diff --git a/docs/source/conf.py b/docs/source/conf.py
index 4a482c98d9ef5a764939f34283b30f48887a7bb6..399c927e52b89a0f9c3d9f84c266e232e7477b33 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -20,6 +20,7 @@ extensions = [
'sphinx.ext.doctest',
'sphinx.ext.autodoc',
'sphinx.ext.autosummary',
+ 'sphinx.ext.autosectionlabel',
'sphinx.ext.intersphinx',
'sphinx.ext.napoleon',
'nbsphinx',
diff --git a/docs/source/custom_scripts.rst b/docs/source/custom_scripts.rst
index 5fe678adcfcda59e3f798fdadd715ae8caf356ad..350a8a6b996e502aba80e89eaed48be75aa87859 100644
--- a/docs/source/custom_scripts.rst
+++ b/docs/source/custom_scripts.rst
@@ -19,8 +19,6 @@ A workflow method is for example :meth:`dartwrf.workflows.WorkFlows.assimilate`,
Calling :meth:`dartwrf.workflows.WorkFlows.assimilate` triggers the execution of the script `dartwrf/assimilate.py`.
-- Why do I need a separate script (in this case `assimilate.py`) to execute a script?
-Because some users need to use SLURM, which can only call scripts, not run python code directly.
Recipe to add new functionality
*******************************
@@ -31,4 +29,5 @@ If you need write a new script, you need to
#. write a workflow method (`dartwrf/workflows.py`), e.g. copy and modify an existing one,
#. therein you call the script with :meth:`dartwrf.utils.ClusterConfig.run_job` available via `self.cluster.run_job`, be careful which command-line arguments you need
#. write the script and parse the command-line arguments
-#. call whatever python functions you may need
\ No newline at end of file
+#. call whatever python functions you may need
+
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 1f76a8eb7a39602178d1cf85ff64a07f5177af19..e84e10e6d017506eb9eefc944d6149308936eed1 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -1,28 +1,33 @@
-Welcome to the DART-WRF documentation!
-======================================
+DART-WRF documentation
+=======================
+
+**DART-WRF** is a python package which allