1YAADw9PVXaY2NjERcXV+f43NxczJ49G4cPH4alpWWD/cpkMpVtQRDEtpqa+/VOmTIFL774IgCga9euOHr0KD755BPEx8dr/Xn0iWGHiLTCkEMkDbm5ubCzsxO3GxrVSUtLQ0FBAbp37y62KZVKnDx5EgkJCcjMzARwf4TH3d1dPKagoEAc7altDwgIUOm7Y8eOyMnJ0c0H0gPO2SEitXEeDpH02NnZqbwaCjv9+vXDxYsXkZGRIb5CQkIwfvx4ZGRkwNfXF25ubkhKShLPqaysRHJyMsLCwgAA3t7e8PDwEINRrV9++QVeXl76+5CNxJEdInokhhui5s/W1hZBQUEqbTY2NnB0dBTbY2JisHLlSvj7+8Pf3x8rV66EtbU1xo0bB+D+Ja7XXnsNsbGxCA4ORpcuXZCYmIiff/4Zn3/+eZN/JnUx7BBRgxhyiFqW+fPno7y8HNOmTcOdO3fQo0cPHD58GLa2tuIxMTExuHfvHubMmYPbt28jODgYSUlJ8PPzM2DlDycTBEEwdBH6VFxcDIVCgf4uL8HMxMLQ5RA1Cww51FJVC5U4VvFfFBUVqcyD0SXxe8l3NsxMtV+bplpZgSPX3tNrrcaCIztEJGLIISJjxLBDRAw5RGTUGHaIWjCGHCJqCRh2iFoghhwiakkYdohaEIYcImqJGHaIWgCGHCJqyRh2iIwYQw4RER8XQWS0GHSIiO7jyA6RkWHIISJSxbBDZCQYcoiamZJSwKRK+/NrKnVXi5Fj2CFq5hhyiIgejmGHqJliyCEiUg8nKBM1Qww6RETq48gOUTPCkENEpDmGHaJmgCGHiEh7DDtEEsaQQ0TUeAw7RBLEkENEpDucoEwkMQw6RES6xZEdIolgyCEi0g+GHSIDY8ghItIvhh0iA2HIISJqGgw7RE2MIYeIqGkx7BA1EYYcIiLDYNgh0iMGHCIiw2PYIdIDhhwiepSaomLUyCy0P1+o1GE1xo1hh0iHGHKIiKSHYYeokRhwiIikjWGHSAsMOEREzQfDDpGaGHCIiJonhh2ih2DAISJq/hh2iP6GAYeIyLgw7FCLx3BDRGTcGHaoxWG4ISJqWRh2yGgx1BAREcCwQ0aAoYaIiB6GYYckjUGGiIgay8TQBajjww8/hI+PDywtLdG9e3ecOnXK0CWRmmoKixv1IiIi3YiPj8cTTzwBW1tbuLi44JlnnkFmZqbKMdHR0ZDJZCqvnj17qhxTUVGBmTNnwsnJCTY2Nhg2bBh+++23pvwoGpP8yM7u3bsRExODDz/8EL169cJHH32EqKgoXLlyBW3btlW7n8Y+cI2IiKg5S05OxvTp0/HEE0+guroaixcvRmRkJK5cuQIbGxvxuEGDBmHr1q3itoWF6ndnTEwMvvzyS+zatQuOjo549dVX8fTTTyMtLQ2mpqZN9nk0IRMEQTB0EQ/To0cPdOvWDRs2bBDbOnbsiGeeeQbx8fGPPL+4uBgKhQJ95aNgxrBDREQPUS1U4ljFf1FUVAQ7Ozu9vIeuvpcaW+tff/0FFxcXJCcn46mnngJwf2SnsLAQ+/fvr/ecoqIiODs749///jdGjx4NAPjjjz/g6emJr7/+GgMHDtT68+iTpEd2KisrkZaWhoULF6q0R0ZGIiUlpd5zKioqUFFRIW4XFRUBAKqFKv0VSkRERqH2u6IpxgGqUQU04m2qcb/W4mLVS/5yuRxyufyR59d+Pzo4OKi0nzhxAi4uLrC3t0d4eDhWrFgBFxcXAEBaWhqqqqoQGRkpHu/h4YGgoCCkpKQw7Gjj5s2bUCqVcHV1VWl3dXVFfn5+vefEx8dj6dKlddpPVu7TS41ERGR8bt26BYVCoZe+LSws4ObmhpP5jf9eatWqFTw9PVXaYmNjERcX99DzBEHA3Llz0bt3bwQFBYntUVFRGDlyJLy8vJCdnY033ngDffv2RVpaGuRyOfLz82FhYYHWrVur9Pew72UpkHTYqSWTyVS2BUGo01Zr0aJFmDt3rrhdWFgILy8v5OTk6O1fXF0qLi6Gp6cncnNz9TaEqkusV79Yr/41t5pZr34VFRWhbdu2dUY7dMnS0hLZ2dmorKxsdF/1fR+qM6ozY8YMXLhwAadPn1Zpr700BQBBQUEICQmBl5cXDh48iBEjRmhUh5RIOuw4OTnB1NS0TlosKCioM9pTq6HhO4VC0Sz+Q6tlZ2fHevWI9epXc6sXaH41s179MjHR783KlpaWsLS01Ot7NGTmzJk4cOAATp48iccee+yhx7q7u8PLywtXr14FALi5uaGyshJ37txRGd0pKChAWFiYXutuDEnfem5hYYHu3bsjKSlJpT0pKUnSv1QiIiKpEQQBM2bMwN69e3Hs2DH4+Pg88pxbt24hNzcX7u7uAIDu3bvD3Nxc5Xs5Ly8Ply5dkvT3sqRHdgBg7ty5mDBhAkJCQhAaGopNmzYhJycHU6dONXRpREREzcb06dOxc+dOfPHFF7C1tRWvmigUClhZWaGkpARxcXF47rnn4O7ujuvXr+P111+Hk5MTnn32WfHYyZMn49VXX4WjoyMcHBwwb948dOrUCf379zfkx3soyYed0aNH49atW1i2bBny8vIQFBSEr7/+Gl5eXmqdL5fLERsbq9Y1TClgvfrFevWrudULNL+aWa9+Nbd6NVG7hEtERIRK+9atWxEdHQ1TU1NcvHgR27dvR2FhIdzd3dGnTx/s3r0btra24vHvvvsuzMzMMGrUKJSXl6Nfv37Ytm2bZNfYAZrBOjtEREREjSHpOTtEREREjcWwQ0REREaNYYeIiIiMGsMOERERGTWjDjsffvghfHx8YGlpie7du+PUqVOGLkl08uRJDB06FB4eHpDJZHUeuiYIAuLi4uDh4QErKytERETg8uXLBqk1Pj4eTzzxBGxtbeHi4oJnnnkGmZmZkq13w4YN6Ny5s7iIWWhoKL755htJ1lqf+Ph4yGQyxMTEiG1SqzkuLg4ymUzl5ebmJtl6AeD333/H888/D0dHR1hbW6NLly5IS0sT90upZm9v7zq/X5lMhunTp0uuVgCorq7GkiVL4OPjAysrK/j6+mLZsmWoqakRj5FazXfv3kVMTAy8vLxgZWWFsLAwpKamSrZeaiTBSO3atUswNzcXNm/eLFy5ckWYPXu2YGNjI9y4ccPQpQmCIAhff/21sHjxYmHPnj0CAGHfvn0q+1etWiXY2toKe/bsES5evCiMHj1acHd3F4qLi5u81oEDBwpbt24VLl26JGRkZAhDhgwR2rZtK5SUlEiy3gMHDggHDx4UMjMzhczMTOH1118XzM3NhUuXLkmu1r/7/vvvBW9vb6Fz587C7NmzxXap1RwbGysEBgYKeXl54qugoECy9d6+fVvw8vISoqOjhXPnzgnZ2dnCkSNHhF9//VWSNRcUFKj8bpOSkgQAwvHjxyVXqyAIwvLlywVHR0fhq6++ErKzs4XPPvtMaNWqlbBu3TrxGKnVPGrUKCEgIEBITk4Wrl69KsTGxgp2dnbCb7/9Jsl6qXGMNuz84x//EKZOnarS9vjjjwsLFy40UEUN+3vYqampEdzc3IRVq1aJbffu3RMUCoWwceNGA1SoqqCgQAAgJCcnC4Ig/XoFQRBat24tfPzxx5Ku9e7du4K/v7+QlJQkhIeHi2FHijXHxsYKwcHB9e6TYr0LFiwQevfu3eB+Kdb8oNmzZwt+fn5CTU2NJGsdMmSIMGnSJJW2ESNGCM8//7wgCNL7/ZaVlQmmpqbCV199pdIeHBwsLF68WHL1UuMZ5WWsyspKpKWlqTyCHgAiIyORkpJioKrUl52djfz8fJX65XI5wsPDJVF/UVERAIgPypNyvUqlErt27UJpaSlCQ0MlXev06dMxZMiQOquQSrXmq1evwsPDAz4+PhgzZgyuXbsGQJr1HjhwACEhIRg5ciRcXFzQtWtXbN68WdwvxZprVVZWYseOHZg0aRJkMpkka+3duzeOHj2KX375BQDw448/4vTp0xg8eDAA6f1+q6uroVQq6zybysrKCqdPn5ZcvdR4Rhl2bt68CaVSWedhoVJ/BH2t2hqlWL8gCJg7dy569+6NoKAgANKs9+LFi2jVqhXkcjmmTp2Kffv2ISAgQJK1AsCuXbtw/vx5xMfH19knxZp79OiB7du349tvv8XmzZuRn5+PsLAw3Lp1S5L1Xrt2DRs2bIC/vz++/fZbTJ06FbNmzcL27dsBSPN3XGv//v0oLCxEdHQ0AGnWumDBAowdOxaPP/44zM3N0bVrV8TExGDs2LEApFezra0tQkND8dZbb+GPP/6AUqnEjh07cO7cOeTl5UmuXmo8yT8uojH+/rh5QeKPoP87KdY/Y8YMXLhwAadPn66zT0r1dujQARkZGSgsLMSePXswceJEJCcni/ulVGtubi5mz56Nw4cPP/QpyFKqOSoqSvy5U6dOCA0NhZ+fHxITE9GzZ08A0qq3pqYGISEhWLlyJQCga9euuHz5MjZs2IAXXnhBPE5KNdfasmULoqKi4OHhodIupVp3796NHTt2YOfOnQgMDERGRgZiYmLg4eGBiRMnisdJqeZ///vfmDRpEtq0aQNTU1N069YN48aNw/nz58VjpFQvNY5Rjuw4OTnB1NS0TgIvKCiok9SlqPauFqnVP3PmTBw4cADHjx/HY489JrZLsV4LCwu0a9cOISEhiI+PR3BwMN577z1J1pqWloaCggJ0794dZmZmMDMzQ3JyMtavXw8zMzOxLinV/Hc2Njbo1KkTrl69Ksnfsbu7OwICAlTaOnbsiJycHADS/HcYAG7cuIEjR47gpZdeEtukWOtrr72GhQsXYsyYMejUqRMmTJiAOXPmiCOVUqzZz88PycnJKCkpQW5uLr7//ntUVVXBx8dHkvVS4xhl2LGwsED37t1VHkEPAElJSZJ+BH2t2v/YHqy/srISycnJBqlfEATMmDEDe/fuxbFjx+Dj46OyX2r11kcQBFRUVEiy1n79+uHixYvIyMgQXyEhIRg/fjwyMjLg6+sruZr/rqKiAj/99BPc3d0l+Tvu1atXneUSfvnlF/GBwlKsGbj/gEYXFxcMGTJEbJNirWVlZTAxUf06MTU1FW89l2LNtWxsbODu7o47d+7g22+/xfDhwyVdL2nJINOim0DtredbtmwRrly5IsTExAg2NjbC9evXDV2aIAj377xJT08X0tPTBQDC2rVrhfT0dPHW+FWrVgkKhULYu3evcPHiRWHs2LEGu+3x//7v/wSFQiGcOHFC5XbYsrIy8Rgp1bto0SLh5MmTQnZ2tnDhwgXh9ddfF0xMTITDhw9LrtaGPHg3liBIr+ZXX31VOHHihHDt2jXh7NmzwtNPPy3Y2tqK/31Jrd7vv/9eMDMzE1asWCFcvXpV+PTTTwVra2thx44d4jFSq1mpVApt27YVFixYUGef1GqdOHGi0KZNG/HW87179wpOTk7C/PnzJVvzoUOHhG+++Ua4du2acPjwYSE4OFj4xz/+IVRWVkqyXmocow07giAIH3zwgeDl5SVYWFgI3bp1E2+VloLjx48LAOq8Jk6cKAjC/Vs1Y2NjBTc3N0EulwtPPfWUcPHiRYPUWl+dAIStW7eKx0ip3kmTJon/3J2dnYV+/fqJQUdqtTbk72FHajXXrjlibm4ueHh4CCNGjBAuX74s2XoFQRC+/PJLISgoSJDL5cLjjz8ubNq0SWW/1Gr+9ttvBQBCZmZmnX1Sq7W4uFiYPXu20LZtW8HS0lLw9fUVFi9eLFRUVEi25t27dwu+vr6ChYWF4ObmJkyfPl0oLCyUbL3UODJBEASDDCkRERERNQGjnLNDREREVIthh4iIiIwaww4REREZNYYdIiIiMmoMO0RERGTUGHaIiIjIqDHsEBERkVFj2CEiIiKjxrBDpGMRERGIiYmRTD/1iY6OxjPPPNOoPry9vSGTySCTyVBYWKiTupqibyJqeRh2iAzsxIkT9X6p7927F2+99Za47e3tjXXr1jVtcY+wbNky5OXlQaFQiG2bN2+Gl5cXunTpgjNnzojttZ8zKCgISqVSpR97e3ts27ZN3E5NTcWePXv0Xj8RtQwMO0QS5eDgAFtbW0OX8VC2trZwc3ODTCYDAOTk5GDNmjXYtWsXlixZgsmTJ9c5JysrC9u3b39ov87OznBwcNBLzUTU8jDsEOnZjh07EBISIgaDcePGoaCgAABw/fp19OnTBwDQunVryGQyREdHA1C9jBUREYEbN25gzpw54uUdAIiLi0OXLl1U3m/dunXw9vYWt5VKJebOnQt7e3s4Ojpi/vz5+Psj8QRBwJo1a+Dr6wsrKysEBwfj888/1/izFhcXw97eHp07d0b37t1RXl5e55iZM2ciNjYW9+7d07h/IiJtMOwQ6VllZSXeeust/Pjjj9i/fz+ys7PFQOPp6SlersnMzEReXh7ee++9On3s3bsXjz32mHjZKC8vT+33/9e//oVPPvkEW7ZswenTp3H79m3s27dP5ZglS5Zg69at2LBhAy5fvow5c+bg+eefR3JyskafNSgoCMHBwVAoFAgMDMTy5cvrHBMTE4Pq6mokJCRo1DcRkbbMDF0AkbGbNGmS+LOvry/Wr1+Pf/zjHygpKUGrVq3EyzUuLi6wt7evtw8HBweYmpqKo0OaWLduHRYtWoTnnnsOALBx40Z8++234v7S0lKsXbsWx44dQ2hoqFjn6dOn8dFHHyE8PFyj9/v444+xevVqWFtbw8rKqs5+a2trxMbG4vXXX8fLL7+sMt+HiEgfOLJDpGfp6ekYPnw4vLy8YGtri4iICAD357foW1FREfLy8sQQAwBmZmYICQkRt69cuYJ79+5hwIABaNWqlfjavn07srKytHpfR0fHeoNOrcmTJ8PJyQmrV6/Wqn8iIk1wZIdIj0pLSxEZGYnIyEjs2LEDzs7OyMnJwcCBA1FZWdno/k1MTOrMv6mqqtKoj5qaGgDAwYMH0aZNG5V9crm8cQU2wMzMDMuXL0d0dDRmzJihl/cgIqrFkR0iPfr5559x8+ZNrFq1Ck8++SQef/xxcXJyLQsLCwCoczv231lYWNQ5xtnZGfn5+SqBJyMjQ/xZoVDA3d0dZ8+eFduqq6uRlpYmbgcEBEAulyMnJwft2rVTeXl6emr8mdU1cuRIBAYGYunSpXp7DyIigCM7RHrVtm1bWFhY4P3338fUqVNx6dIllbVzAMDLywsymQxfffUVBg8eDCsrK7Rq1apOX97e3jh58iTGjBkDuVwOJycnRERE4K+//sKaNWvwz3/+E4cOHcI333wDOzs78bzZs2dj1apV8Pf3R8eOHbF27VqVNX1sbW0xb948zJkzBzU1NejduzeKi4uRkpKCVq1aYeLEiXr7/axatQoDBw7UW/9ERABHdoj0ytnZGdu2bcNnn32GgIAArFq1Cu+8847KMW3atMHSpUuxcOFCuLq6NnhZZ9myZbh+/Tr8/Pzg7OwMAOjYsSM+/PBDfPDBBwgODsb333+PefPmqZz36quv4oUXXkB0dDRCQ0Nha2uLZ599VuWYt956C2+++Sbi4+PRsWNHDBw4EF9++SV8fHx0+Nuoq2/fvujbty+qq6v1+j5E1LLJhL9f8CciUoO3tzdiYmL09kiLEydOoE+fPrhz506Dd6kREamDYYeItOLt7Y28vDyYm5vj999/1+kt5IGBgbh27Rru3bvHsENEjcawQ0RauXHjhnjnl6+vL0xMdHdVXJ99E1HLw7BDRERERo3/u0RERERGjWGHiIiIjBrDDhERERk1hh0iIiIyagw7REREZNQYdoiIiMioMewQERGRUWPYISIiIqP2/wDKYiOtmUTOpwAAAABJRU5ErkJggg==",
+ "text/plain": [
+ "<Figure size 640x480 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_zonfield(theta,'potential temperature [K]',vmin=250,vmax=1500,levels=25)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "b34b7f1d-733a-467a-803b-4bf934ae9834",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_zonfield(rh*100,'relative humidity [%]')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "94a6defe-d358-4f13-b031-40d1621d2d21",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_zonfield(qv,'specific humidity [kg/kg]',vmin=0,vmax=0.02,levels=21)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "11f4d833-6194-4a06-b6ba-89f4d856ed33",
+ "metadata": {},
+ "source": [
+ "## Now to the moist atmosphere...\n",
+ "\n",
+ "To make it clear we redefine the functions from above"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bed20f84-eeda-42fb-b0d9-b0e3cbf21678",
+ "metadata": {},
+ "source": [
+ "### 6) define latitude independent reference virtual temperature profile (similar to Polvani and Esler, 2007; eqn A5)\n",
+ "\n",
+ "This implies a difference compared to above procedure"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "id": "957f16b4-fea9-4cf5-90f1-3f0cc4aa3ba3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "Tv0 = 300 # K, similar as above"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "e0fddd5a-3497-486f-9cf8-dd784f4a45b5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "Tvr = np.zeros((nz, nlat)) + np.nan\n",
+ "for i in range(0, nz):\n",
+ " Tvr[i, :] = Tv0 + Gamma0/np.power((np.power(zT,-alpha)+np.power(z[i],-alpha)),1/alpha)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "decf0827-da80-41e0-b583-1aafcd830c9f",
+ "metadata": {},
+ "source": [
+ "### 7) compute temperature profile in zonal wind balance with wind field (as in Polvani and Esler, 2007; eqn A4)\n",
+ "#### 7A) define integrand from eqn A4"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "id": "c1d0c333-d1bb-4fd3-944c-03bb7ef14a24",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "@jit\n",
+ "def Tvintegrand(latrad, z, zT, U0, a, Omega):\n",
+ " f = 2*Omega*np.sin(np.deg2rad(45.0))\n",
+ " F = np.power(np.sin(np.pi*np.power(np.sin(latrad),2)),3)\n",
+ " if latrad<0: F=0.0\n",
+ " u1 = U0*F*(z/zT)*np.exp(-0.5*(np.power(z/zT,2)-1))\n",
+ " du1dz = u1*(1/z-z/np.power(zT,2))\n",
+ " return (a*f+2*u1*np.tan(latrad))*du1dz"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f2697afd-e73e-4a2a-9ef7-b17bd397ff1b",
+ "metadata": {},
+ "source": [
+ "#### 7B) integrate"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "id": "231eff75-8291-4c98-9f4d-ad5dff21aadf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "tmpv = np.zeros((nz, nlat)) + np.nan\n",
+ "for i in range(0, nz):\n",
+ " for j in range(0, nlat):\n",
+ " tmpv[i, j] =scipy.integrate.quad(Tvintegrand, 0, latrad[j], args=(z[i], zT, u0, a, Omega))[0]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1faa9a40-5db4-4c6f-b739-ba887cd0890e",
+ "metadata": {},
+ "source": [
+ "#### 7C) add integrand into eqn A4"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "id": "f466e6f9-4b37-4577-861c-488dcc1c5735",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "Tv=Tvr-H/R*tmpv"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "198262cc-978a-4c35-83c2-779df66009a2",
+ "metadata": {},
+ "source": [
+ "### skip step 8) (calculate potential temperature) from above here and do it later"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b634b9e2-db92-42c0-b9bd-5065e3d970dd",
+ "metadata": {},
+ "source": [
+ "### use same rh profile as above. So no need to redefine it (see 9) define relative humidity profile)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4b1d1701-ca92-4be2-9b62-f72e1472aa08",
+ "metadata": {},
+ "source": [
+ "### turn above calculation of qv (10) calculate specific humidty) into a 0D function qv(T,rh,p)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "id": "9537e1a0-6c74-43e0-9c8b-127b1bbecf8d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_qv_0D(T,rh,p):\n",
+ " \n",
+ " # define constants\n",
+ " b1 = 610.78 # --> c1es in mo_convect_tables.f90 \n",
+ " b2w= 17.269 # --> c3les \n",
+ " b3 = 273.15 # --> tmelt; melting temperature in K\n",
+ " b4w= 35.86 # --> c4les\n",
+ "\n",
+ " Rdv = 287.04/461.51 # Rd/Rv; replace Rd by R as values are identical?\n",
+ " o_m_Rdv = 1-Rdv # 1-Rd/Rv\n",
+ " \n",
+ " sat_pres_water = b1*np.exp(b2w*(T-b3)/(T-b4w))\n",
+ "\n",
+ " qv = rh*Rdv*sat_pres_water/(p-o_m_Rdv*sat_pres_water) # Do I understand this equation?\n",
+ " \n",
+ " return qv"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "58578b88-9c85-4eca-900c-75ca99a4dcd9",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "### 11) get Tmoist from Tv\n",
+ "#### 11A) define 1D nonlinear eq to be solved for Tmoist"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "id": "ef2c2424-e32c-484f-b2a3-ba3836bf6a26",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_Tmoist(T,Tv,rh,p):\n",
+ " qv = get_qv_0D(T,rh,p)\n",
+ " return Tv/(1+0.61*qv) - T"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a020ab66-7e75-490a-bca6-7d0549b59b49",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "#### 11B) solve nonlinear eq\n",
+ "\n",
+ "use Tv as initial solution for Tmoist"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "id": "8aae7c13-7085-4be2-86c4-b67eae288798",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from scipy.optimize import fsolve"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "id": "3a0a9581-8790-4029-a904-ee73f21c58b9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "Tmoist = np.zeros((nz, nlat)) + np.nan\n",
+ "for i in range(0, nz):\n",
+ " for j in range(0, nlat):\n",
+ " Tmoist[i,j] = fsolve(get_Tmoist, Tv[i,j], args = (Tv[i,j],rh[i,j],p[i]))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ee5edda6-51bb-48f4-abd1-8b27d673f65f",
+ "metadata": {},
+ "source": [
+ "#### 11C) Check quality of results by seeing whether nonlinear equation is fulfilled"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "id": "f17cd26d-01c0-46a9-ba62-00bcfe009cca",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for i in range(0, nz):\n",
+ " for j in range(0, nlat):\n",
+ " if not np.isclose(get_Tmoist(Tmoist[i,j],Tv[i,j],rh[i,j],p[i]), 0):\n",
+ " print('Not solved.')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fe1d04c7-fd8c-469c-98d7-2c262649f17f",
+ "metadata": {},
+ "source": [
+ "### 12) calculate potential temperature"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "id": "6ea70539-2937-4029-844b-05a01076ad44",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "theta_moist=Tmoist*np.expand_dims(np.exp(kappa*z/H),axis=1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d28e93cd-39de-49c9-8012-cb6b889f2eea",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "### 13) calculate specific humidty (similar to dry procedure above but turned into a function)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "id": "2fedeed9-8628-4dff-845b-5b939096c06d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_qv(T,rh,p,nz,nlat):\n",
+ " \n",
+ " # define constants\n",
+ " b1 = 610.78 # --> c1es in mo_convect_tables.f90 \n",
+ " b2w= 17.269 # --> c3les \n",
+ " b3 = 273.15 # --> tmelt; melting temperature in K\n",
+ " b4w= 35.86 # --> c4les\n",
+ "\n",
+ " Rdv = 287.04/461.51 # Rd/Rv; replace Rd by R as values are identical?\n",
+ " o_m_Rdv = 1-Rdv # 1-Rd/Rv\n",
+ " \n",
+ " sat_pres_water = b1*np.exp(b2w*(T-b3)/(T-b4w))\n",
+ "\n",
+ " # initialize qv\n",
+ " qv = np.zeros((nz, nlat))\n",
+ " \n",
+ " # calculate qv\n",
+ " for i in range(0, nz):\n",
+ " for j in range(0, nlat):\n",
+ " qv[i, j] = rh[i,j]*Rdv*sat_pres_water[i,j]/(p[i]-o_m_Rdv*sat_pres_water[i,j]) # Do I understand this equation?\n",
+ " \n",
+ " return qv"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "id": "b6e276de-c65d-42ab-a96a-523911c4585f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "qv_moist = get_qv(Tmoist,rh,p,nz,nlat)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8e764d85-712c-4ff7-8ad7-e667191c94d3",
+ "metadata": {},
+ "source": [
+ "### 14) Plotting results for dry atmosphere"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "id": "334a76ef-5a88-4856-bca6-5cf9cf1965ad",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_zonfield(Tmoist,'T [K]')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "id": "06d453c4-406f-43db-a743-bfac773d53db",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_zonfield(theta_moist,'potential temperature [K]',vmin=250,vmax=1500,levels=25)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "id": "e8110102-4180-40f9-8582-6c59ddc60d10",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_zonfield(qv_moist,'specific humidity [kg/kg]',vmin=0,vmax=0.02,levels=21)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "de0a237b-9001-4643-ae3d-d4da6b6a4331",
+ "metadata": {},
+ "source": [
+ "## Finally plotting differences between moist and dry atmosphere"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "id": "16d8809a-56e3-4446-b658-45eccbe20a5a",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_zonfield((Tmoist-T),'Tmoist - T [K]')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "id": "4c1d9e8c-1e2d-47ca-a379-cbc2adfae89b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_zonfield((Tmoist-T)/T*100,'rel. difference in temperature [%]')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "id": "24b5a265-6d54-4761-b3ea-f0eebeae7585",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_zonfield((theta_moist-theta),'theta_moist - theta [K]')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "id": "26daf7f6-ffac-4e98-8f9e-48cfe43c80e2",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_zonfield((theta_moist-theta)/theta*100,'rel. difference in potential temperature [%]')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "id": "d340087e-0414-41e9-ad6e-9d8fc9af93c2",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_zonfield((qv_moist-qv),'qv_moist-qv [kg/kg]')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "id": "c2ce3ec4-7de9-49d1-8320-7cdeb97727cb",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/tmp/ipykernel_107218/2211109322.py:1: RuntimeWarning: invalid value encountered in divide\n",
+ " plot_zonfield((qv_moist-qv)/qv*100,'rel. difference in specific humidity [%]')\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_zonfield((qv_moist-qv)/qv*100,'rel. difference in specific humidity [%]')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ca54a04f-0161-4a2d-b68a-b9789d6fc75c",
+ "metadata": {},
+ "source": [
+ "## Archive"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "id": "c7654ee2-cac7-45cf-9b61-f710877f2fd2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 2d function that did not work right away\n",
+ "def func(T,Tv,rh,p,nz,nlat):\n",
+ " qv = get_qv(T,rh,p,nz,nlat)\n",
+ " return Tv/(1+0.61*qv) - T"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7d79add1-ad7f-4e5b-85ae-3bf397dac681",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (based on the module python3/2023.01)",
+ "language": "python",
+ "name": "python3_2023_01"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Scripts_for_preprocessing/generate_initialdata/extpar_helper.py b/Scripts_for_preprocessing/generate_initialdata/extpar_helper.py
new file mode 100644
index 0000000000000000000000000000000000000000..d7d12df0b5cf5cf674950b4508fd2d395a8c966b
--- /dev/null
+++ b/Scripts_for_preprocessing/generate_initialdata/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/Scripts_for_preprocessing/generate_initialdata/lc1_initial_condition_fixedCoriolisParameter_4K.py b/Scripts_for_preprocessing/generate_initialdata/lc1_initial_condition_fixedCoriolisParameter_4K.py
new file mode 100644
index 0000000000000000000000000000000000000000..9480bf28f8642a49ca6d45e154875c2326492d02
--- /dev/null
+++ b/Scripts_for_preprocessing/generate_initialdata/lc1_initial_condition_fixedCoriolisParameter_4K.py
@@ -0,0 +1,221 @@
+import numpy as np
+import scipy.integrate
+import matplotlib.pyplot as plt
+import netCDF4 as nc
+from numba import jit
+
+@jit
+def Tintegrand(latrad, z, zT, U0, a, Omega):
+ f = 2*Omega*np.sin(np.deg2rad(45.0))
+ F = np.power(np.sin(np.pi*np.power(np.sin(latrad),2)),3)
+ if latrad<0: F=0.0
+ u1 = U0*F*(z/zT)*np.exp(-0.5*(np.power(z/zT,2)-1))
+ du1dz = u1*(1/z-z/np.power(zT,2))
+ return (a*f+2*u1*np.tan(latrad))*du1dz
+
+def load_levelinfo():
+ from netCDF4 import Dataset
+ file = Dataset('./ifs2icon_verticalgridinfo_137levels.nc', 'r')
+ hyam = np.squeeze(np.array(file.variables['hyam']))
+ hybm = np.squeeze(np.array(file.variables['hybm']))
+ hyai = np.squeeze(np.array(file.variables['hyai']))
+ hybi = np.squeeze(np.array(file.variables['hybi']))
+ lev = np.squeeze(np.array(file.variables['lev' ]))
+ lev_2= np.squeeze(np.array(file.variables['lev_2']))
+ return hyam, hybm, hyai, hybi, lev, lev_2
+
+hyam, hybm, hyai, hybi, lev, lev_2 = load_levelinfo()
+
+# constants
+# physical constants are set to the values used in icon-nwp-2.0.15/src/shared/mo_physical_constants.f90
+u0 = 45.0 # in m/s
+zT = 13.0e3 # in m
+H = 7.5e3 # in m
+R = 287.04 # dry gas constant in J/(kg K) (parameter rd in ICON)
+a = 6.371229e6 # average Earth radius in m (parameter earth_radius in ICON)
+Omega = 7.29212e-5 # angular velocity in 1/s (parameter earth_angular_velocity in ICON)
+t0 = 300 # in K
+Gamma0 = -6.5e-3 # in K/m
+alpha = 10 # unitless
+kappa = 2.0/7.0 # unitless
+g = 9.80665 # av. gravitational acceleration in m/s2 (parameter grav in ICON)
+p0 = 1.0e5 # globally-uniform surface pressure in Pa
+# for relative humidity following Booth et al., 2013 Climate Dynamics
+zTrh = 12.0e3
+rh0 = 0.80 # relative humidity scaling factor from 0..1
+
+#latitude-longitude grid
+lat = np.linspace(-90, 90, 360)
+lon = np.linspace(0.0,360,10)
+nlat = lat.size
+nlon = lon.size
+
+# vertical grid: for computation of initial state we convert the ifs2icon hybrid levels
+# to height levels assuming a globally-uniform surface pressure (defined above)
+# and defining ehight according to Polvani and Elsner as z = H ln (p0/p)
+p = hyam + hybm*p0
+z = H*np.log(p0/p) # np.log is natural logarithm
+nz = z.size
+#print(z, nz)
+
+# latitude in radians
+latrad = lat * np.pi/180.0 #lat2
+
+# lifecycle 1
+u1 = np.zeros((nz,nlat))+np.nan
+F = np.power(np.sin(np.pi*np.power(np.sin(latrad),2)),3); F[lat<0] = 0.0
+for i in range(0, nz):
+ for j in range(0, nlat):
+ u1[i,j] = u0*F[j]*(z[i]/zT)*np.exp(-0.5*(np.power(z[i]/zT,2)-1))
+#du1dz = u1*np.expand_dims(1/z-z/np.power(zT,2),axis=1)
+
+# compute temperature profile in zonal wind balance with u1
+t=np.zeros((nz, nlat)) + np.nan
+
+# latitude independe reference profile
+tr = np.zeros((nz, nlat)) + np.nan
+for i in range(0, nz):
+ tr[i, :] = t0 + Gamma0/np.power((np.power(zT,-alpha)+np.power(z[i],-alpha)),1/alpha)
+
+# latitude dependent modification
+tmp = np.zeros((nz, nlat)) + np.nan
+for i in range(0, nz):
+ for j in range(0, nlat):
+ tmp[i, j] =scipy.integrate.quad(Tintegrand, 0, latrad[j], args=(z[i], zT, u0, a, Omega))[0]
+t=tr-H/R*tmp
+
+# add 4K to temperature field
+t = t + 4
+
+# potential temperature
+theta=t*np.expand_dims(np.exp(kappa*z/H),axis=1)
+
+# relative humidity
+# follows Booth et al. 2013, Climate Dynamics
+rh = np.zeros((nz, nlat)) + np.nan
+for i in range(0, nz):
+ rh[i, :] = rh0*np.power(1-0.85*z[i]/zTrh, 1.25)
+ if z[i]>14e3:
+ rh[i, :] = 0.0
+
+
+# specific humidty
+# follows calculation in icon
+# /icon-nwp-2.0.15/src/atm_phy_schemes/mo_satad.f90 -> sat_pres_water,spec_humi
+b1 = 610.78 # --> c1es in mo_convect_tables.f90
+b2w= 17.269 # --> c3les
+b3 = 273.15 # --> tmelt; melting temperature in K
+b4w= 35.86 # --> c4les
+sat_pres_water = b1*np.exp(b2w*(t-b3)/(t-b4w))
+Rdv = 287.04/461.51 # Rd/Rv
+o_m_Rdv = 1-Rdv # 1-Rd/Rv
+print(Rdv, o_m_Rdv)
+qv = np.zeros((nz, nlat))
+for i in range(0, nz):
+ for j in range(0, nlat):
+ qv[i, j] = rh[i,j]*Rdv*sat_pres_water[i,j]/(p[i]-o_m_Rdv*sat_pres_water[i,j])
+
+#---------------------------------------
+# save to netcdf files
+#---------------------------------------
+ncfile = nc.Dataset('lc1_initialcondition_4K_r10x360.nc', 'w', clobber=True, format='NETCDF3_CLASSIC')
+ncfile.description = 'Initial condition for LC1, for U and T according to Polvani and Esler 2007 JGR'
+
+# dimensions
+ncfile.createDimension('lat', nlat)
+ncfile.createDimension('lon', nlon)
+ncfile.createDimension('lev', nz)
+ncfile.createDimension('lev_2', 1)
+ncfile.createDimension('nhym', nz)
+ncfile.createDimension('nhyi', nz+1)
+ncfile.createDimension('time', 1)
+
+# variables
+nc_latitude = ncfile.createVariable('lat' , 'f8', ('lat',))
+nc_longitude = ncfile.createVariable('lon' , 'f8', ('lon',))
+nc_lev = ncfile.createVariable('lev' , 'f8', ('lev',))
+nc_lev_2 = ncfile.createVariable('lev_2' , 'f8', ('lev_2',))
+nc_hyam = ncfile.createVariable('hyam' , 'f8', ('nhym',))
+nc_hybm = ncfile.createVariable('hybm' , 'f8', ('nhym',))
+nc_hyai = ncfile.createVariable('hyai' , 'f8', ('nhyi',))
+nc_hybi = ncfile.createVariable('hybi' , 'f8', ('nhyi',))
+nc_time = ncfile.createVariable('time' , 'f8', ('time',))
+nc_T = ncfile.createVariable('T' , 'f4', ('time', 'lev', 'lat', 'lon'))
+nc_U = ncfile.createVariable('U' , 'f4', ('time', 'lev', 'lat', 'lon'))
+nc_V = ncfile.createVariable('V' , 'f4', ('time', 'lev', 'lat', 'lon'))
+nc_W = ncfile.createVariable('W' , 'f4', ('time', 'lev', 'lat', 'lon'))
+nc_QV = ncfile.createVariable('QV' , 'f4', ('time', 'lev', 'lat', 'lon'))
+nc_QC = ncfile.createVariable('QC' , 'f4', ('time', 'lev', 'lat', 'lon'))
+nc_QI = ncfile.createVariable('QI' , 'f4', ('time', 'lev', 'lat', 'lon'))
+nc_SST = ncfile.createVariable('SST' , 'f4', ('time', 'lat', 'lon'))
+nc_LNPS = ncfile.createVariable('LNPS' , 'f4', ('time', 'lev_2', 'lat', 'lon'))
+nc_GEOP_SFC = ncfile.createVariable('GEOP_SFC', 'f4', ('time', 'lev_2', 'lat', 'lon'))
+nc_GEOP_ML = ncfile.createVariable('GEOP_ML' , 'f4', ('time', 'lev_2', 'lat', 'lon'))
+
+# set horizontal grid data
+nc_latitude[:] = lat
+nc_longitude[:] = lon
+
+# set vertical grid data
+nc_lev[:] = lev
+nc_lev.standard_name = "hybrid_sigma_pressure"
+nc_lev.long_name = "hybrid level at layer midpoints"
+nc_lev.formula = "hyam hybm (mlev=hyam+hybm*aps)"
+nc_lev.formula_terms = "ap: hyam b: hybm ps: aps"
+nc_lev.units = "level"
+nc_lev.positive = "down"
+nc_lev_2[:] = lev_2
+nc_lev_2.standard_name = "hybrid_sigma_pressure"
+nc_lev_2.long_name = "hybrid level at layer midpoints"
+nc_lev_2.formula = "hyam hybm (mlev=hyam+hybm*aps)"
+nc_lev_2.formula_terms = "ap: hyam b: hybm ps: aps"
+nc_lev_2.units = "level"
+nc_lev_2.positive = "down"
+nc_hyam[:] = hyam
+nc_hyam.long_name = "hybrid A coefficient at layer midpoints"
+nc_hyam.units = "Pa"
+nc_hybm[:] = hybm
+nc_hybm.long_name = "hybrid B coefficient at layer midpoints"
+nc_hybm.units = "1"
+nc_hyai[:] = hyai
+nc_hyai.long_name = "hybrid A coefficient at layer interfaces"
+nc_hyai.units = "Pa"
+nc_hybi[:] = hybi
+nc_hybi.long_name = "hybrid B coefficient at layer interfaces"
+nc_hybi.units = "1"
+
+# set time data
+nc_time[0] = 23790716.25 # this is the time from ifs2icon_0010_R02B04_aquaplanet.nc
+nc_time.standard_name = "time"
+nc_time.units = "day as %Y%m%d.%f"
+nc_time.calendar = "proleptic_gregorian"
+nc_time.axis = "T"
+
+# set variable data
+for i in range(0, nlon):
+ nc_T[:,:,:,i] = t
+ nc_U[:,:,:,i] = u1
+ nc_V[:,:,:,i] = 0.0
+ nc_W[:,:,:,i] = 0.0
+ nc_QV[:,:,:,i] = qv
+ nc_QC[:,:,:,i] = 0.0
+ nc_QI[:,:,:,i] = 0.0
+ nc_SST[:,:,i] = t[nz-1,:] - 0.5 # as in Booth et al., 2013, Clim. Dynamics we set the SST to lowest-level initial T-0.5K
+ nc_LNPS[:,:,:,i] = np.log(p0)
+ nc_GEOP_SFC[:,:,:,i] = 0.0
+ nc_GEOP_ML[:,:,:,i] = g*z[nz-1]
+
+# set variable attributes
+nc_T.units = "K"; nc_T.standard_name = "temperature"; nc_T.long_name = "Atmospheric temperature"
+nc_U.units = "m s**-1"; nc_U.standard_name = "u-wind"; nc_U.long_name = "Zonal wind"
+nc_V.units = "m s**-1"; nc_V.standard_name = "v-wind"; nc_V.long_name = "Meridional wind"
+nc_W.units = "Pa s**-1"; nc_W.standard_name = "lagrangian_tendency_of_air_pressure"; nc_W.long_name = "Vertical Velocity (Pressure) (omega=dp/dt)"
+nc_QV.units = "kg kg**-1"; nc_QV.standard_name = "spec. humidity"; nc_QV.long_name = "Specific humidity"
+nc_QC.units = "kg kg**-1"; nc_QC.standard_name = "cloud liq. water"; nc_QC.long_name = "Specific cloud liquid water content"
+nc_QI.units = "kg kg**-1"; nc_QI.standard_name = "cloud ice"; nc_QI.long_name = "Specific cloud ice water content"
+nc_GEOP_ML.units = "m**2 s**-2"; nc_GEOP_ML.standard_name = "geopotential"; nc_GEOP_ML.long_name = "Geopotential"
+nc_SST.units = "K"; nc_SST.standard_name = "sst"; nc_SST.long_name = "Sea-surface temperature"
+nc_LNPS.units = "n/a"; nc_LNPS.standard_name = "ln sfc pressure"; nc_LNPS.long_name = "Logarithm of surface pressure"
+nc_GEOP_SFC.units = "m**2 s**-2"; nc_GEOP_SFC.standard_name = "sfc geopotential"; nc_GEOP_SFC.long_name = "Sfc Geopotential"
+
+ncfile.close()
diff --git a/Scripts_for_preprocessing/generate_initialdata/lc1_initial_condition_fixedCoriolisParameter_4K_qvfromcontrol.py b/Scripts_for_preprocessing/generate_initialdata/lc1_initial_condition_fixedCoriolisParameter_4K_qvfromcontrol.py
new file mode 100644
index 0000000000000000000000000000000000000000..0df036ede1db70d1fc27da1fcb0700754e1becf2
--- /dev/null
+++ b/Scripts_for_preprocessing/generate_initialdata/lc1_initial_condition_fixedCoriolisParameter_4K_qvfromcontrol.py
@@ -0,0 +1,222 @@
+import numpy as np
+import scipy.integrate
+import matplotlib.pyplot as plt
+import netCDF4 as nc
+from numba import jit
+
+@jit
+def Tintegrand(latrad, z, zT, U0, a, Omega):
+ f = 2*Omega*np.sin(np.deg2rad(45.0))
+ F = np.power(np.sin(np.pi*np.power(np.sin(latrad),2)),3)
+ if latrad<0: F=0.0
+ u1 = U0*F*(z/zT)*np.exp(-0.5*(np.power(z/zT,2)-1))
+ du1dz = u1*(1/z-z/np.power(zT,2))
+ return (a*f+2*u1*np.tan(latrad))*du1dz
+
+def load_levelinfo():
+ from netCDF4 import Dataset
+ file = Dataset('./ifs2icon_verticalgridinfo_137levels.nc', 'r')
+ hyam = np.squeeze(np.array(file.variables['hyam']))
+ hybm = np.squeeze(np.array(file.variables['hybm']))
+ hyai = np.squeeze(np.array(file.variables['hyai']))
+ hybi = np.squeeze(np.array(file.variables['hybi']))
+ lev = np.squeeze(np.array(file.variables['lev' ]))
+ lev_2= np.squeeze(np.array(file.variables['lev_2']))
+ return hyam, hybm, hyai, hybi, lev, lev_2
+
+hyam, hybm, hyai, hybi, lev, lev_2 = load_levelinfo()
+
+# constants
+# physical constants are set to the values used in icon-nwp-2.0.15/src/shared/mo_physical_constants.f90
+u0 = 45.0 # in m/s
+zT = 13.0e3 # in m
+H = 7.5e3 # in m
+R = 287.04 # dry gas constant in J/(kg K) (parameter rd in ICON)
+a = 6.371229e6 # average Earth radius in m (parameter earth_radius in ICON)
+Omega = 7.29212e-5 # angular velocity in 1/s (parameter earth_angular_velocity in ICON)
+t0 = 300 # in K
+Gamma0 = -6.5e-3 # in K/m
+alpha = 10 # unitless
+kappa = 2.0/7.0 # unitless
+g = 9.80665 # av. gravitational acceleration in m/s2 (parameter grav in ICON)
+p0 = 1.0e5 # globally-uniform surface pressure in Pa
+# for relative humidity following Booth et al., 2013 Climate Dynamics
+zTrh = 12.0e3
+rh0 = 0.80 # relative humidity scaling factor from 0..1
+
+#latitude-longitude grid
+lat = np.linspace(-90, 90, 360)
+lon = np.linspace(0.0,360,10)
+nlat = lat.size
+nlon = lon.size
+
+# vertical grid: for computation of initial state we convert the ifs2icon hybrid levels
+# to height levels assuming a globally-uniform surface pressure (defined above)
+# and defining ehight according to Polvani and Elsner as z = H ln (p0/p)
+p = hyam + hybm*p0
+z = H*np.log(p0/p) # np.log is natural logarithm
+nz = z.size
+#print(z, nz)
+
+# latitude in radians
+latrad = lat * np.pi/180.0 #lat2
+
+# lifecycle 1
+u1 = np.zeros((nz,nlat))+np.nan
+F = np.power(np.sin(np.pi*np.power(np.sin(latrad),2)),3); F[lat<0] = 0.0
+for i in range(0, nz):
+ for j in range(0, nlat):
+ u1[i,j] = u0*F[j]*(z[i]/zT)*np.exp(-0.5*(np.power(z[i]/zT,2)-1))
+#du1dz = u1*np.expand_dims(1/z-z/np.power(zT,2),axis=1)
+
+# compute temperature profile in zonal wind balance with u1
+t=np.zeros((nz, nlat)) + np.nan
+
+# latitude independe reference profile
+tr = np.zeros((nz, nlat)) + np.nan
+for i in range(0, nz):
+ tr[i, :] = t0 + Gamma0/np.power((np.power(zT,-alpha)+np.power(z[i],-alpha)),1/alpha)
+
+# latitude dependent modification
+tmp = np.zeros((nz, nlat)) + np.nan
+for i in range(0, nz):
+ for j in range(0, nlat):
+ tmp[i, j] =scipy.integrate.quad(Tintegrand, 0, latrad[j], args=(z[i], zT, u0, a, Omega))[0]
+t=tr-H/R*tmp
+
+# add 4K to temperature field
+t = t + 4
+
+# potential temperature
+theta=t*np.expand_dims(np.exp(kappa*z/H),axis=1)
+
+# relative humidity
+# follows Booth et al. 2013, Climate Dynamics
+rh = np.zeros((nz, nlat)) + np.nan
+for i in range(0, nz):
+ rh[i, :] = rh0*np.power(1-0.85*z[i]/zTrh, 1.25)
+ if z[i]>14e3:
+ rh[i, :] = 0.0
+
+
+# specific humidty
+# should be consistent with temperature field from control simulation (t-4)
+# follows calculation in icon
+# /icon-nwp-2.0.15/src/atm_phy_schemes/mo_satad.f90 -> sat_pres_water,spec_humi
+b1 = 610.78 # --> c1es in mo_convect_tables.f90
+b2w= 17.269 # --> c3les
+b3 = 273.15 # --> tmelt; melting temperature in K
+b4w= 35.86 # --> c4les
+sat_pres_water = b1*np.exp(b2w*(t-4-b3)/(t-4-b4w))
+Rdv = 287.04/461.51 # Rd/Rv
+o_m_Rdv = 1-Rdv # 1-Rd/Rv
+print(Rdv, o_m_Rdv)
+qv = np.zeros((nz, nlat))
+for i in range(0, nz):
+ for j in range(0, nlat):
+ qv[i, j] = rh[i,j]*Rdv*sat_pres_water[i,j]/(p[i]-o_m_Rdv*sat_pres_water[i,j])
+
+#---------------------------------------
+# save to netcdf files
+#---------------------------------------
+ncfile = nc.Dataset('lc1_initialcondition_4K_qvCTL_r10x360.nc', 'w', clobber=True, format='NETCDF3_CLASSIC')
+ncfile.description = 'Initial condition for LC1, for U and T according to Polvani and Esler 2007 JGR'
+
+# dimensions
+ncfile.createDimension('lat', nlat)
+ncfile.createDimension('lon', nlon)
+ncfile.createDimension('lev', nz)
+ncfile.createDimension('lev_2', 1)
+ncfile.createDimension('nhym', nz)
+ncfile.createDimension('nhyi', nz+1)
+ncfile.createDimension('time', 1)
+
+# variables
+nc_latitude = ncfile.createVariable('lat' , 'f8', ('lat',))
+nc_longitude = ncfile.createVariable('lon' , 'f8', ('lon',))
+nc_lev = ncfile.createVariable('lev' , 'f8', ('lev',))
+nc_lev_2 = ncfile.createVariable('lev_2' , 'f8', ('lev_2',))
+nc_hyam = ncfile.createVariable('hyam' , 'f8', ('nhym',))
+nc_hybm = ncfile.createVariable('hybm' , 'f8', ('nhym',))
+nc_hyai = ncfile.createVariable('hyai' , 'f8', ('nhyi',))
+nc_hybi = ncfile.createVariable('hybi' , 'f8', ('nhyi',))
+nc_time = ncfile.createVariable('time' , 'f8', ('time',))
+nc_T = ncfile.createVariable('T' , 'f4', ('time', 'lev', 'lat', 'lon'))
+nc_U = ncfile.createVariable('U' , 'f4', ('time', 'lev', 'lat', 'lon'))
+nc_V = ncfile.createVariable('V' , 'f4', ('time', 'lev', 'lat', 'lon'))
+nc_W = ncfile.createVariable('W' , 'f4', ('time', 'lev', 'lat', 'lon'))
+nc_QV = ncfile.createVariable('QV' , 'f4', ('time', 'lev', 'lat', 'lon'))
+nc_QC = ncfile.createVariable('QC' , 'f4', ('time', 'lev', 'lat', 'lon'))
+nc_QI = ncfile.createVariable('QI' , 'f4', ('time', 'lev', 'lat', 'lon'))
+nc_SST = ncfile.createVariable('SST' , 'f4', ('time', 'lat', 'lon'))
+nc_LNPS = ncfile.createVariable('LNPS' , 'f4', ('time', 'lev_2', 'lat', 'lon'))
+nc_GEOP_SFC = ncfile.createVariable('GEOP_SFC', 'f4', ('time', 'lev_2', 'lat', 'lon'))
+nc_GEOP_ML = ncfile.createVariable('GEOP_ML' , 'f4', ('time', 'lev_2', 'lat', 'lon'))
+
+# set horizontal grid data
+nc_latitude[:] = lat
+nc_longitude[:] = lon
+
+# set vertical grid data
+nc_lev[:] = lev
+nc_lev.standard_name = "hybrid_sigma_pressure"
+nc_lev.long_name = "hybrid level at layer midpoints"
+nc_lev.formula = "hyam hybm (mlev=hyam+hybm*aps)"
+nc_lev.formula_terms = "ap: hyam b: hybm ps: aps"
+nc_lev.units = "level"
+nc_lev.positive = "down"
+nc_lev_2[:] = lev_2
+nc_lev_2.standard_name = "hybrid_sigma_pressure"
+nc_lev_2.long_name = "hybrid level at layer midpoints"
+nc_lev_2.formula = "hyam hybm (mlev=hyam+hybm*aps)"
+nc_lev_2.formula_terms = "ap: hyam b: hybm ps: aps"
+nc_lev_2.units = "level"
+nc_lev_2.positive = "down"
+nc_hyam[:] = hyam
+nc_hyam.long_name = "hybrid A coefficient at layer midpoints"
+nc_hyam.units = "Pa"
+nc_hybm[:] = hybm
+nc_hybm.long_name = "hybrid B coefficient at layer midpoints"
+nc_hybm.units = "1"
+nc_hyai[:] = hyai
+nc_hyai.long_name = "hybrid A coefficient at layer interfaces"
+nc_hyai.units = "Pa"
+nc_hybi[:] = hybi
+nc_hybi.long_name = "hybrid B coefficient at layer interfaces"
+nc_hybi.units = "1"
+
+# set time data
+nc_time[0] = 23790716.25 # this is the time from ifs2icon_0010_R02B04_aquaplanet.nc
+nc_time.standard_name = "time"
+nc_time.units = "day as %Y%m%d.%f"
+nc_time.calendar = "proleptic_gregorian"
+nc_time.axis = "T"
+
+# set variable data
+for i in range(0, nlon):
+ nc_T[:,:,:,i] = t
+ nc_U[:,:,:,i] = u1
+ nc_V[:,:,:,i] = 0.0
+ nc_W[:,:,:,i] = 0.0
+ nc_QV[:,:,:,i] = qv
+ nc_QC[:,:,:,i] = 0.0
+ nc_QI[:,:,:,i] = 0.0
+ nc_SST[:,:,i] = t[nz-1,:] - 0.5 # as in Booth et al., 2013, Clim. Dynamics we set the SST to lowest-level initial T-0.5K
+ nc_LNPS[:,:,:,i] = np.log(p0)
+ nc_GEOP_SFC[:,:,:,i] = 0.0
+ nc_GEOP_ML[:,:,:,i] = g*z[nz-1]
+
+# set variable attributes
+nc_T.units = "K"; nc_T.standard_name = "temperature"; nc_T.long_name = "Atmospheric temperature"
+nc_U.units = "m s**-1"; nc_U.standard_name = "u-wind"; nc_U.long_name = "Zonal wind"
+nc_V.units = "m s**-1"; nc_V.standard_name = "v-wind"; nc_V.long_name = "Meridional wind"
+nc_W.units = "Pa s**-1"; nc_W.standard_name = "lagrangian_tendency_of_air_pressure"; nc_W.long_name = "Vertical Velocity (Pressure) (omega=dp/dt)"
+nc_QV.units = "kg kg**-1"; nc_QV.standard_name = "spec. humidity"; nc_QV.long_name = "Specific humidity"
+nc_QC.units = "kg kg**-1"; nc_QC.standard_name = "cloud liq. water"; nc_QC.long_name = "Specific cloud liquid water content"
+nc_QI.units = "kg kg**-1"; nc_QI.standard_name = "cloud ice"; nc_QI.long_name = "Specific cloud ice water content"
+nc_GEOP_ML.units = "m**2 s**-2"; nc_GEOP_ML.standard_name = "geopotential"; nc_GEOP_ML.long_name = "Geopotential"
+nc_SST.units = "K"; nc_SST.standard_name = "sst"; nc_SST.long_name = "Sea-surface temperature"
+nc_LNPS.units = "n/a"; nc_LNPS.standard_name = "ln sfc pressure"; nc_LNPS.long_name = "Logarithm of surface pressure"
+nc_GEOP_SFC.units = "m**2 s**-2"; nc_GEOP_SFC.standard_name = "sfc geopotential"; nc_GEOP_SFC.long_name = "Sfc Geopotential"
+
+ncfile.close()
diff --git a/Scripts_for_preprocessing/generate_initialdata/lc1_initial_condition_fixedCoriolisParameter_CTL.py b/Scripts_for_preprocessing/generate_initialdata/lc1_initial_condition_fixedCoriolisParameter_CTL.py
new file mode 100644
index 0000000000000000000000000000000000000000..89fccef86825e0e27156a2b8715e8db6b8d3738c
--- /dev/null
+++ b/Scripts_for_preprocessing/generate_initialdata/lc1_initial_condition_fixedCoriolisParameter_CTL.py
@@ -0,0 +1,218 @@
+import numpy as np
+import scipy.integrate
+import matplotlib.pyplot as plt
+import netCDF4 as nc
+from numba import jit
+
+@jit
+def Tintegrand(latrad, z, zT, U0, a, Omega):
+ f = 2*Omega*np.sin(np.deg2rad(45.0))
+ F = np.power(np.sin(np.pi*np.power(np.sin(latrad),2)),3)
+ if latrad<0: F=0.0
+ u1 = U0*F*(z/zT)*np.exp(-0.5*(np.power(z/zT,2)-1))
+ du1dz = u1*(1/z-z/np.power(zT,2))
+ return (a*f+2*u1*np.tan(latrad))*du1dz
+
+def load_levelinfo():
+ from netCDF4 import Dataset
+ file = Dataset('./ifs2icon_verticalgridinfo_137levels.nc', 'r')
+ hyam = np.squeeze(np.array(file.variables['hyam']))
+ hybm = np.squeeze(np.array(file.variables['hybm']))
+ hyai = np.squeeze(np.array(file.variables['hyai']))
+ hybi = np.squeeze(np.array(file.variables['hybi']))
+ lev = np.squeeze(np.array(file.variables['lev' ]))
+ lev_2= np.squeeze(np.array(file.variables['lev_2']))
+ return hyam, hybm, hyai, hybi, lev, lev_2
+
+hyam, hybm, hyai, hybi, lev, lev_2 = load_levelinfo()
+
+# constants
+# physical constants are set to the values used in icon-nwp-2.0.15/src/shared/mo_physical_constants.f90
+u0 = 45.0 # in m/s
+zT = 13.0e3 # in m
+H = 7.5e3 # in m
+R = 287.04 # dry gas constant in J/(kg K) (parameter rd in ICON)
+a = 6.371229e6 # average Earth radius in m (parameter earth_radius in ICON)
+Omega = 7.29212e-5 # angular velocity in 1/s (parameter earth_angular_velocity in ICON)
+t0 = 300 # in K
+Gamma0 = -6.5e-3 # in K/m
+alpha = 10 # unitless
+kappa = 2.0/7.0 # unitless
+g = 9.80665 # av. gravitational acceleration in m/s2 (parameter grav in ICON)
+p0 = 1.0e5 # globally-uniform surface pressure in Pa
+# for relative humidity following Booth et al., 2013 Climate Dynamics
+zTrh = 12.0e3
+rh0 = 0.80 # relative humidity scaling factor from 0..1
+
+#latitude-longitude grid
+lat = np.linspace(-90, 90, 360)
+lon = np.linspace(0.0,360,10)
+nlat = lat.size
+nlon = lon.size
+
+# vertical grid: for computation of initial state we convert the ifs2icon hybrid levels
+# to height levels assuming a globally-uniform surface pressure (defined above)
+# and defining ehight according to Polvani and Elsner as z = H ln (p0/p)
+p = hyam + hybm*p0
+z = H*np.log(p0/p) # np.log is natural logarithm
+nz = z.size
+#print(z, nz)
+
+# latitude in radians
+latrad = lat * np.pi/180.0 #lat2
+
+# lifecycle 1
+u1 = np.zeros((nz,nlat))+np.nan
+F = np.power(np.sin(np.pi*np.power(np.sin(latrad),2)),3); F[lat<0] = 0.0
+for i in range(0, nz):
+ for j in range(0, nlat):
+ u1[i,j] = u0*F[j]*(z[i]/zT)*np.exp(-0.5*(np.power(z[i]/zT,2)-1))
+#du1dz = u1*np.expand_dims(1/z-z/np.power(zT,2),axis=1)
+
+# compute temperature profile in zonal wind balance with u1
+t=np.zeros((nz, nlat)) + np.nan
+
+# latitude independe reference profile
+tr = np.zeros((nz, nlat)) + np.nan
+for i in range(0, nz):
+ tr[i, :] = t0 + Gamma0/np.power((np.power(zT,-alpha)+np.power(z[i],-alpha)),1/alpha)
+
+# latitude dependent modification
+tmp = np.zeros((nz, nlat)) + np.nan
+for i in range(0, nz):
+ for j in range(0, nlat):
+ tmp[i, j] =scipy.integrate.quad(Tintegrand, 0, latrad[j], args=(z[i], zT, u0, a, Omega))[0]
+t=tr-H/R*tmp
+
+# potential temperature
+theta=t*np.expand_dims(np.exp(kappa*z/H),axis=1)
+
+# relative humidity
+# follows Booth et al. 2013, Climate Dynamics
+rh = np.zeros((nz, nlat)) + np.nan
+for i in range(0, nz):
+ rh[i, :] = rh0*np.power(1-0.85*z[i]/zTrh, 1.25)
+ if z[i]>14e3:
+ rh[i, :] = 0.0
+
+
+# specific humidty
+# follows calculation in icon
+# /icon-nwp-2.0.15/src/atm_phy_schemes/mo_satad.f90 -> sat_pres_water,spec_humi
+b1 = 610.78 # --> c1es in mo_convect_tables.f90
+b2w= 17.269 # --> c3les
+b3 = 273.15 # --> tmelt; melting temperature in K
+b4w= 35.86 # --> c4les
+sat_pres_water = b1*np.exp(b2w*(t-b3)/(t-b4w))
+Rdv = 287.04/461.51 # Rd/Rv
+o_m_Rdv = 1-Rdv # 1-Rd/Rv
+print(Rdv, o_m_Rdv)
+qv = np.zeros((nz, nlat))
+for i in range(0, nz):
+ for j in range(0, nlat):
+ qv[i, j] = rh[i,j]*Rdv*sat_pres_water[i,j]/(p[i]-o_m_Rdv*sat_pres_water[i,j])
+
+#---------------------------------------
+# save to netcdf files
+#---------------------------------------
+ncfile = nc.Dataset('lc1_initialcondition_CTL_r10x360.nc', 'w', clobber=True, format='NETCDF3_CLASSIC')
+ncfile.description = 'Initial condition for LC1, for U and T according to Polvani and Esler 2007 JGR'
+
+# dimensions
+ncfile.createDimension('lat', nlat)
+ncfile.createDimension('lon', nlon)
+ncfile.createDimension('lev', nz)
+ncfile.createDimension('lev_2', 1)
+ncfile.createDimension('nhym', nz)
+ncfile.createDimension('nhyi', nz+1)
+ncfile.createDimension('time', 1)
+
+# variables
+nc_latitude = ncfile.createVariable('lat' , 'f8', ('lat',))
+nc_longitude = ncfile.createVariable('lon' , 'f8', ('lon',))
+nc_lev = ncfile.createVariable('lev' , 'f8', ('lev',))
+nc_lev_2 = ncfile.createVariable('lev_2' , 'f8', ('lev_2',))
+nc_hyam = ncfile.createVariable('hyam' , 'f8', ('nhym',))
+nc_hybm = ncfile.createVariable('hybm' , 'f8', ('nhym',))
+nc_hyai = ncfile.createVariable('hyai' , 'f8', ('nhyi',))
+nc_hybi = ncfile.createVariable('hybi' , 'f8', ('nhyi',))
+nc_time = ncfile.createVariable('time' , 'f8', ('time',))
+nc_T = ncfile.createVariable('T' , 'f4', ('time', 'lev', 'lat', 'lon'))
+nc_U = ncfile.createVariable('U' , 'f4', ('time', 'lev', 'lat', 'lon'))
+nc_V = ncfile.createVariable('V' , 'f4', ('time', 'lev', 'lat', 'lon'))
+nc_W = ncfile.createVariable('W' , 'f4', ('time', 'lev', 'lat', 'lon'))
+nc_QV = ncfile.createVariable('QV' , 'f4', ('time', 'lev', 'lat', 'lon'))
+nc_QC = ncfile.createVariable('QC' , 'f4', ('time', 'lev', 'lat', 'lon'))
+nc_QI = ncfile.createVariable('QI' , 'f4', ('time', 'lev', 'lat', 'lon'))
+nc_SST = ncfile.createVariable('SST' , 'f4', ('time', 'lat', 'lon'))
+nc_LNPS = ncfile.createVariable('LNPS' , 'f4', ('time', 'lev_2', 'lat', 'lon'))
+nc_GEOP_SFC = ncfile.createVariable('GEOP_SFC', 'f4', ('time', 'lev_2', 'lat', 'lon'))
+nc_GEOP_ML = ncfile.createVariable('GEOP_ML' , 'f4', ('time', 'lev_2', 'lat', 'lon'))
+
+# set horizontal grid data
+nc_latitude[:] = lat
+nc_longitude[:] = lon
+
+# set vertical grid data
+nc_lev[:] = lev
+nc_lev.standard_name = "hybrid_sigma_pressure"
+nc_lev.long_name = "hybrid level at layer midpoints"
+nc_lev.formula = "hyam hybm (mlev=hyam+hybm*aps)"
+nc_lev.formula_terms = "ap: hyam b: hybm ps: aps"
+nc_lev.units = "level"
+nc_lev.positive = "down"
+nc_lev_2[:] = lev_2
+nc_lev_2.standard_name = "hybrid_sigma_pressure"
+nc_lev_2.long_name = "hybrid level at layer midpoints"
+nc_lev_2.formula = "hyam hybm (mlev=hyam+hybm*aps)"
+nc_lev_2.formula_terms = "ap: hyam b: hybm ps: aps"
+nc_lev_2.units = "level"
+nc_lev_2.positive = "down"
+nc_hyam[:] = hyam
+nc_hyam.long_name = "hybrid A coefficient at layer midpoints"
+nc_hyam.units = "Pa"
+nc_hybm[:] = hybm
+nc_hybm.long_name = "hybrid B coefficient at layer midpoints"
+nc_hybm.units = "1"
+nc_hyai[:] = hyai
+nc_hyai.long_name = "hybrid A coefficient at layer interfaces"
+nc_hyai.units = "Pa"
+nc_hybi[:] = hybi
+nc_hybi.long_name = "hybrid B coefficient at layer interfaces"
+nc_hybi.units = "1"
+
+# set time data
+nc_time[0] = 23790716.25 # this is the time from ifs2icon_0010_R02B04_aquaplanet.nc
+nc_time.standard_name = "time"
+nc_time.units = "day as %Y%m%d.%f"
+nc_time.calendar = "proleptic_gregorian"
+nc_time.axis = "T"
+
+# set variable data
+for i in range(0, nlon):
+ nc_T[:,:,:,i] = t
+ nc_U[:,:,:,i] = u1
+ nc_V[:,:,:,i] = 0.0
+ nc_W[:,:,:,i] = 0.0
+ nc_QV[:,:,:,i] = qv
+ nc_QC[:,:,:,i] = 0.0
+ nc_QI[:,:,:,i] = 0.0
+ nc_SST[:,:,i] = t[nz-1,:] - 0.5 # as in Booth et al., 2013, Clim. Dynamics we set the SST to lowest-level initial T-0.5K
+ nc_LNPS[:,:,:,i] = np.log(p0)
+ nc_GEOP_SFC[:,:,:,i] = 0.0
+ nc_GEOP_ML[:,:,:,i] = g*z[nz-1]
+
+# set variable attributes
+nc_T.units = "K"; nc_T.standard_name = "temperature"; nc_T.long_name = "Atmospheric temperature"
+nc_U.units = "m s**-1"; nc_U.standard_name = "u-wind"; nc_U.long_name = "Zonal wind"
+nc_V.units = "m s**-1"; nc_V.standard_name = "v-wind"; nc_V.long_name = "Meridional wind"
+nc_W.units = "Pa s**-1"; nc_W.standard_name = "lagrangian_tendency_of_air_pressure"; nc_W.long_name = "Vertical Velocity (Pressure) (omega=dp/dt)"
+nc_QV.units = "kg kg**-1"; nc_QV.standard_name = "spec. humidity"; nc_QV.long_name = "Specific humidity"
+nc_QC.units = "kg kg**-1"; nc_QC.standard_name = "cloud liq. water"; nc_QC.long_name = "Specific cloud liquid water content"
+nc_QI.units = "kg kg**-1"; nc_QI.standard_name = "cloud ice"; nc_QI.long_name = "Specific cloud ice water content"
+nc_GEOP_ML.units = "m**2 s**-2"; nc_GEOP_ML.standard_name = "geopotential"; nc_GEOP_ML.long_name = "Geopotential"
+nc_SST.units = "K"; nc_SST.standard_name = "sst"; nc_SST.long_name = "Sea-surface temperature"
+nc_LNPS.units = "n/a"; nc_LNPS.standard_name = "ln sfc pressure"; nc_LNPS.long_name = "Logarithm of surface pressure"
+nc_GEOP_SFC.units = "m**2 s**-2"; nc_GEOP_SFC.standard_name = "sfc geopotential"; nc_GEOP_SFC.long_name = "Sfc Geopotential"
+
+ncfile.close()
diff --git a/Scripts_for_preprocessing/generate_initialdata/lc1_initial_condition_fixedCoriolisParameter_Tanom.py b/Scripts_for_preprocessing/generate_initialdata/lc1_initial_condition_fixedCoriolisParameter_Tanom.py
new file mode 100644
index 0000000000000000000000000000000000000000..a4d1988fb29646f8ec4eb4803659c734c23feffc
--- /dev/null
+++ b/Scripts_for_preprocessing/generate_initialdata/lc1_initial_condition_fixedCoriolisParameter_Tanom.py
@@ -0,0 +1,472 @@
+import numpy as np
+import scipy.integrate
+import matplotlib.pyplot as plt
+import netCDF4 as nc
+from numba import jit
+import xarray as xr # for temperature anomalies
+import sys # for temperature anomalies
+
+@jit
+def Tintegrand(latrad, z, zT, U0, a, Omega):
+ f = 2*Omega*np.sin(np.deg2rad(45.0))
+ F = np.power(np.sin(np.pi*np.power(np.sin(latrad),2)),3)
+ if latrad<0: F=0.0
+ u1 = U0*F*(z/zT)*np.exp(-0.5*(np.power(z/zT,2)-1))
+ du1dz = u1*(1/z-z/np.power(zT,2))
+ return (a*f+2*u1*np.tan(latrad))*du1dz
+
+def load_levelinfo():
+ from netCDF4 import Dataset
+ file = Dataset('./ifs2icon_verticalgridinfo_137levels.nc', 'r')
+ hyam = np.squeeze(np.array(file.variables['hyam']))
+ hybm = np.squeeze(np.array(file.variables['hybm']))
+ hyai = np.squeeze(np.array(file.variables['hyai']))
+ hybi = np.squeeze(np.array(file.variables['hybi']))
+ lev = np.squeeze(np.array(file.variables['lev' ]))
+ lev_2= np.squeeze(np.array(file.variables['lev_2']))
+ return hyam, hybm, hyai, hybi, lev, lev_2
+
+hyam, hybm, hyai, hybi, lev, lev_2 = load_levelinfo()
+
+# function to calculate the meridional gradient of variable "var"
+def get_dxdlat(var, lats, levs):
+ nlat = lats.size # length of latitude vector
+ # calculate distance between entries of latitude vector
+ dlat = np.full(lats.shape, np.nan, dtype=float)
+ dlat[0] = np.abs(lats[1] - lats[0])
+ # distance between lat[2]-lat[0], lat[3]-lat[1], etc.
+ dlat[1:nlat-1] = np.abs(lats[2:nlat] - lats[0:nlat-2])
+ dlat[nlat-1] = np.abs(lats[nlat-1] - lats[nlat-2])
+
+ # check that first dimension is levs and second dimension is lats
+ if (var.shape[0] == levs.size) and (var.shape[1] == lats.size):
+ # check that latitudes go from south to north
+ if lats[0] > lats[1]:
+ var = var[:, ::-1]
+ lats = lats[::-1]
+ print('changed lats and var')
+
+ # centered finite differences
+ var_grad = np.full(var.shape, np.nan, dtype=float)
+ var_grad[:, 0] = (var[:, 1] - var[:, 0]) / dlat[0]
+ for la in range(1, nlat-1):
+ var_grad[:, la] = (var[:, la+1] - var[:, la-1]) / dlat[la]
+ del la
+ var_grad[:, nlat-1] = (var[:, nlat-1] - var[:, nlat-2]) / dlat[nlat-1]
+ else:
+ print('ERROR: Dimensions are not lat and lev. ' + \
+ 'Exit function get_dxdlat')
+ return
+
+ return var_grad
+
+# function to calculate the zonal wind from atm. temperature following thermal
+# wind balance
+def get_u_from_t(tanom, tropo, zin, zout, lat, latrad, read_T=False, T=False):
+# input: tanom: atmospheric temperature anomaly, dimensions (zin-lat)
+# tropo: tropopause height in m
+# zin: vertical levels of temperature field in m
+# zout: vertical levels of zonal wind field in m
+# lat: latitudes of temperature field in degree
+# latrad: latitudes in radian
+
+ # 1. prepare the temperature field
+ # 1.1 We want to derive the zonal wind from the surface to TOA.
+ # If levels do not go from surface to TOA, change them.
+ if zin[0] > zin[1]:
+ print('change order of zin to go from surface to TOA')
+ tanom_calc = tanom[::-1, :]
+ zin_calc = zin[::-1]
+ else:
+ tanom_calc = tanom.copy()
+ zin_calc = zin.copy()
+ del tanom, zin
+
+ # 1.2 interpolate the vertical grid to a finer resolution
+ # (10m distance between levels), use height levels zout that
+ # come from the initial data
+ if zout[0] > zout[1]:
+ print('zout from TOA to surface')
+ zin_int = np.arange(zout[-1], zout[0], 10)
+ else:
+ print('zout from surface to TOA')
+ zin_int = np.arange(zout[0], zout[-1], 10)
+ tanom_int = np.full((zin_int.size, lat.size), np.nan, dtype=float)
+ for la in range(lat.size): # loop over latitudes
+ tanom_int[:, la] = np.interp(zin_int, zin_calc, tanom_calc[:,la])
+ del la
+ del tanom_calc, zin_calc
+
+ # 1.3 set temperature anomalies in stratosphere to 0 and smooth the temperature
+ # field around the tropopause to avoid sharp gradients
+ mask = np.full(tanom_int.shape, np.nan, dtype=bool)
+ for la in range(len(lat)): # loop over latitudes
+ mask[:,la] = zin_int < tropo[la]
+ del la
+ # smooth data around the tropopause
+ tanom_mask = tanom_int * mask
+ for la in range(len(lat)): # loop over latitudes
+ # index of highest level below tropopause
+ ind = np.where(mask[:,la])[0][-1]
+ # smooth data for 25 levels below and 24 levels above tropopause
+ x = np.linspace(tanom_int[ind-25, la], 0, 50)
+ tanom_mask[ind-25:ind+25, la] = x
+ del ind, x
+ del la
+
+ del tanom_int, mask, tropo
+
+ # 1.4 smooth the temperature profile
+ window = np.ones(10)/10
+ tanom_mask_runmean = np.full(tanom_mask.shape, np.nan, dtype=float)
+ for le in range(len(zin_int)):
+ tanom_mask_runmean[le, :] = np.convolve(tanom_mask[le, :], window, 'same')
+ del le, window
+
+ del tanom_mask
+
+ # the running mean introduces strong gradients at the North pole
+ # -> set temperature values poleward of 85N to the value at 85N
+ xlat = (np.abs(lat-85)).argmin()
+ for la in range(xlat+1, len(lat)):
+ tanom_mask_runmean[:, la] = tanom_mask_runmean[:, xlat]
+ del la
+ del xlat
+
+ ###################################################################
+ # 2. calculate the zonal wind field
+ # 2.1 helper variables
+ f = 2*Omega*np.sin(np.deg2rad(45.0))
+ var_a = -1*H*a*f/R
+ var_b = -2*H*np.tan(latrad)/R
+
+ # 2.2 meridional temperature gradient
+ dTdlat = get_dxdlat(tanom_mask_runmean, latrad, zin_int)
+
+ # smooth the meridional temperature gradient
+ window = np.ones(20)/20
+ dTdlat_runmean = np.full(dTdlat.shape, np.nan, dtype=float)
+ for le in range(len(zin_int)):
+ dTdlat_runmean[le, :] = np.convolve(dTdlat[le, :], window, 'same')
+ del le, window
+ del dTdlat
+
+ # 2.3 difference between levels
+ # array has one entry less than z: first entry is
+ # the difference between the first and second level
+ dz = np.diff(zin_int)
+
+ # 2.4 calculate zonal wind
+ uanom_int = np.full(tanom_mask_runmean.shape, np.nan, dtype=float)
+
+ # no wind at the surface
+ uanom_int[0, :] = 0.0
+
+ # other levels
+ for le in range(1, len(zin_int)): # loop over levels
+ uanom_int[le, :] = (dTdlat_runmean[le-1,:] + \
+ var_a * uanom_int[le-1,:] / dz[le-1] + \
+ var_b * uanom_int[le-1,:] * uanom_int[le-1,:] / dz[le-1]) * \
+ dz[le-1] / (var_a + var_b * uanom_int[le-1,:])
+ del le
+ del f, var_a, var_b, dTdlat_runmean, dz
+
+ # 2.5 interpolate the calculated zonal wind and the temperature anomaly
+ # to the zout vertical grid
+ uanom = np.full((zout.size, lat.size), np.nan, dtype=float)
+ tanom = np.full((zout.size, lat.size), np.nan, dtype=float)
+
+ # levels must be monotonically increasing:
+ if zout[0] > zout[1]:
+ print('zout is not monotonically increasing. Use zout[::-1].')
+ for la in range(lat.size):
+ uanom[:, la] = np.interp(zout[::-1], zin_int, uanom_int[:, la])
+ tanom[:, la] = np.interp(zout[::-1], zin_int, tanom_mask_runmean[:, la])
+ del la
+ del zin_int, uanom_int, tanom_mask_runmean
+
+ # first level should be TOA again
+ tanom = tanom[::-1, :]
+ uanom = uanom[::-1, :]
+ else:
+ print('zout is monotonically increasing. Use z.')
+ for la in range(lat.size):
+ uanom[:, la] = np.interp(zout, zin_int, uanom_int[:, la])
+ tanom[:, la] = np.interp(zout, zin_int, tanom_mask_runmean[:, la])
+ del la
+ del zin_int, uanom_int, tanom_mask_runmean
+
+ # first level should be TOA again
+ tanom = tanom[::-1, :]
+ uanom = uanom[::-1, :]
+
+ return uanom, tanom
+
+
+# constants
+# physical constants are set to the values used in icon-nwp-2.0.15/src/shared/mo_physical_constants.f90
+u0 = 45.0 # in m/s
+zT = 13.0e3 # in m
+H = 7.5e3 # in m
+R = 287.04 # dry gas constant in J/(kg K) (parameter rd in ICON)
+a = 6.371229e6 # average Earth radius in m (parameter earth_radius in ICON)
+Omega = 7.29212e-5 # angular velocity in 1/s (parameter earth_angular_velocity in ICON)
+t0 = 300 # in K
+Gamma0 = -6.5e-3 # in K/m
+alpha = 10 # unitless
+kappa = 2.0/7.0 # unitless
+g = 9.80665 # av. gravitational acceleration in m/s2 (parameter grav in ICON)
+p0 = 1.0e5 # globally-uniform surface pressure in Pa
+# for relative humidity following Booth et al., 2013 Climate Dynamics
+zTrh = 12.0e3
+rh0 = 0.80 # relative humidity scaling factor from 0..1
+
+#latitude-longitude grid
+lat = np.arange(-89.75, 90, 0.5) #np.linspace(-90, 90, 360)
+lon = np.linspace(0.0,360,10)
+nlat = lat.size
+nlon = lon.size
+
+# vertical grid: for computation of initial state we convert the ifs2icon hybrid levels
+# to height levels assuming a globally-uniform surface pressure (defined above)
+# and defining ehight according to Polvani and Elsner as z = H ln (p0/p)
+p = hyam + hybm*p0
+z = H*np.log(p0/p) # np.log is natural logarithm
+nz = z.size
+#print(z, nz)
+
+# latitude in radians
+latrad = lat * np.pi/180.0 #lat2
+
+# lifecycle 1
+u1 = np.zeros((nz,nlat))+np.nan
+F = np.power(np.sin(np.pi*np.power(np.sin(latrad),2)),3); F[lat<0] = 0.0
+for i in range(0, nz):
+ for j in range(0, nlat):
+ u1[i,j] = u0*F[j]*(z[i]/zT)*np.exp(-0.5*(np.power(z[i]/zT,2)-1))
+#du1dz = u1*np.expand_dims(1/z-z/np.power(zT,2),axis=1)
+
+# compute temperature profile in zonal wind balance with u1
+t=np.zeros((nz, nlat)) + np.nan
+
+# latitude independe reference profile
+tr = np.zeros((nz, nlat)) + np.nan
+for i in range(0, nz):
+ tr[i, :] = t0 + Gamma0/np.power((np.power(zT,-alpha)+np.power(z[i],-alpha)),1/alpha)
+
+# latitude dependent modification
+tmp = np.zeros((nz, nlat)) + np.nan
+for i in range(0, nz):
+ for j in range(0, nlat):
+ tmp[i, j] =scipy.integrate.quad(Tintegrand, 0, latrad[j], args=(z[i], zT, u0, a, Omega))[0]
+t=tr-H/R*tmp
+
+#######################################################################
+# BEGINNING OF CALCULATIONS FOR TEMPERATURE AND ZONAL WIND ANOMALIES
+# read the temperature anomaly from CMIP6 models and derive the zonal
+# wind anomaly for this temperature anomaly based on thermal wind
+# balance
+
+# read the temperature anomaly
+# the path and filename are given in the bash script that calls
+# this python script and are read in here with "sys"
+ipath = sys.argv[1]
+ifile = sys.argv[2]
+da_tanom = xr.open_dataset(ipath + ifile).squeeze()['ta']
+del ipath, ifile
+
+# store values for tanom and latitude in numpy arrays
+tanom = da_tanom.values
+lat_tanom = da_tanom['lat'].values
+
+# convert pressure levels to height levels in m
+# as in Polvani & Esler 2007
+zlev_tanom = (H*np.log(p0/da_tanom['plev'])).values
+del da_tanom
+
+# Check if the temperature anomaly is nan for the first index (in latitude).
+# If true: set value at first index to value at second index
+if np.isnan(tanom[:,0]).all():
+ print('change column for first latitude of tanom')
+ tanom[:,0] = tanom[:,1]
+print("Nan's in tanom: " + str(np.isnan(tanom).any()))
+
+# set temperature anomalies southward of 20N to value at 20N
+# and northward of 80N to value at 80N to avoid sharp gradients
+tanom_alllat = np.full((zlev_tanom.size, lat.size), np.nan, dtype=float)
+
+# find indices, where lat agrees with the first and last entries of lat_tanom
+xlat = np.where(lat == lat_tanom[0])[0][0]
+ylat = np.where(lat == lat_tanom[-1])[0][0]
+
+tanom_alllat[:, xlat:ylat+1] = tanom
+for la in range(len(lat[:xlat])):
+ tanom_alllat[:, la] = tanom[:, 0]
+del la
+for la in range(len(lat[ylat+1:])):
+ tanom_alllat[:, la+ylat+1] = tanom[:, -1]
+del xlat, ylat
+del tanom, lat_tanom
+
+#######################################################################
+# read tropopause for initial conditions
+ipath = '/work/bb1152/Module_A/A6_CyclEx/b380490_Albern/input_data/' + \
+ 'lc1_initialcondition_tropopause/'
+ifile = 'lc1_initialcondition_CTL_tropopause_zm.nc'
+tropo_ini_plev = xr.open_dataset(ipath + ifile)
+del ipath, ifile
+
+# convert tropopause to height in m
+tropo_ini = (H*np.log(p0/tropo_ini_plev['ptrop'])).values
+del tropo_ini_plev
+
+#######################################################################
+# calculate zonal wind anomaly based on temperature anomaly
+uanom, tanom_smooth = get_u_from_t(tanom_alllat, tropo_ini, zlev_tanom,
+ z, lat, latrad)
+
+#######################################################################
+# add temperature and temperature anomaly
+t = t + tanom_smooth
+
+# add zonal wind and zonal wind anomaly
+u1 = u1 + uanom
+
+#######################################################################
+# delete auxiliary variables
+del zlev_tanom, tanom_alllat, tropo_ini, uanom, tanom_smooth
+
+# END OF CALCULATIONS FOR TEMPERATURE AND ZONAL WIND ANOMALY
+#######################################################################
+
+# potential temperature
+theta=t*np.expand_dims(np.exp(kappa*z/H),axis=1)
+
+# relative humidity
+# follows Booth et al. 2013, Climate Dynamics
+rh = np.zeros((nz, nlat)) + np.nan
+for i in range(0, nz):
+ rh[i, :] = rh0*np.power(1-0.85*z[i]/zTrh, 1.25)
+ if z[i]>14e3:
+ rh[i, :] = 0.0
+
+# specific humidty
+# follows calculation in icon
+# /icon-nwp-2.0.15/src/atm_phy_schemes/mo_satad.f90 -> sat_pres_water,spec_humi
+b1 = 610.78 # --> c1es in mo_convect_tables.f90
+b2w= 17.269 # --> c3les
+b3 = 273.15 # --> tmelt; melting temperature in K
+b4w= 35.86 # --> c4les
+sat_pres_water = b1*np.exp(b2w*(t-b3)/(t-b4w))
+Rdv = 287.04/461.51 # Rd/Rv
+o_m_Rdv = 1-Rdv # 1-Rd/Rv
+print(Rdv, o_m_Rdv)
+qv = np.zeros((nz, nlat))
+for i in range(0, nz):
+ for j in range(0, nlat):
+ qv[i, j] = rh[i,j]*Rdv*sat_pres_water[i,j]/(p[i]-o_m_Rdv*sat_pres_water[i,j])
+
+#---------------------------------------
+# save to netcdf files
+#---------------------------------------
+ncfile = nc.Dataset('lc1_initialcondition_Tanom_r10x360.nc', 'w', clobber=True, format='NETCDF3_CLASSIC')
+ncfile.description = 'Initial condition for LC1, for U and T according to Polvani and Esler 2007 JGR'
+
+# dimensions
+ncfile.createDimension('lat', nlat)
+ncfile.createDimension('lon', nlon)
+ncfile.createDimension('lev', nz)
+ncfile.createDimension('lev_2', 1)
+ncfile.createDimension('nhym', nz)
+ncfile.createDimension('nhyi', nz+1)
+ncfile.createDimension('time', 1)
+
+# variables
+nc_latitude = ncfile.createVariable('lat' , 'f8', ('lat',))
+nc_longitude = ncfile.createVariable('lon' , 'f8', ('lon',))
+nc_lev = ncfile.createVariable('lev' , 'f8', ('lev',))
+nc_lev_2 = ncfile.createVariable('lev_2' , 'f8', ('lev_2',))
+nc_hyam = ncfile.createVariable('hyam' , 'f8', ('nhym',))
+nc_hybm = ncfile.createVariable('hybm' , 'f8', ('nhym',))
+nc_hyai = ncfile.createVariable('hyai' , 'f8', ('nhyi',))
+nc_hybi = ncfile.createVariable('hybi' , 'f8', ('nhyi',))
+nc_time = ncfile.createVariable('time' , 'f8', ('time',))
+nc_T = ncfile.createVariable('T' , 'f4', ('time', 'lev', 'lat', 'lon'))
+nc_U = ncfile.createVariable('U' , 'f4', ('time', 'lev', 'lat', 'lon'))
+nc_V = ncfile.createVariable('V' , 'f4', ('time', 'lev', 'lat', 'lon'))
+nc_W = ncfile.createVariable('W' , 'f4', ('time', 'lev', 'lat', 'lon'))
+nc_QV = ncfile.createVariable('QV' , 'f4', ('time', 'lev', 'lat', 'lon'))
+nc_QC = ncfile.createVariable('QC' , 'f4', ('time', 'lev', 'lat', 'lon'))
+nc_QI = ncfile.createVariable('QI' , 'f4', ('time', 'lev', 'lat', 'lon'))
+nc_SST = ncfile.createVariable('SST' , 'f4', ('time', 'lat', 'lon'))
+nc_LNPS = ncfile.createVariable('LNPS' , 'f4', ('time', 'lev_2', 'lat', 'lon'))
+nc_GEOP_SFC = ncfile.createVariable('GEOP_SFC', 'f4', ('time', 'lev_2', 'lat', 'lon'))
+nc_GEOP_ML = ncfile.createVariable('GEOP_ML' , 'f4', ('time', 'lev_2', 'lat', 'lon'))
+
+# set horizontal grid data
+nc_latitude[:] = lat
+nc_longitude[:] = lon
+
+# set vertical grid data
+nc_lev[:] = lev
+nc_lev.standard_name = "hybrid_sigma_pressure"
+nc_lev.long_name = "hybrid level at layer midpoints"
+nc_lev.formula = "hyam hybm (mlev=hyam+hybm*aps)"
+nc_lev.formula_terms = "ap: hyam b: hybm ps: aps"
+nc_lev.units = "level"
+nc_lev.positive = "down"
+nc_lev_2[:] = lev_2
+nc_lev_2.standard_name = "hybrid_sigma_pressure"
+nc_lev_2.long_name = "hybrid level at layer midpoints"
+nc_lev_2.formula = "hyam hybm (mlev=hyam+hybm*aps)"
+nc_lev_2.formula_terms = "ap: hyam b: hybm ps: aps"
+nc_lev_2.units = "level"
+nc_lev_2.positive = "down"
+nc_hyam[:] = hyam
+nc_hyam.long_name = "hybrid A coefficient at layer midpoints"
+nc_hyam.units = "Pa"
+nc_hybm[:] = hybm
+nc_hybm.long_name = "hybrid B coefficient at layer midpoints"
+nc_hybm.units = "1"
+nc_hyai[:] = hyai
+nc_hyai.long_name = "hybrid A coefficient at layer interfaces"
+nc_hyai.units = "Pa"
+nc_hybi[:] = hybi
+nc_hybi.long_name = "hybrid B coefficient at layer interfaces"
+nc_hybi.units = "1"
+
+# set time data
+nc_time[0] = 23790716.25 # this is the time from ifs2icon_0010_R02B04_aquaplanet.nc
+nc_time.standard_name = "time"
+nc_time.units = "day as %Y%m%d.%f"
+nc_time.calendar = "proleptic_gregorian"
+nc_time.axis = "T"
+
+# set variable data
+for i in range(0, nlon):
+ nc_T[:,:,:,i] = t
+ nc_U[:,:,:,i] = u1
+ nc_V[:,:,:,i] = 0.0
+ nc_W[:,:,:,i] = 0.0
+ nc_QV[:,:,:,i] = qv
+ nc_QC[:,:,:,i] = 0.0
+ nc_QI[:,:,:,i] = 0.0
+ nc_SST[:,:,i] = t[nz-1,:] - 0.5 # as in Booth et al., 2013, Clim. Dynamics we set the SST to lowest-level initial T-0.5K
+ nc_LNPS[:,:,:,i] = np.log(p0)
+ nc_GEOP_SFC[:,:,:,i] = 0.0
+ nc_GEOP_ML[:,:,:,i] = g*z[nz-1]
+
+# set variable attributes
+nc_T.units = "K"; nc_T.standard_name = "temperature"; nc_T.long_name = "Atmospheric temperature"
+nc_U.units = "m s**-1"; nc_U.standard_name = "u-wind"; nc_U.long_name = "Zonal wind"
+nc_V.units = "m s**-1"; nc_V.standard_name = "v-wind"; nc_V.long_name = "Meridional wind"
+nc_W.units = "Pa s**-1"; nc_W.standard_name = "lagrangian_tendency_of_air_pressure"; nc_W.long_name = "Vertical Velocity (Pressure) (omega=dp/dt)"
+nc_QV.units = "kg kg**-1"; nc_QV.standard_name = "spec. humidity"; nc_QV.long_name = "Specific humidity"
+nc_QC.units = "kg kg**-1"; nc_QC.standard_name = "cloud liq. water"; nc_QC.long_name = "Specific cloud liquid water content"
+nc_QI.units = "kg kg**-1"; nc_QI.standard_name = "cloud ice"; nc_QI.long_name = "Specific cloud ice water content"
+nc_GEOP_ML.units = "m**2 s**-2"; nc_GEOP_ML.standard_name = "geopotential"; nc_GEOP_ML.long_name = "Geopotential"
+nc_SST.units = "K"; nc_SST.standard_name = "sst"; nc_SST.long_name = "Sea-surface temperature"
+nc_LNPS.units = "n/a"; nc_LNPS.standard_name = "ln sfc pressure"; nc_LNPS.long_name = "Logarithm of surface pressure"
+nc_GEOP_SFC.units = "m**2 s**-2"; nc_GEOP_SFC.standard_name = "sfc geopotential"; nc_GEOP_SFC.long_name = "Sfc Geopotential"
+
+ncfile.close()
diff --git a/Scripts_for_preprocessing/generate_initialdata/lc1_initial_condition_fixedCoriolisParameter_Tanom_TR_PO.py b/Scripts_for_preprocessing/generate_initialdata/lc1_initial_condition_fixedCoriolisParameter_Tanom_TR_PO.py
new file mode 100644
index 0000000000000000000000000000000000000000..5ff4b73c6353efb3c0c2d9888df5783609f02ed7
--- /dev/null
+++ b/Scripts_for_preprocessing/generate_initialdata/lc1_initial_condition_fixedCoriolisParameter_Tanom_TR_PO.py
@@ -0,0 +1,512 @@
+import numpy as np
+import scipy.integrate
+import matplotlib.pyplot as plt
+import netCDF4 as nc
+from numba import jit
+import xarray as xr # for temperature anomalies
+import sys # for temperature anomalies
+
+@jit
+def Tintegrand(latrad, z, zT, U0, a, Omega):
+ f = 2*Omega*np.sin(np.deg2rad(45.0))
+ F = np.power(np.sin(np.pi*np.power(np.sin(latrad),2)),3)
+ if latrad<0: F=0.0
+ u1 = U0*F*(z/zT)*np.exp(-0.5*(np.power(z/zT,2)-1))
+ du1dz = u1*(1/z-z/np.power(zT,2))
+ return (a*f+2*u1*np.tan(latrad))*du1dz
+
+def load_levelinfo():
+ from netCDF4 import Dataset
+ file = Dataset('./ifs2icon_verticalgridinfo_137levels.nc', 'r')
+ hyam = np.squeeze(np.array(file.variables['hyam']))
+ hybm = np.squeeze(np.array(file.variables['hybm']))
+ hyai = np.squeeze(np.array(file.variables['hyai']))
+ hybi = np.squeeze(np.array(file.variables['hybi']))
+ lev = np.squeeze(np.array(file.variables['lev' ]))
+ lev_2= np.squeeze(np.array(file.variables['lev_2']))
+ return hyam, hybm, hyai, hybi, lev, lev_2
+
+hyam, hybm, hyai, hybi, lev, lev_2 = load_levelinfo()
+
+# function to calculate the meridional gradient of variable "var"
+def get_dxdlat(var, lats, levs):
+ nlat = lats.size # length of latitude vector
+ # calculate distance between entries of latitude vector
+ dlat = np.full(lats.shape, np.nan, dtype=float)
+ dlat[0] = np.abs(lats[1] - lats[0])
+ # distance between lat[2]-lat[0], lat[3]-lat[1], etc.
+ dlat[1:nlat-1] = np.abs(lats[2:nlat] - lats[0:nlat-2])
+ dlat[nlat-1] = np.abs(lats[nlat-1] - lats[nlat-2])
+
+ # check that first dimension is levs and second dimension is lats
+ if (var.shape[0] == levs.size) and (var.shape[1] == lats.size):
+ # check that latitudes go from south to north
+ if lats[0] > lats[1]:
+ var = var[:, ::-1]
+ lats = lats[::-1]
+ print('changed lats and var')
+
+ # centered finite differences
+ var_grad = np.full(var.shape, np.nan, dtype=float)
+ var_grad[:, 0] = (var[:, 1] - var[:, 0]) / dlat[0]
+ for la in range(1, nlat-1):
+ var_grad[:, la] = (var[:, la+1] - var[:, la-1]) / dlat[la]
+ del la
+ var_grad[:, nlat-1] = (var[:, nlat-1] - var[:, nlat-2]) / dlat[nlat-1]
+ else:
+ print('ERROR: Dimensions are not lat and lev. ' + \
+ 'Exit function get_dxdlat')
+ return
+
+ return var_grad
+
+# function to calculate the zonal wind from atm. temperature following thermal
+# wind balance
+# only the tropical or polar temperature anomaly is used to derive the zonal
+# wind anomaly
+def get_u_from_t_trpo(tanom, tropo, zin, zout, lat, latrad, trpo):
+# input: tanom: atmospheric temperature anomaly, dimensions (zin-lat)
+# tropo: tropopause height in m
+# zin: vertical levels of temperature field in m
+# zout: vertical levels of zonal wind field in m
+# lat: latitudes of temperature field in degree
+# latrad: latitudes in radian
+# trpo: use tropical or polar temperature anomaly
+
+ # 1. prepare the temperature field
+ # 1.1 We want to derive the zonal wind from the surface to TOA.
+ # If levels do not go from surface to TOA, change them.
+ if zin[0] > zin[1]:
+ print('change order of zin to go from surface to TOA')
+ tanom_calc = tanom[::-1, :]
+ zin_calc = zin[::-1]
+ else:
+ tanom_calc = tanom.copy()
+ zin_calc = zin.copy()
+ del tanom, zin
+
+ # 1.2 interpolate the vertical grid to a finer resolution
+ # (10m distance between levels), use height levels zout that
+ # come from the initial data
+ if zout[0] > zout[1]:
+ print('zout from TOA to surface')
+ zin_int = np.arange(zout[-1], zout[0], 10)
+ else:
+ print('zout from surface to TOA')
+ zin_int = np.arange(zout[0], zout[-1], 10)
+ tanom_int = np.full((zin_int.size, lat.size), np.nan, dtype=float)
+ for la in range(lat.size): # loop over latitudes
+ tanom_int[:, la] = np.interp(zin_int, zin_calc, tanom_calc[:,la])
+ del la
+ del tanom_calc, zin_calc
+
+ # 1.3 set temperature anomalies in stratosphere to 0 and smooth the temperature
+ # field around the tropopause to avoid sharp gradients
+ mask = np.full(tanom_int.shape, np.nan, dtype=bool)
+ for la in range(len(lat)): # loop over latitudes
+ mask[:,la] = zin_int < tropo[la]
+ del la
+ # smooth data around the tropopause
+ tanom_mask = tanom_int * mask
+ for la in range(len(lat)): # loop over latitudes
+ # index of highest level below tropopause
+ ind = np.where(mask[:,la])[0][-1]
+ # smooth data for 25 levels below and 24 levels above tropopause
+ x = np.linspace(tanom_int[ind-25, la], 0, 50)
+ tanom_mask[ind-25:ind+25, la] = x
+ del ind, x
+ del la
+
+ del tanom_int, mask, tropo
+
+ # 1.4 smooth the temperature profile
+ window = np.ones(10)/10
+ tanom_mask_runmean = np.full(tanom_mask.shape, np.nan, dtype=float)
+ for le in range(len(zin_int)):
+ tanom_mask_runmean[le, :] = np.convolve(tanom_mask[le, :], window, 'same')
+ del le, window
+
+ del tanom_mask
+
+ # the running mean introduces strong gradients at the North pole
+ # -> set temperature values poleward of 85N to the value at 85N
+ xlat = (np.abs(lat-85)).argmin()
+ for la in range(xlat+1, len(lat)):
+ tanom_mask_runmean[:, la] = tanom_mask_runmean[:, xlat]
+ del la
+ del xlat
+
+ # 1.5 apply mask to get tropical or polar temperature anomalies
+ latsout = (np.abs(lat - 40)).argmin()
+ latnort = (np.abs(lat - 50)).argmin()+2
+ print(lat[latsout:latnort])
+ mask_trpo = np.full(lat.shape, np.nan, dtype=float)
+ if trpo == 'tropical':
+ trop_lin = np.linspace(1, 0, 22)
+ mask_trpo[:latsout] = 1
+ mask_trpo[latsout:latnort] = trop_lin
+ mask_trpo[latnort:] = 0
+ del trop_lin
+ elif trpo == 'polar':
+ polar_lin = np.linspace(0, 1, 22)
+ mask_trpo[:latsout] = 0
+ mask_trpo[latsout:latnort] = polar_lin
+ mask_trpo[latnort:] = 1
+ del polar_lin
+ else:
+ print('trpo must be "tropical" or "polar"')
+ mask_trpo = 1
+ del latsout, latnort
+
+ # apply mask to tanom_mask_runmean
+ tanom_mask_runmean = tanom_mask_runmean * mask_trpo
+ del mask_trpo
+
+ ###################################################################
+ # 2. calculate the zonal wind field
+ # 2.1 helper variables
+ f = 2*Omega*np.sin(np.deg2rad(45.0))
+ var_a = -1*H*a*f/R
+ var_b = -2*H*np.tan(latrad)/R
+
+ # 2.2 meridional temperature gradient
+ dTdlat = get_dxdlat(tanom_mask_runmean, latrad, zin_int)
+
+ # smooth the meridional temperature gradient
+ window = np.ones(20)/20
+ dTdlat_runmean = np.full(dTdlat.shape, np.nan, dtype=float)
+ for le in range(len(zin_int)):
+ dTdlat_runmean[le, :] = np.convolve(dTdlat[le, :], window, 'same')
+ del le, window
+ del dTdlat
+
+ # 2.3 difference between levels
+ # array has one entry less than z: first entry is
+ # the difference between the first and second level
+ dz = np.diff(zin_int)
+
+ # 2.4 calculate zonal wind
+ uanom_int = np.full(tanom_mask_runmean.shape, np.nan, dtype=float)
+
+ # no wind at the surface
+ uanom_int[0, :] = 0.0
+
+ # other levels
+ for le in range(1, len(zin_int)): # loop over levels
+ uanom_int[le, :] = (dTdlat_runmean[le-1,:] + \
+ var_a * uanom_int[le-1,:] / dz[le-1] + \
+ var_b * uanom_int[le-1,:] * uanom_int[le-1,:] / dz[le-1]) * \
+ dz[le-1] / (var_a + var_b * uanom_int[le-1,:])
+ del le
+ del f, var_a, var_b, dTdlat_runmean, dz
+
+ # 2.5 interpolate the calculated zonal wind and the temperature anomaly
+ # to the zout vertical grid
+ uanom = np.full((zout.size, lat.size), np.nan, dtype=float)
+ tanom = np.full((zout.size, lat.size), np.nan, dtype=float)
+
+ # levels must be monotonically increasing:
+ if zout[0] > zout[1]:
+ print('zout is not monotonically increasing. Use zout[::-1].')
+ for la in range(lat.size):
+ uanom[:, la] = np.interp(zout[::-1], zin_int, uanom_int[:, la])
+ tanom[:, la] = np.interp(zout[::-1], zin_int, tanom_mask_runmean[:, la])
+ del la
+ del zin_int, uanom_int, tanom_mask_runmean
+
+ # first level should be TOA again
+ tanom = tanom[::-1, :]
+ uanom = uanom[::-1, :]
+ else:
+ print('zout is monotonically increasing. Use z.')
+ for la in range(lat.size):
+ uanom[:, la] = np.interp(zout, zin_int, uanom_int[:, la])
+ tanom[:, la] = np.interp(zout, zin_int, tanom_mask_runmean[:, la])
+ del la
+ del zin_int, uanom_int, tanom_mask_runmean
+
+ # first level should be TOA again
+ tanom = tanom[::-1, :]
+ uanom = uanom[::-1, :]
+
+ return uanom, tanom
+
+# constants
+# physical constants are set to the values used in icon-nwp-2.0.15/src/shared/mo_physical_constants.f90
+u0 = 45.0 # in m/s
+zT = 13.0e3 # in m
+H = 7.5e3 # in m
+R = 287.04 # dry gas constant in J/(kg K) (parameter rd in ICON)
+a = 6.371229e6 # average Earth radius in m (parameter earth_radius in ICON)
+Omega = 7.29212e-5 # angular velocity in 1/s (parameter earth_angular_velocity in ICON)
+t0 = 300 # in K
+Gamma0 = -6.5e-3 # in K/m
+alpha = 10 # unitless
+kappa = 2.0/7.0 # unitless
+g = 9.80665 # av. gravitational acceleration in m/s2 (parameter grav in ICON)
+p0 = 1.0e5 # globally-uniform surface pressure in Pa
+# for relative humidity following Booth et al., 2013 Climate Dynamics
+zTrh = 12.0e3
+rh0 = 0.80 # relative humidity scaling factor from 0..1
+
+#latitude-longitude grid
+lat = np.arange(-89.75, 90, 0.5) #np.linspace(-90, 90, 360)
+lon = np.linspace(0.0,360,10)
+nlat = lat.size
+nlon = lon.size
+
+# vertical grid: for computation of initial state we convert the ifs2icon hybrid levels
+# to height levels assuming a globally-uniform surface pressure (defined above)
+# and defining ehight according to Polvani and Elsner as z = H ln (p0/p)
+p = hyam + hybm*p0
+z = H*np.log(p0/p) # np.log is natural logarithm
+nz = z.size
+#print(z, nz)
+
+# latitude in radians
+latrad = lat * np.pi/180.0 #lat2
+
+# lifecycle 1
+u1 = np.zeros((nz,nlat))+np.nan
+F = np.power(np.sin(np.pi*np.power(np.sin(latrad),2)),3); F[lat<0] = 0.0
+for i in range(0, nz):
+ for j in range(0, nlat):
+ u1[i,j] = u0*F[j]*(z[i]/zT)*np.exp(-0.5*(np.power(z[i]/zT,2)-1))
+#du1dz = u1*np.expand_dims(1/z-z/np.power(zT,2),axis=1)
+
+# compute temperature profile in zonal wind balance with u1
+t=np.zeros((nz, nlat)) + np.nan
+
+# latitude independe reference profile
+tr = np.zeros((nz, nlat)) + np.nan
+for i in range(0, nz):
+ tr[i, :] = t0 + Gamma0/np.power((np.power(zT,-alpha)+np.power(z[i],-alpha)),1/alpha)
+
+# latitude dependent modification
+tmp = np.zeros((nz, nlat)) + np.nan
+for i in range(0, nz):
+ for j in range(0, nlat):
+ tmp[i, j] =scipy.integrate.quad(Tintegrand, 0, latrad[j], args=(z[i], zT, u0, a, Omega))[0]
+t=tr-H/R*tmp
+
+#######################################################################
+# BEGINNING OF CALCULATIONS FOR TEMPERATURE AND ZONAL WIND ANOMALIES
+# read the temperature anomaly from CMIP6 models and derive the zonal
+# wind anomaly for this temperature anomaly based on thermal wind
+# balance
+
+# read the temperature anomaly
+# the path and filename are given in the bash script that calls
+# this python script and are read in here with "sys"
+ipath = sys.argv[1]
+ifile = sys.argv[2]
+da_tanom = xr.open_dataset(ipath + ifile).squeeze()['ta']
+del ipath, ifile
+
+# store values for tanom and latitude in numpy arrays
+tanom = da_tanom.values
+lat_tanom = da_tanom['lat'].values
+
+# convert pressure levels to height levels in m
+# as in Polvani & Esler 2007
+zlev_tanom = (H*np.log(p0/da_tanom['plev'])).values
+del da_tanom
+
+# Check if the temperature anomaly is nan for the first index (in latitude).
+# If true: set value at first index to value at second index
+if np.isnan(tanom[:,0]).all():
+ print('change column for first latitude of tanom')
+ tanom[:,0] = tanom[:,1]
+print("Nan's in tanom: " + str(np.isnan(tanom).any()))
+
+# set temperature anomalies southward of 20N to value at 20N
+# and northward of 80N to value at 80N to avoid sharp gradients
+tanom_alllat = np.full((zlev_tanom.size, lat.size), np.nan, dtype=float)
+
+# find indices, where lat agrees with the first and last entries of lat_tanom
+xlat = np.where(lat == lat_tanom[0])[0][0]
+ylat = np.where(lat == lat_tanom[-1])[0][0]
+
+tanom_alllat[:, xlat:ylat+1] = tanom
+for la in range(len(lat[:xlat])):
+ tanom_alllat[:, la] = tanom[:, 0]
+del la
+for la in range(len(lat[ylat+1:])):
+ tanom_alllat[:, la+ylat+1] = tanom[:, -1]
+del xlat, ylat
+del tanom, lat_tanom
+
+#######################################################################
+# read tropopause for initial conditions
+ipath = '/work/bb1152/Module_A/A6_CyclEx/b380490_Albern/input_data/' + \
+ 'lc1_initialcondition_tropopause/'
+ifile = 'lc1_initialcondition_CTL_tropopause_zm.nc'
+tropo_ini_plev = xr.open_dataset(ipath + ifile)
+del ipath, ifile
+
+# convert tropopause to height in m
+tropo_ini = (H*np.log(p0/tropo_ini_plev['ptrop'])).values
+del tropo_ini_plev
+
+#######################################################################
+# calculate zonal wind anomaly based on temperature anomaly
+
+# decide if tropical or polar temperature anomaly is used
+# as the path and filename for the temperature anomaly, this variable
+# is given in the bash script that calls this python script and is
+# read in here with "sys"
+trpo = sys.argv[3]
+print('type of trpo: ', type(trpo))
+
+uanom, tanom_smooth = get_u_from_t_trpo(tanom_alllat, tropo_ini,
+ zlev_tanom, z, lat, latrad,
+ trpo)
+
+#######################################################################
+# add temperature and temperature anomaly
+t = t + tanom_smooth
+
+# add zonal wind and zonal wind anomaly
+u1 = u1 + uanom
+
+#######################################################################
+# delete auxiliary variables
+del zlev_tanom, tanom_alllat, tropo_ini, uanom, tanom_smooth
+
+# END OF CALCULATIONS FOR TEMPERATURE AND ZONAL WIND ANOMALY
+#######################################################################
+
+# potential temperature
+theta=t*np.expand_dims(np.exp(kappa*z/H),axis=1)
+
+# relative humidity
+# follows Booth et al. 2013, Climate Dynamics
+rh = np.zeros((nz, nlat)) + np.nan
+for i in range(0, nz):
+ rh[i, :] = rh0*np.power(1-0.85*z[i]/zTrh, 1.25)
+ if z[i]>14e3:
+ rh[i, :] = 0.0
+
+# specific humidty
+# follows calculation in icon
+# /icon-nwp-2.0.15/src/atm_phy_schemes/mo_satad.f90 -> sat_pres_water,spec_humi
+b1 = 610.78 # --> c1es in mo_convect_tables.f90
+b2w= 17.269 # --> c3les
+b3 = 273.15 # --> tmelt; melting temperature in K
+b4w= 35.86 # --> c4les
+sat_pres_water = b1*np.exp(b2w*(t-b3)/(t-b4w))
+Rdv = 287.04/461.51 # Rd/Rv
+o_m_Rdv = 1-Rdv # 1-Rd/Rv
+print(Rdv, o_m_Rdv)
+qv = np.zeros((nz, nlat))
+for i in range(0, nz):
+ for j in range(0, nlat):
+ qv[i, j] = rh[i,j]*Rdv*sat_pres_water[i,j]/(p[i]-o_m_Rdv*sat_pres_water[i,j])
+
+#---------------------------------------
+# save to netcdf files
+#---------------------------------------
+if trpo == 'tropical':
+ ncfile = nc.Dataset('lc1_initialcondition_Tanom_tropics_r10x360.nc', 'w', clobber=True, format='NETCDF3_CLASSIC')
+elif trpo == 'polar':
+ ncfile = nc.Dataset('lc1_initialcondition_Tanom_polar_r10x360.nc', 'w', clobber=True, format='NETCDF3_CLASSIC')
+ncfile.description = 'Initial condition for LC1, for U and T according to Polvani and Esler 2007 JGR'
+
+# dimensions
+ncfile.createDimension('lat', nlat)
+ncfile.createDimension('lon', nlon)
+ncfile.createDimension('lev', nz)
+ncfile.createDimension('lev_2', 1)
+ncfile.createDimension('nhym', nz)
+ncfile.createDimension('nhyi', nz+1)
+ncfile.createDimension('time', 1)
+
+# variables
+nc_latitude = ncfile.createVariable('lat' , 'f8', ('lat',))
+nc_longitude = ncfile.createVariable('lon' , 'f8', ('lon',))
+nc_lev = ncfile.createVariable('lev' , 'f8', ('lev',))
+nc_lev_2 = ncfile.createVariable('lev_2' , 'f8', ('lev_2',))
+nc_hyam = ncfile.createVariable('hyam' , 'f8', ('nhym',))
+nc_hybm = ncfile.createVariable('hybm' , 'f8', ('nhym',))
+nc_hyai = ncfile.createVariable('hyai' , 'f8', ('nhyi',))
+nc_hybi = ncfile.createVariable('hybi' , 'f8', ('nhyi',))
+nc_time = ncfile.createVariable('time' , 'f8', ('time',))
+nc_T = ncfile.createVariable('T' , 'f4', ('time', 'lev', 'lat', 'lon'))
+nc_U = ncfile.createVariable('U' , 'f4', ('time', 'lev', 'lat', 'lon'))
+nc_V = ncfile.createVariable('V' , 'f4', ('time', 'lev', 'lat', 'lon'))
+nc_W = ncfile.createVariable('W' , 'f4', ('time', 'lev', 'lat', 'lon'))
+nc_QV = ncfile.createVariable('QV' , 'f4', ('time', 'lev', 'lat', 'lon'))
+nc_QC = ncfile.createVariable('QC' , 'f4', ('time', 'lev', 'lat', 'lon'))
+nc_QI = ncfile.createVariable('QI' , 'f4', ('time', 'lev', 'lat', 'lon'))
+nc_SST = ncfile.createVariable('SST' , 'f4', ('time', 'lat', 'lon'))
+nc_LNPS = ncfile.createVariable('LNPS' , 'f4', ('time', 'lev_2', 'lat', 'lon'))
+nc_GEOP_SFC = ncfile.createVariable('GEOP_SFC', 'f4', ('time', 'lev_2', 'lat', 'lon'))
+nc_GEOP_ML = ncfile.createVariable('GEOP_ML' , 'f4', ('time', 'lev_2', 'lat', 'lon'))
+
+# set horizontal grid data
+nc_latitude[:] = lat
+nc_longitude[:] = lon
+
+# set vertical grid data
+nc_lev[:] = lev
+nc_lev.standard_name = "hybrid_sigma_pressure"
+nc_lev.long_name = "hybrid level at layer midpoints"
+nc_lev.formula = "hyam hybm (mlev=hyam+hybm*aps)"
+nc_lev.formula_terms = "ap: hyam b: hybm ps: aps"
+nc_lev.units = "level"
+nc_lev.positive = "down"
+nc_lev_2[:] = lev_2
+nc_lev_2.standard_name = "hybrid_sigma_pressure"
+nc_lev_2.long_name = "hybrid level at layer midpoints"
+nc_lev_2.formula = "hyam hybm (mlev=hyam+hybm*aps)"
+nc_lev_2.formula_terms = "ap: hyam b: hybm ps: aps"
+nc_lev_2.units = "level"
+nc_lev_2.positive = "down"
+nc_hyam[:] = hyam
+nc_hyam.long_name = "hybrid A coefficient at layer midpoints"
+nc_hyam.units = "Pa"
+nc_hybm[:] = hybm
+nc_hybm.long_name = "hybrid B coefficient at layer midpoints"
+nc_hybm.units = "1"
+nc_hyai[:] = hyai
+nc_hyai.long_name = "hybrid A coefficient at layer interfaces"
+nc_hyai.units = "Pa"
+nc_hybi[:] = hybi
+nc_hybi.long_name = "hybrid B coefficient at layer interfaces"
+nc_hybi.units = "1"
+
+# set time data
+nc_time[0] = 23790716.25 # this is the time from ifs2icon_0010_R02B04_aquaplanet.nc
+nc_time.standard_name = "time"
+nc_time.units = "day as %Y%m%d.%f"
+nc_time.calendar = "proleptic_gregorian"
+nc_time.axis = "T"
+
+# set variable data
+for i in range(0, nlon):
+ nc_T[:,:,:,i] = t
+ nc_U[:,:,:,i] = u1
+ nc_V[:,:,:,i] = 0.0
+ nc_W[:,:,:,i] = 0.0
+ nc_QV[:,:,:,i] = qv
+ nc_QC[:,:,:,i] = 0.0
+ nc_QI[:,:,:,i] = 0.0
+ nc_SST[:,:,i] = t[nz-1,:] - 0.5 # as in Booth et al., 2013, Clim. Dynamics we set the SST to lowest-level initial T-0.5K
+ nc_LNPS[:,:,:,i] = np.log(p0)
+ nc_GEOP_SFC[:,:,:,i] = 0.0
+ nc_GEOP_ML[:,:,:,i] = g*z[nz-1]
+
+# set variable attributes
+nc_T.units = "K"; nc_T.standard_name = "temperature"; nc_T.long_name = "Atmospheric temperature"
+nc_U.units = "m s**-1"; nc_U.standard_name = "u-wind"; nc_U.long_name = "Zonal wind"
+nc_V.units = "m s**-1"; nc_V.standard_name = "v-wind"; nc_V.long_name = "Meridional wind"
+nc_W.units = "Pa s**-1"; nc_W.standard_name = "lagrangian_tendency_of_air_pressure"; nc_W.long_name = "Vertical Velocity (Pressure) (omega=dp/dt)"
+nc_QV.units = "kg kg**-1"; nc_QV.standard_name = "spec. humidity"; nc_QV.long_name = "Specific humidity"
+nc_QC.units = "kg kg**-1"; nc_QC.standard_name = "cloud liq. water"; nc_QC.long_name = "Specific cloud liquid water content"
+nc_QI.units = "kg kg**-1"; nc_QI.standard_name = "cloud ice"; nc_QI.long_name = "Specific cloud ice water content"
+nc_GEOP_ML.units = "m**2 s**-2"; nc_GEOP_ML.standard_name = "geopotential"; nc_GEOP_ML.long_name = "Geopotential"
+nc_SST.units = "K"; nc_SST.standard_name = "sst"; nc_SST.long_name = "Sea-surface temperature"
+nc_LNPS.units = "n/a"; nc_LNPS.standard_name = "ln sfc pressure"; nc_LNPS.long_name = "Logarithm of surface pressure"
+nc_GEOP_SFC.units = "m**2 s**-2"; nc_GEOP_SFC.standard_name = "sfc geopotential"; nc_GEOP_SFC.long_name = "Sfc Geopotential"
+
+ncfile.close()
diff --git a/Scripts_for_preprocessing/generate_initialdata/lc1_initial_condition_fixedCoriolisParameter_for_tropopause.py b/Scripts_for_preprocessing/generate_initialdata/lc1_initial_condition_fixedCoriolisParameter_for_tropopause.py
new file mode 100644
index 0000000000000000000000000000000000000000..7766ba953031433c2d6725ee5037126097c3868a
--- /dev/null
+++ b/Scripts_for_preprocessing/generate_initialdata/lc1_initial_condition_fixedCoriolisParameter_for_tropopause.py
@@ -0,0 +1,221 @@
+import numpy as np
+import scipy.integrate
+import matplotlib.pyplot as plt
+import netCDF4 as nc
+from numba import jit
+
+@jit
+def Tintegrand(latrad, z, zT, U0, a, Omega):
+ f = 2*Omega*np.sin(np.deg2rad(45.0))
+ F = np.power(np.sin(np.pi*np.power(np.sin(latrad),2)),3)
+ if latrad<0: F=0.0
+ u1 = U0*F*(z/zT)*np.exp(-0.5*(np.power(z/zT,2)-1))
+ du1dz = u1*(1/z-z/np.power(zT,2))
+ return (a*f+2*u1*np.tan(latrad))*du1dz
+
+def load_levelinfo():
+ from netCDF4 import Dataset
+ file = Dataset('../ifs2icon_verticalgridinfo_137levels.nc', 'r')
+ hyam = np.squeeze(np.array(file.variables['hyam']))
+ hybm = np.squeeze(np.array(file.variables['hybm']))
+ hyai = np.squeeze(np.array(file.variables['hyai']))
+ hybi = np.squeeze(np.array(file.variables['hybi']))
+ lev = np.squeeze(np.array(file.variables['lev' ]))
+ lev_2= np.squeeze(np.array(file.variables['lev_2']))
+ return hyam, hybm, hyai, hybi, lev, lev_2
+
+hyam, hybm, hyai, hybi, lev, lev_2 = load_levelinfo()
+
+# constants
+# physical constants are set to the values used in icon-nwp-2.0.15/src/shared/mo_physical_constants.f90
+u0 = 45.0 # in m/s
+zT = 13.0e3 # in m
+H = 7.5e3 # in m
+R = 287.04 # dry gas constant in J/(kg K) (parameter rd in ICON)
+a = 6.371229e6 # average Earth radius in m (parameter earth_radius in ICON)
+Omega = 7.29212e-5 # angular velocity in 1/s (parameter earth_angular_velocity in ICON)
+t0 = 300 # in K
+Gamma0 = -6.5e-3 # in K/m
+alpha = 10 # unitless
+kappa = 2.0/7.0 # unitless
+g = 9.80665 # av. gravitational acceleration in m/s2 (parameter grav in ICON)
+p0 = 1.0e5 # globally-uniform surface pressure in Pa
+# for relative humidity following Booth et al., 2013 Climate Dynamics
+zTrh = 12.0e3
+rh0 = 0.80 # relative humidity scaling factor from 0..1
+
+#latitude-longitude grid
+lat = np.linspace(-90, 90, 360)
+lon = np.linspace(0.0,360,10)
+nlat = lat.size
+nlon = lon.size
+
+# vertical grid: for computation of initial state we convert the ifs2icon hybrid levels
+# to height levels assuming a globally-uniform surface pressure (defined above)
+# and defining ehight according to Polvani and Elsner as z = H ln (p0/p)
+p = hyam + hybm*p0
+z = H*np.log(p0/p) # np.log is natural logarithm
+nz = z.size
+#print(z, nz)
+
+# latitude in radians
+latrad = lat * np.pi/180.0 #lat2
+
+# lifecycle 1
+u1 = np.zeros((nz,nlat))+np.nan
+F = np.power(np.sin(np.pi*np.power(np.sin(latrad),2)),3); F[lat<0] = 0.0
+for i in range(0, nz):
+ for j in range(0, nlat):
+ u1[i,j] = u0*F[j]*(z[i]/zT)*np.exp(-0.5*(np.power(z[i]/zT,2)-1))
+#du1dz = u1*np.expand_dims(1/z-z/np.power(zT,2),axis=1)
+
+# compute temperature profile in zonal wind balance with u1
+t=np.zeros((nz, nlat)) + np.nan
+
+# latitude independe reference profile
+tr = np.zeros((nz, nlat)) + np.nan
+for i in range(0, nz):
+ tr[i, :] = t0 + Gamma0/np.power((np.power(zT,-alpha)+np.power(z[i],-alpha)),1/alpha)
+
+# latitude dependent modification
+tmp = np.zeros((nz, nlat)) + np.nan
+for i in range(0, nz):
+ for j in range(0, nlat):
+ tmp[i, j] =scipy.integrate.quad(Tintegrand, 0, latrad[j], args=(z[i], zT, u0, a, Omega))[0]
+t=tr-H/R*tmp
+
+# potential temperature
+theta=t*np.expand_dims(np.exp(kappa*z/H),axis=1)
+
+# relative humidity
+# follows Booth et al. 2013, Climate Dynamics
+rh = np.zeros((nz, nlat)) + np.nan
+for i in range(0, nz):
+ rh[i, :] = rh0*np.power(1-0.85*z[i]/zTrh, 1.25)
+ if z[i]>14e3:
+ rh[i, :] = 0.0
+
+
+# specific humidty
+# follows calculation in icon
+# /icon-nwp-2.0.15/src/atm_phy_schemes/mo_satad.f90 -> sat_pres_water,spec_humi
+b1 = 610.78 # --> c1es in mo_convect_tables.f90
+b2w= 17.269 # --> c3les
+b3 = 273.15 # --> tmelt; melting temperature in K
+b4w= 35.86 # --> c4les
+sat_pres_water = b1*np.exp(b2w*(t-b3)/(t-b4w))
+Rdv = 287.04/461.51 # Rd/Rv
+o_m_Rdv = 1-Rdv # 1-Rd/Rv
+print(Rdv, o_m_Rdv)
+qv = np.zeros((nz, nlat))
+for i in range(0, nz):
+ for j in range(0, nlat):
+ qv[i, j] = rh[i,j]*Rdv*sat_pres_water[i,j]/(p[i]-o_m_Rdv*sat_pres_water[i,j])
+
+#---------------------------------------
+# save to netcdf files
+#---------------------------------------
+ncfile = nc.Dataset('lc1_initialcondition_CTL_r10x360_with_ps.nc', 'w', clobber=True, format='NETCDF3_CLASSIC')
+ncfile.description = 'Initial condition for LC1, for U and T according to Polvani and Esler 2007 JGR'
+
+# dimensions
+ncfile.createDimension('lat', nlat)
+ncfile.createDimension('lon', nlon)
+ncfile.createDimension('lev', nz)
+ncfile.createDimension('lev_2', 1)
+ncfile.createDimension('nhym', nz)
+ncfile.createDimension('nhyi', nz+1)
+ncfile.createDimension('time', 1)
+
+# variables
+nc_latitude = ncfile.createVariable('lat' , 'f8', ('lat',))
+nc_longitude = ncfile.createVariable('lon' , 'f8', ('lon',))
+nc_lev = ncfile.createVariable('lev' , 'f8', ('lev',))
+nc_lev_2 = ncfile.createVariable('lev_2' , 'f8', ('lev_2',))
+nc_hyam = ncfile.createVariable('hyam' , 'f8', ('nhym',))
+nc_hybm = ncfile.createVariable('hybm' , 'f8', ('nhym',))
+nc_hyai = ncfile.createVariable('hyai' , 'f8', ('nhyi',))
+nc_hybi = ncfile.createVariable('hybi' , 'f8', ('nhyi',))
+nc_time = ncfile.createVariable('time' , 'f8', ('time',))
+nc_T = ncfile.createVariable('T' , 'f4', ('time', 'lev', 'lat', 'lon'))
+nc_U = ncfile.createVariable('U' , 'f4', ('time', 'lev', 'lat', 'lon'))
+nc_V = ncfile.createVariable('V' , 'f4', ('time', 'lev', 'lat', 'lon'))
+nc_W = ncfile.createVariable('W' , 'f4', ('time', 'lev', 'lat', 'lon'))
+nc_QV = ncfile.createVariable('QV' , 'f4', ('time', 'lev', 'lat', 'lon'))
+nc_QC = ncfile.createVariable('QC' , 'f4', ('time', 'lev', 'lat', 'lon'))
+nc_QI = ncfile.createVariable('QI' , 'f4', ('time', 'lev', 'lat', 'lon'))
+nc_SST = ncfile.createVariable('SST' , 'f4', ('time', 'lat', 'lon'))
+nc_LNPS = ncfile.createVariable('LNPS' , 'f4', ('time', 'lev_2', 'lat', 'lon'))
+nc_PS = ncfile.createVariable('ps' , 'f4', ('time', 'lat', 'lon'))
+nc_GEOP_SFC = ncfile.createVariable('GEOP_SFC', 'f4', ('time', 'lev_2', 'lat', 'lon'))
+nc_GEOP_ML = ncfile.createVariable('GEOP_ML' , 'f4', ('time', 'lev_2', 'lat', 'lon'))
+
+# set horizontal grid data
+nc_latitude[:] = lat
+nc_longitude[:] = lon
+
+# set vertical grid data
+nc_lev[:] = lev
+nc_lev.standard_name = "hybrid_sigma_pressure"
+nc_lev.long_name = "hybrid level at layer midpoints"
+nc_lev.formula = "hyam hybm (mlev=hyam+hybm*aps)"
+nc_lev.formula_terms = "ap: hyam b: hybm ps: aps"
+nc_lev.units = "level"
+nc_lev.positive = "down"
+nc_lev_2[:] = lev_2
+nc_lev_2.standard_name = "hybrid_sigma_pressure"
+nc_lev_2.long_name = "hybrid level at layer midpoints"
+nc_lev_2.formula = "hyam hybm (mlev=hyam+hybm*aps)"
+nc_lev_2.formula_terms = "ap: hyam b: hybm ps: aps"
+nc_lev_2.units = "level"
+nc_lev_2.positive = "down"
+nc_hyam[:] = hyam
+nc_hyam.long_name = "hybrid A coefficient at layer midpoints"
+nc_hyam.units = "Pa"
+nc_hybm[:] = hybm
+nc_hybm.long_name = "hybrid B coefficient at layer midpoints"
+nc_hybm.units = "1"
+nc_hyai[:] = hyai
+nc_hyai.long_name = "hybrid A coefficient at layer interfaces"
+nc_hyai.units = "Pa"
+nc_hybi[:] = hybi
+nc_hybi.long_name = "hybrid B coefficient at layer interfaces"
+nc_hybi.units = "1"
+
+# set time data
+nc_time[0] = 23790716.25 # this is the time from ifs2icon_0010_R02B04_aquaplanet.nc
+nc_time.standard_name = "time"
+nc_time.units = "day as %Y%m%d.%f"
+nc_time.calendar = "proleptic_gregorian"
+nc_time.axis = "T"
+
+# set variable data
+for i in range(0, nlon):
+ nc_T[:,:,:,i] = t
+ nc_U[:,:,:,i] = u1
+ nc_V[:,:,:,i] = 0.0
+ nc_W[:,:,:,i] = 0.0
+ nc_QV[:,:,:,i] = qv
+ nc_QC[:,:,:,i] = 0.0
+ nc_QI[:,:,:,i] = 0.0
+ nc_SST[:,:,i] = t[nz-1,:] - 0.5 # as in Booth et al., 2013, Clim. Dynamics we set the SST to lowest-level initial T-0.5K
+ nc_LNPS[:,:,:,i] = np.log(p0)
+ nc_PS[:,:,i] = p0
+ nc_GEOP_SFC[:,:,:,i] = 0.0
+ nc_GEOP_ML[:,:,:,i] = g*z[nz-1]
+
+# set variable attributes
+nc_T.units = "K"; nc_T.standard_name = "temperature"; nc_T.long_name = "Atmospheric temperature"
+nc_U.units = "m s**-1"; nc_U.standard_name = "u-wind"; nc_U.long_name = "Zonal wind"
+nc_V.units = "m s**-1"; nc_V.standard_name = "v-wind"; nc_V.long_name = "Meridional wind"
+nc_W.units = "Pa s**-1"; nc_W.standard_name = "lagrangian_tendency_of_air_pressure"; nc_W.long_name = "Vertical Velocity (Pressure) (omega=dp/dt)"
+nc_QV.units = "kg kg**-1"; nc_QV.standard_name = "spec. humidity"; nc_QV.long_name = "Specific humidity"
+nc_QC.units = "kg kg**-1"; nc_QC.standard_name = "cloud liq. water"; nc_QC.long_name = "Specific cloud liquid water content"
+nc_QI.units = "kg kg**-1"; nc_QI.standard_name = "cloud ice"; nc_QI.long_name = "Specific cloud ice water content"
+nc_GEOP_ML.units = "m**2 s**-2"; nc_GEOP_ML.standard_name = "geopotential"; nc_GEOP_ML.long_name = "Geopotential"
+nc_SST.units = "K"; nc_SST.standard_name = "sst"; nc_SST.long_name = "Sea-surface temperature"
+nc_LNPS.units = "n/a"; nc_LNPS.standard_name = "ln sfc pressure"; nc_LNPS.long_name = "Logarithm of surface pressure"
+nc_PS.units = "Pa"; nc_PS.standard_name = "surface_air_pressure"; nc_PS.long_name = "Surface pressure"
+nc_GEOP_SFC.units = "m**2 s**-2"; nc_GEOP_SFC.standard_name = "sfc geopotential"; nc_GEOP_SFC.long_name = "Sfc Geopotential"
+
+ncfile.close()
diff --git a/Scripts_for_preprocessing/generate_initialdata/lc1_perturbation_condition_wavenumber7.py b/Scripts_for_preprocessing/generate_initialdata/lc1_perturbation_condition_wavenumber7.py
new file mode 100644
index 0000000000000000000000000000000000000000..436f55a9bb780cbcef6eb8aeff450b4ec04e5eb4
--- /dev/null
+++ b/Scripts_for_preprocessing/generate_initialdata/lc1_perturbation_condition_wavenumber7.py
@@ -0,0 +1,112 @@
+import numpy as np
+import netCDF4 as nc
+import matplotlib.pyplot as plt
+
+def load_levelinfo():
+ from netCDF4 import Dataset
+ file = Dataset('./ifs2icon_verticalgridinfo_137levels.nc', 'r')
+ hyam = np.squeeze(np.array(file.variables['hyam']))
+ hybm = np.squeeze(np.array(file.variables['hybm']))
+ hyai = np.squeeze(np.array(file.variables['hyai']))
+ hybi = np.squeeze(np.array(file.variables['hybi']))
+ lev = np.squeeze(np.array(file.variables['lev' ]))
+ lev_2= np.squeeze(np.array(file.variables['lev_2']))
+ return hyam, hybm, hyai, hybi, lev, lev_2
+
+hyam, hybm, hyai, hybi, lev, lev_2 = load_levelinfo()
+
+# constants
+That = 1.0
+phihat = 45.0
+m = 7.06
+
+#latitude-longitude grid
+lat = np.linspace(-90,90,360)
+lon = np.linspace(0,360,720)
+nlat = lat.size
+nlon = lon.size
+nz = lev.size
+
+# latitude in radians
+latrad = lat*np.pi/180.0
+lonrad = lon*np.pi/180.0
+
+# Tp temperature perturbation
+Tp = np.zeros((nz, nlat, nlon))
+for j in range(0, nlat):
+ for i in range(0,nlon):
+ Tp[:, j, i] = That*np.sin(m*lonrad[i])*np.power(1/np.cosh(m*(latrad[j]-phihat*np.pi/180.0)),2)
+
+#---------------------------------------
+# save to netcdf files
+#---------------------------------------
+ncfile = nc.Dataset('lc1_tperturb_720x360.nc', 'w', clobber=True, format='NETCDF3_CLASSIC')
+ncfile.description = 'Temperature perturbation Tprime for the initial condition of lifecylce, Eq(10) of Polvani and Esler 2007 JGR'
+
+# dimensions
+ncfile.createDimension('lat', nlat)
+ncfile.createDimension('lon', nlon)
+ncfile.createDimension('lev', nz)
+ncfile.createDimension('lev_2', 1)
+ncfile.createDimension('nhym', nz)
+ncfile.createDimension('nhyi', nz+1)
+ncfile.createDimension('time', 1)
+
+# variables
+nc_latitude = ncfile.createVariable('lat' , 'f8', ('lat',))
+nc_longitude = ncfile.createVariable('lon' , 'f8', ('lon',))
+nc_lev = ncfile.createVariable('lev' , 'f8', ('lev',))
+nc_lev_2 = ncfile.createVariable('lev_2' , 'f8', ('lev_2',))
+nc_hyam = ncfile.createVariable('hyam' , 'f8', ('nhym',))
+nc_hybm = ncfile.createVariable('hybm' , 'f8', ('nhym',))
+nc_hyai = ncfile.createVariable('hyai' , 'f8', ('nhyi',))
+nc_hybi = ncfile.createVariable('hybi' , 'f8', ('nhyi',))
+nc_time = ncfile.createVariable('time' , 'f8', ('time',))
+nc_Tp = ncfile.createVariable('Tp' , 'f4', ('time', 'lev', 'lat', 'lon'))
+
+# set horizontal grid data
+nc_latitude[:] = lat
+nc_longitude[:] = lon
+
+# set vertical grid data
+nc_lev[:] = lev
+nc_lev.standard_name = "hybrid_sigma_pressure"
+nc_lev.long_name = "hybrid level at layer midpoints"
+nc_lev.formula = "hyam hybm (mlev=hyam+hybm*aps)"
+nc_lev.formula_terms = "ap: hyam b: hybm ps: aps"
+nc_lev.units = "level"
+nc_lev.positive = "down"
+nc_lev_2[:] = lev_2
+nc_lev_2.standard_name = "hybrid_sigma_pressure"
+nc_lev_2.long_name = "hybrid level at layer midpoints"
+nc_lev_2.formula = "hyam hybm (mlev=hyam+hybm*aps)"
+nc_lev_2.formula_terms = "ap: hyam b: hybm ps: aps"
+nc_lev_2.units = "level"
+nc_lev_2.positive = "down"
+nc_hyam[:] = hyam
+nc_hyam.long_name = "hybrid A coefficient at layer midpoints"
+nc_hyam.units = "Pa"
+nc_hybm[:] = hybm
+nc_hybm.long_name = "hybrid B coefficient at layer midpoints"
+nc_hybm.units = "1"
+nc_hyai[:] = hyai
+nc_hyai.long_name = "hybrid A coefficient at layer interfaces"
+nc_hyai.units = "Pa"
+nc_hybi[:] = hybi
+nc_hybi.long_name = "hybrid B coefficient at layer interfaces"
+nc_hybi.units = "1"
+
+# set time data
+nc_time[0] = 23790716.25 # this is the time from ifs2icon_0010_R02B04_aquaplanet.nc
+nc_time.standard_name = "time"
+nc_time.units = "day as %Y%m%d.%f"
+nc_time.calendar = "proleptic_gregorian"
+nc_time.axis = "T"
+
+# set variable data
+nc_Tp[:,:,:,:] = Tp
+
+# set variable attributes
+nc_Tp.units = "K"; nc_Tp.standard_name = "temperature"; nc_Tp.long_name = "Atmospheric temperature"
+
+ncfile.close()
diff --git a/Scripts_for_preprocessing/get_Tanomalies/get_Tanom_from_MPI-ESM_mlev.sh b/Scripts_for_preprocessing/get_Tanomalies/get_Tanom_from_MPI-ESM_mlev.sh
new file mode 100644
index 0000000000000000000000000000000000000000..a5632d230dd25ad98b57cc9f51079d0277919f2d
--- /dev/null
+++ b/Scripts_for_preprocessing/get_Tanomalies/get_Tanom_from_MPI-ESM_mlev.sh
@@ -0,0 +1,197 @@
+#!/bin/bash
+# Prepare 6-hourly model output on hybrid sigma levels for ICON simulation
+# historical: 1980-2010 -> winter 1980/81 - 2009/10
+# near future: 2020-2050 -> winter 2020/21 - 2049/50
+# far future: 2070-2100 -> winter 2070/71 - 2099/2100
+
+institute="MPI-M"
+model="MPI-ESM1-2-LR"
+
+# height levels from ICON initial conditions file
+z="86345.5722915056,79322.4093339252,76170.1357211878,73231.8163420052,70486.3538673693,67915.2997410308,65502.4537494155,63233.5368503841,61095.9178385572,59078.3837407794,57170.9495323468,55364.6954914310,53651.6294395941,52024.5710477045,50477.0516284331,49003.2296016428,47597.8157372562,46256.0105986010,44973.4495802030,43746.1541951765,42570.4912757526,41443.1365116378,40361.0425635773,39321.4107735892,38321.6659695432,37359.4352149795,36432.5285699321,35538.9216273151,34676.7397702895,33844.2451757581,33039.8250383766,32261.9801857344,31509.3142704131,30780.5259960997,30074.4019263420,29389.8078588518,28725.6832619181,28081.0353174911,27454.9329702988,26846.5033455732,26254.9266416751,25679.4317675026,25119.2933299999,24573.8280085659,24042.4566394904,23524.7537974728,23020.3679085019,22528.9558286229,22050.1844356583,21583.7290375909,21129.1227772948,20685.3989775932,20251.2752075614,19825.5353714588,19407.0497385363,18994.7634584440,18587.6682754168,18184.7940552324,17785.4424700615,17389.3842191586,16996.6080444898,16607.1025216615,16220.8541241609,15837.8515679465,15458.0887088975,15081.5477644531,14708.2061690694,14338.0500775912,13971.0562290607,13607.1963004363,13246.4473516155,12888.7801509908,12534.1635292784,12182.5684686355,11833.9636844194,11488.3172198557,11145.5969650376,10805.7708081153,10468.8068300842,10134.6735365985,9803.3392926197,9474.7732713214,9148.9454669403,8825.8250552529,8505.3835842148,8187.5928725691,7872.4243294653,7559.8509520542,7249.8458861630,6942.3833085896,6637.4378265350,6334.9850677737,6035.0006778394,5737.4603644125,5442.3406300771,5149.9827319463,4861.4402848198,4578.3062377667,4301.9399834781,4033.2433181982,3773.0060230056,3521.8995647162,3280.4752514175,3049.1642690842,2828.2810754964,2618.0294296530,2418.5075824695,2229.7175088433,2051.5753284917,1883.9193146682,1726.5212387473,1579.0978323314,1441.3181969590,1312.8153127827,1193.1951432829,1082.0429248404,978.9312212192,883.4271524030,795.0968573945,713.5103920015,638.2459582444,568.8927204503,505.0539122959,446.3471272144,392.4077580309,342.8892241721,297.4631385151,255.8194769287,217.6660756373,182.7303318654,150.7565010082,121.5063720715,94.7576755780,70.3045992144,47.9562863048,27.5372203559,8.8923220126"
+
+# North Atlantic-European region
+lonlatbox="-90,40,20,80"
+lonlatboxstr="90w40e20n80n"
+
+# store output in /work/bb1152/Module_A/A6_CyclEx/b380490_Albern/CMIP6_anomalies_mlev
+cd /work/bb1152/Module_A/A6_CyclEx/b380490_Albern/CMIP6_anomalies_mlev
+
+#######################################################################
+# historical simulations
+exp="historical"
+
+# path to historical simulation
+path_hist=$'/pool/data/CMIP6/data/CMIP/'$institute$'/'$model$'/'$exp$'/r1i1p1f1/6hrLev/ta/gn/v20190710'
+echo $path_hist
+
+# files needed for historical period
+file_hist1=$'ta_6hrLev_'$model$'_'$exp$'_r1i1p1f1_gn_197001010600-199001010000.nc'
+file_hist2=$'ta_6hrLev_'$model$'_'$exp$'_r1i1p1f1_gn_199001010600-201001010000.nc'
+file_hist3=$'ta_6hrLev_'$model$'_'$exp$'_r1i1p1f1_gn_201001010600-201501010000.nc'
+#echo $file_hist1
+#echo $file_hist2
+#echo $file_hist3
+
+# calculate monthly-mean data
+ofile1=${file_hist1%.*}$'.monmean.nc'
+ofile2=${file_hist2%.*}$'.monmean.nc'
+ofile3=${file_hist3%.*}$'.monmean.nc'
+#echo $ofile1
+#echo $ofile2
+#echo $ofile3
+#cdo monmean $path_hist/$file_hist1 $ofile1
+#cdo monmean $path_hist/$file_hist2 $ofile2
+#cdo monmean $path_hist/$file_hist3 $ofile3
+
+# merge the two files
+ofile_merge=${ofile1%-*}$'-'${ofile3##*-}
+#echo $ofile_merge
+cdo mergetime $ofile1 $ofile2 $ofile3 $ofile_merge
+
+# extract 1980-2010 and calculate seasonal mean
+ofile_seasons=$'ta_Lev_'$model$'_'$exp$'_r1i1p1f1_1980-81to2009-10.seasmean.nc'
+echo $ofile_seasons
+cdo seasmean -selyear,1980/2010 $ofile_merge $ofile_seasons
+
+# get time mean over DJF season
+# Note: output of seasmean contains timesteps for Jan, Apr, July, October
+# (and December for last year). We only keep the January timesteps as
+# this is the DJF mean.
+# We do not use the first timestep because this is only a Jan-Feb mean
+# and not a "full" winter. Thus, we keep winter 1980/81 - 2009/10
+# (=30 winter seasons).
+ofile_djf=${ofile_seasons%.*}$'.DJF.timmean.nc'
+echo $ofile_djf
+cdo timmean -selmon,1 -seltimestep,2/125 $ofile_seasons $ofile_djf
+
+# remap data to 1deg x 1deg resolution and cut out the North Atlantic-European region
+ofile_naeu=${ofile_djf%.*}$'.remapcon.'$lonlatboxstr$'.nc'
+echo $ofile_naeu
+cdo -s sellonlatbox,$lonlatbox -remapcon,r360x180 $ofile_djf $ofile_naeu
+
+# interpolate hypbrid sigma pressure coordinate to height levels needed for ICON simulation
+ofile_hl=${ofile_naeu%.*}$'.ml2hl.nc'
+echo $ofile_hl
+cdo ml2hl,$z $ofile_naeu $ofile_hl
+
+# calculate zonal mean
+ofile_zm=${ofile_hl%.*}$'.zonmean.nc'
+echo $ofile_zm
+cdo zonmean $ofile_hl $ofile_zm
+
+# change filenames to a shorter version
+file_hist=${ofile_hl%%.*}$'.DJFmean.'$lonlatboxstr$'.nc'
+file_hist_zm=${ofile_hl%%.*}$'.DJFmean.'$lonlatboxstr$'.zonmean.nc'
+mv $ofile_hl $file_hist
+mv $ofile_zm $file_hist_zm
+
+# delete auxiliary data (keep monthly mean data because computing
+# it takes about 30min per file)
+###rm $ofile1 $ofile2 $ofile3
+###rm $ofile_merge
+rm $ofile_seasons $ofile_djf $ofile_naeu
+
+echo
+echo
+
+#####################################################################
+# path to SSP585 simulation
+path_ssp=/pool/data/CMIP6/data/ScenarioMIP/MPI-M/MPI-ESM1-2-LR/ssp585/r1i1p1f1/6hrLev/ta/gn/v20190710
+
+# loop over near future and far future time periods
+futures=( "near" "far" )
+for future in "${futures[@]}"; do
+echo $future
+if [ "$future" == "near" ]; then
+ # files needed for near future period
+ file1=ta_6hrLev_MPI-ESM1-2-LR_ssp585_r1i1p1f1_gn_201501010600-203501010000.nc
+ file2=ta_6hrLev_MPI-ESM1-2-LR_ssp585_r1i1p1f1_gn_201501010600-203501010000.nc
+ file3=ta_6hrLev_MPI-ESM1-2-LR_ssp585_r1i1p1f1_gn_203501010600-205501010000.nc
+elif [ "$future" == "far" ]; then
+ # files needed for far future period
+ file1=ta_6hrLev_MPI-ESM1-2-LR_ssp585_r1i1p1f1_gn_205501010600-207501010000.nc
+ file2=ta_6hrLev_MPI-ESM1-2-LR_ssp585_r1i1p1f1_gn_207501010600-209501010000.nc
+ file3=ta_6hrLev_MPI-ESM1-2-LR_ssp585_r1i1p1f1_gn_209501010600-210101010000.nc
+fi
+
+# calculate monthly-mean data
+ofile1=${file1%.*}$'.monmean.nc'
+ofile2=${file2%.*}$'.monmean.nc'
+ofile3=${file3%.*}$'.monmean.nc'
+#echo $ofile1
+#echo $ofile2
+#echo $ofile3
+#cdo monmean $path_ssp/$file1 $ofile1
+#cdo monmean $path_ssp/$file2 $ofile2
+#cdo monmean $path_ssp/$file3 $ofile3
+
+# merge the files for near future and the files for far future
+ofile_merge=${ofile1%-*}$'-'${ofile3##*-}
+echo $ofile_merge
+cdo mergetime $ofile1 $ofile2 $ofile3 $ofile_merge
+
+# extract 2020-2050 and 2070-2100, respectively, and calculate seasonal mean
+if [ "$future" == "near" ];then
+ ofile_seasons=$'ta_Lev_'$model$'_ssp585_r1i1p1f1_2020-21to2049-50.seasmean.nc'
+ echo $ofile_seasons
+ cdo seasmean -selyear,2020/2050 $ofile_merge $ofile_seasons
+elif [ "$future" == "far" ]; then
+ ofile_seasons=$'ta_Lev_'$model$'_ssp585_r1i1p1f1_2070-71to2099-00.seasmean.nc'
+ echo $ofile_seasons
+ cdo seasmean -selyear,2070/2100 $ofile_merge $ofile_seasons
+fi
+
+# get time mean over DJF season
+# Note: As for the historical simulation, we do not use the first
+# timestep of the seasmean file and keep 30 winter seasons.
+ofile_djf=${ofile_seasons%.*}$'.DJF.nc'
+echo $ofile_djf
+cdo timmean -selmon,1 -seltimestep,2/125 $ofile_seasons $ofile_djf
+
+# remap data to 1deg x 1deg resolution and cut out the North Atlantic-European region
+ofile_naeu=${ofile_djf%.*}$'.remapcon.'$lonlatboxstr$'.nc'
+echo $ofile_naeu
+cdo -s sellonlatbox,$lonlatbox -remapcon,r360x180 $ofile_djf $ofile_naeu
+
+# interpolate hypbrid sigma pressure coordinate to height levels needed for ICON simulation
+ofile_hl=${ofile_naeu%.*}$'.ml2hl.nc'
+echo $ofile_hl
+cdo ml2hl,$z $ofile_naeu $ofile_hl
+
+# calculate zonal mean
+ofile_zm=${ofile_hl%.*}$'.zonmean.nc'
+echo $ofile_zm
+cdo zonmean $ofile_hl $ofile_zm
+
+# change filenames to a shorter version
+if [ "$future" == "near" ];then
+ file_near=${ofile_hl%%.*}$'.DJFmean.'$lonlatboxstr$'.nc'
+ file_near_zm=${ofile_hl%%.*}$'.DJFmean.'$lonlatboxstr$'.zonmean.nc'
+ mv $ofile_hl $file_near
+ mv $ofile_zm $file_near_zm
+elif [ "$future" == "far" ]; then
+ file_far=${ofile_hl%%.*}$'.DJFmean.'$lonlatboxstr$'.nc'
+ file_far_zm=${ofile_hl%%.*}$'.DJFmean.'$lonlatboxstr$'.zonmean.nc'
+ mv $ofile_hl $file_far
+ mv $ofile_zm $file_far_zm
+fi
+
+# delete auxiliary data
+###rm $ofile1 $ofile2 $ofile3
+###rm $ofile_merge
+rm $ofile_seasons $ofile_djf $ofile_naeu
+
+echo
+echo
+
+done
+
+
+#######################################################################
+# subtract near future and historical and far future and historical
+# to get the temperature anomalies
+
+cdo sub $file_near_zm $file_hist_zm $model$'_nearfuture-historical_zm.nc'
+cdo sub $file_far_zm $file_hist_zm $model$'_farfuture-historical_zm.nc'
+#
diff --git a/Scripts_for_preprocessing/get_Tanomalies/get_Tanom_from_MPI-ESM_plev.sh b/Scripts_for_preprocessing/get_Tanomalies/get_Tanom_from_MPI-ESM_plev.sh
new file mode 100644
index 0000000000000000000000000000000000000000..74f636b9df6842fe4346a3fa80c68c662d81551e
--- /dev/null
+++ b/Scripts_for_preprocessing/get_Tanomalies/get_Tanom_from_MPI-ESM_plev.sh
@@ -0,0 +1,145 @@
+#!/bin/bash
+# Prepare monthly-mean model output on pressure levels for ICON simulation
+# historical: 1980-2010 -> winter 1980/81 - 2009/10
+# near future: 2020-2050 -> winter 2020/21 - 2049/50
+# far future: 2070-2100 -> winter 2070/71 - 2099/2100
+
+institute="MPI-M"
+model="MPI-ESM1-2-LR"
+ensmem="r1i1p1f1"
+
+# North Atlantic-European region
+lonlatbox="-90,40,20,80"
+lonlatboxstr="90w40e20n80n"
+
+# store output in /work/bb1152/Module_A/A6_CyclEx/b380490_Albern/CMIP6_anomalies_plev
+cd /work/bb1152/Module_A/A6_CyclEx/b380490_Albern/CMIP6_anomalies_plev
+
+# path to data that was prepared by Hilke
+path="/work/bb1152/Module_A/A6_CyclEx/b380543_lentink/Environment_for_ICON/data_clim/clim3D"
+#echo $path
+
+#######################################################################
+# historical simulation
+exp="historical"
+echo $exp
+
+# merge years 1980-2010
+# input data is stored with this syntax:
+# clim3D_Amon_<MODEL>_<EXP>_<ENSMEM>_gn_<YEAR>_90w40e20n80n.nc
+ofile_merge=$"clim3D_Amon_"$model$"_"$exp$"_"$ensmem$"_1980-81to2009-10.nc"
+#echo $ofile_merge
+cdo mergetime $path/$"clim3D_Amon_"$model$"_"$exp$"_"$ensmem$"_gn_198"*$"_90w40e20n80n.nc" $path/$"clim3D_Amon_"$model$"_"$exp$"_"$ensmem$"_gn_199"*$"_90w40e20n80n.nc" $path/$"clim3D_Amon_"$model$"_"$exp$"_"$ensmem$"_gn_200"*$"_90w40e20n80n.nc" $path/$"clim3D_Amon_"$model$"_"$exp$"_"$ensmem$"_gn_2010_90w40e20n80n.nc" $ofile_merge
+
+# calculate seasonal mean
+ofile_seasons=${ofile_merge%.*}$".seasmean.nc"
+#echo $ofile_seasons
+cdo seasmean $ofile_merge $ofile_seasons
+
+# get time mean over DJF season
+# Note: output of seasmean contains timesteps for Jan, Apr, July and
+# October for each year and and December for the last year. We only
+# keep the January timesteps as this is the DJF mean.
+# We do not use the first timestep because this is only a Jan-Feb mean
+# and not a "full" winter. Thus, we keep winter 1980/81 - 2009/10
+# (=30 winter seasons).
+ofile_djf=${ofile_seasons%.*}$".DJF.timmean.nc"
+#echo $ofile_djf
+cdo timmean -selmon,1 -seltimestep,2/125 $ofile_seasons $ofile_djf
+
+# remap data to grid from initial file and cut out the
+# North Atlantic-European region
+ofile_1deg=${ofile_djf%.*}$".remapcon."$lonlatboxstr$".nc"
+#echo $ofile_1deg
+cdo -s sellonlatbox,$lonlatbox -remapcon,r360x360 $ofile_djf $ofile_1deg
+
+# calculate the zonal mean
+ofile_zm=${ofile_1deg%.*}$".zonmean.nc"
+#echo $ofile_zm
+cdo zonmean $ofile_1deg $ofile_zm
+
+# change filenames to a shorter version
+file_hist=${ofile_1deg%%.*}$".DJFmean."$lonlatboxstr$".nc"
+file_hist_zm=${ofile_1deg%%.*}$".DJFmean."$lonlatboxstr$".zonmean.nc"
+mv $ofile_1deg $file_hist
+mv $ofile_zm $file_hist_zm
+
+# delete auxiliary data
+rm $ofile_merge $ofile_seasons $ofile_djf
+
+echo
+
+#####################################################################
+# SSP585 simulation
+exp="ssp585"
+
+# loop over near future and far future time periods
+futures=( "near" "far" )
+for future in "${futures[@]}"; do
+echo $future
+if [ "$future" == "near" ]; then
+ # merge years 2020-2050
+ ofile_merge=$"clim3D_Amon_"$model$"_"$exp$"_"$ensmem$"_2020-21to2049-50.nc"
+ #echo $ofile_merge
+ cdo mergetime $path/$"clim3D_Amon_"$model$"_"$exp$"_"$ensmem$"_gn_202"*$"_90w40e20n80n.nc" $path/$"clim3D_Amon_"$model$"_"$exp$"_"$ensmem$"_gn_203"*$"_90w40e20n80n.nc" $path/$"clim3D_Amon_"$model$"_"$exp$"_"$ensmem$"_gn_204"*$"_90w40e20n80n.nc" $path/$"clim3D_Amon_"$model$"_"$exp$"_"$ensmem$"_gn_2050_90w40e20n80n.nc" $ofile_merge
+elif [ "$future" == "far" ]; then
+ # merge years 2070-2100
+ ofile_merge=$"clim3D_Amon_"$model$"_"$exp$"_"$ensmem$"_2070-71to2099-00.nc"
+ #echo $ofile_merge
+ cdo mergetime $path/$"clim3D_Amon_"$model$"_"$exp$"_"$ensmem$"_gn_207"*$"_90w40e20n80n.nc" $path/$"clim3D_Amon_"$model$"_"$exp$"_"$ensmem$"_gn_208"*$"_90w40e20n80n.nc" $path/$"clim3D_Amon_"$model$"_"$exp$"_"$ensmem$"_gn_209"*$"_90w40e20n80n.nc" $path/$"clim3D_Amon_"$model$"_"$exp$"_"$ensmem$"_gn_2100_90w40e20n80n.nc" $ofile_merge
+fi
+
+# calculate seasonal mean
+ofile_seasons=${ofile_merge%.*}$".seasmean.nc"
+#echo $ofile_seasons
+cdo seasmean $ofile_merge $ofile_seasons
+
+# get time mean over DJF season
+# Note: As for the historical simulation, we do not use the first
+# timestep of the seasmean file and keep 30 winter seasons.
+ofile_djf=${ofile_seasons%.*}$".DJF.timmean.nc"
+#echo $ofile_djf
+cdo timmean -selmon,1 -seltimestep,2/125 $ofile_seasons $ofile_djf
+
+# remap data to grid from initial file and cut out the
+# North Atlantic-European region
+ofile_1deg=${ofile_djf%.*}$".remapcon."$lonlatboxstr$".nc"
+#echo $ofile_1deg
+cdo -s sellonlatbox,$lonlatbox -remapcon,r360x360 $ofile_djf $ofile_1deg
+
+# calculate the zonal mean
+ofile_zm=${ofile_1deg%.*}$".zonmean.nc"
+#echo $ofile_zm
+cdo zonmean $ofile_1deg $ofile_zm
+
+# change filenames to a shorter version
+if [ "$future" == "near" ];then
+ file_near=${ofile_1deg%%.*}$".DJFmean."$lonlatboxstr$".nc"
+ file_near_zm=${ofile_1deg%%.*}$".DJFmean."$lonlatboxstr$".zonmean.nc"
+ mv $ofile_1deg $file_near
+ mv $ofile_zm $file_near_zm
+elif [ "$future" == "far" ]; then
+ file_far=${ofile_1deg%%.*}$".DJFmean."$lonlatboxstr$".nc"
+ file_far_zm=${ofile_1deg%%.*}$".DJFmean."$lonlatboxstr$".zonmean.nc"
+ mv $ofile_1deg $file_far
+ mv $ofile_zm $file_far_zm
+fi
+
+# delete auxiliary data
+rm $ofile_merge $ofile_seasons $ofile_djf
+
+echo
+
+done
+
+
+#######################################################################
+# subtract near future and historical and far future and historical
+# to get the temperature anomalies
+echo "Subtract future and historical"
+
+cdo sub $file_near_zm $file_hist_zm $model$"_nearfuture-historical_zm.nc"
+cdo sub $file_far_zm $file_hist_zm $model$"_farfuture-historical_zm.nc"
+
+echo "DONE!"
+#
diff --git a/Scripts_for_preprocessing/get_Tanomalies/get_Tanom_plev.sh b/Scripts_for_preprocessing/get_Tanomalies/get_Tanom_plev.sh
new file mode 100644
index 0000000000000000000000000000000000000000..77cc00a1c8e11bb1a382e0d824b7cbb2de8898ac
--- /dev/null
+++ b/Scripts_for_preprocessing/get_Tanomalies/get_Tanom_plev.sh
@@ -0,0 +1,159 @@
+#!/bin/bash
+# Prepare monthly-mean model output on pressure levels for ICON simulation
+# historical: 1980-2010 -> winter 1980/81 - 2009/10
+# near future: 2020-2050 -> winter 2020/21 - 2049/50
+# far future: 2070-2100 -> winter 2070/71 - 2099/2100
+
+
+# models, for which historical and ssp585 data is available in
+# /work/bb1152/Module_A/A6_CyclEx/b380543_lentink/Environment_for_ICON/data_clim/clim3D
+# (as of July 2021)
+# model ensemble member
+# BCC-CSM2-MR r1i1p1f1
+# CNRM-CM6-1 r1i1p1f2
+# CNRM-ESM2-1 r1i1p1f2
+# MIROC6 r1i1p1f1
+# MPI-ESM1-2-LR r1i1p1f1
+# MRI-ESM2-0 r1i1p1f1
+
+# decide, for wich model and ensemble member the temperature anomalies
+# are calculated
+model="MRI-ESM2-0" #"MPI-ESM1-2-LR"
+ensmem="r1i1p1f1" #"r1i1p1f1"
+
+# North Atlantic-European region
+lonlatbox="-90,40,20,80"
+lonlatboxstr="90w40e20n80n"
+
+# store output in /work/bb1152/Module_A/A6_CyclEx/b380490_Albern/CMIP6_anomalies_plev
+cd /work/bb1152/Module_A/A6_CyclEx/b380490_Albern/CMIP6_anomalies_plev
+
+# path to data that was prepared by Hilke
+path="/work/bb1152/Module_A/A6_CyclEx/b380543_lentink/Environment_for_ICON/data_clim/clim3D"
+#echo $path
+
+#######################################################################
+# historical simulation
+exp="historical"
+echo $exp
+
+# merge years 1980-2010
+# input data is stored with this syntax:
+# clim3D_Amon_<MODEL>_<EXP>_<ENSMEM>_gn_<YEAR>_90w40e20n80n.nc or
+# clim3D_Amon_<MODEL>_<EXP>_<ENSMEM>_gr_<YEAR>_90w40e20n80n.nc
+ofile_merge=$"clim3D_Amon_"$model$"_"$exp$"_"$ensmem$"_1980-81to2009-10.nc"
+#echo $ofile_merge
+cdo mergetime $path/$"clim3D_Amon_"$model$"_"$exp$"_"$ensmem$"_g"*$"_198"*$"_90w40e20n80n.nc" $path/$"clim3D_Amon_"$model$"_"$exp$"_"$ensmem$"_g"*$"_199"*$"_90w40e20n80n.nc" $path/$"clim3D_Amon_"$model$"_"$exp$"_"$ensmem$"_g"*$"_200"*$"_90w40e20n80n.nc" $path/$"clim3D_Amon_"$model$"_"$exp$"_"$ensmem$"_g"*$"_2010_90w40e20n80n.nc" $ofile_merge
+
+# calculate seasonal mean
+ofile_seasons=${ofile_merge%.*}$".seasmean.nc"
+#echo $ofile_seasons
+cdo seasmean $ofile_merge $ofile_seasons
+
+# get time mean over DJF season
+# Note: output of seasmean contains timesteps for Jan, Apr, July and
+# October for each year and and December for the last year. We only
+# keep the January timesteps as this is the DJF mean.
+# We do not use the first timestep because this is only a Jan-Feb mean
+# and not a "full" winter. Thus, we keep winter 1980/81 - 2009/10
+# (=30 winter seasons).
+ofile_djf=${ofile_seasons%.*}$".DJF.timmean.nc"
+#echo $ofile_djf
+cdo timmean -selmon,1 -seltimestep,2/125 $ofile_seasons $ofile_djf
+
+# remap data to grid from initial file and cut out the
+# North Atlantic-European region
+ofile_1deg=${ofile_djf%.*}$".remapcon."$lonlatboxstr$".nc"
+#echo $ofile_1deg
+cdo -s sellonlatbox,$lonlatbox -remapcon,r360x360 $ofile_djf $ofile_1deg
+
+# calculate the zonal mean
+ofile_zm=${ofile_1deg%.*}$".zonmean.nc"
+#echo $ofile_zm
+cdo zonmean $ofile_1deg $ofile_zm
+
+# change filenames to a shorter version
+file_hist=${ofile_1deg%%.*}$".DJFmean."$lonlatboxstr$".nc"
+file_hist_zm=${ofile_1deg%%.*}$".DJFmean."$lonlatboxstr$".zonmean.nc"
+mv $ofile_1deg $file_hist
+mv $ofile_zm $file_hist_zm
+
+# delete auxiliary data
+rm $ofile_merge $ofile_seasons $ofile_djf
+
+echo
+
+#####################################################################
+# SSP585 simulation
+exp="ssp585"
+
+# loop over near future and far future time periods
+futures=( "near" "far" )
+for future in "${futures[@]}"; do
+echo $future
+if [ "$future" == "near" ]; then
+ # merge years 2020-2050
+ ofile_merge=$"clim3D_Amon_"$model$"_"$exp$"_"$ensmem$"_2020-21to2049-50.nc"
+ #echo $ofile_merge
+ cdo mergetime $path/$"clim3D_Amon_"$model$"_"$exp$"_"$ensmem$"_g"*$"_202"*$"_90w40e20n80n.nc" $path/$"clim3D_Amon_"$model$"_"$exp$"_"$ensmem$"_g"*$"_203"*$"_90w40e20n80n.nc" $path/$"clim3D_Amon_"$model$"_"$exp$"_"$ensmem$"_g"*$"_204"*$"_90w40e20n80n.nc" $path/$"clim3D_Amon_"$model$"_"$exp$"_"$ensmem$"_g"*$"_2050_90w40e20n80n.nc" $ofile_merge
+elif [ "$future" == "far" ]; then
+ # merge years 2070-2100
+ ofile_merge=$"clim3D_Amon_"$model$"_"$exp$"_"$ensmem$"_2070-71to2099-00.nc"
+ #echo $ofile_merge
+ cdo mergetime $path/$"clim3D_Amon_"$model$"_"$exp$"_"$ensmem$"_g"*$"_207"*$"_90w40e20n80n.nc" $path/$"clim3D_Amon_"$model$"_"$exp$"_"$ensmem$"_g"*$"_208"*$"_90w40e20n80n.nc" $path/$"clim3D_Amon_"$model$"_"$exp$"_"$ensmem$"_g"*$"_209"*$"_90w40e20n80n.nc" $path/$"clim3D_Amon_"$model$"_"$exp$"_"$ensmem$"_g"*$"_2100_90w40e20n80n.nc" $ofile_merge
+fi
+
+# calculate seasonal mean
+ofile_seasons=${ofile_merge%.*}$".seasmean.nc"
+#echo $ofile_seasons
+cdo seasmean $ofile_merge $ofile_seasons
+
+# get time mean over DJF season
+# Note: As for the historical simulation, we do not use the first
+# timestep of the seasmean file and keep 30 winter seasons.
+ofile_djf=${ofile_seasons%.*}$".DJF.timmean.nc"
+#echo $ofile_djf
+cdo timmean -selmon,1 -seltimestep,2/125 $ofile_seasons $ofile_djf
+
+# remap data to grid from initial file and cut out the
+# North Atlantic-European region
+ofile_1deg=${ofile_djf%.*}$".remapcon."$lonlatboxstr$".nc"
+#echo $ofile_1deg
+cdo -s sellonlatbox,$lonlatbox -remapcon,r360x360 $ofile_djf $ofile_1deg
+
+# calculate the zonal mean
+ofile_zm=${ofile_1deg%.*}$".zonmean.nc"
+#echo $ofile_zm
+cdo zonmean $ofile_1deg $ofile_zm
+
+# change filenames to a shorter version
+if [ "$future" == "near" ];then
+ file_near=${ofile_1deg%%.*}$".DJFmean."$lonlatboxstr$".nc"
+ file_near_zm=${ofile_1deg%%.*}$".DJFmean."$lonlatboxstr$".zonmean.nc"
+ mv $ofile_1deg $file_near
+ mv $ofile_zm $file_near_zm
+elif [ "$future" == "far" ]; then
+ file_far=${ofile_1deg%%.*}$".DJFmean."$lonlatboxstr$".nc"
+ file_far_zm=${ofile_1deg%%.*}$".DJFmean."$lonlatboxstr$".zonmean.nc"
+ mv $ofile_1deg $file_far
+ mv $ofile_zm $file_far_zm
+fi
+
+# delete auxiliary data
+rm $ofile_merge $ofile_seasons $ofile_djf
+
+echo
+
+done
+
+
+#######################################################################
+# subtract near future and historical and far future and historical
+# to get the temperature anomalies
+echo "Subtract future and historical"
+
+cdo sub $file_near_zm $file_hist_zm $model$"_nearfuture-historical_zm.nc"
+cdo sub $file_far_zm $file_hist_zm $model$"_farfuture-historical_zm.nc"
+
+echo "DONE!"
+#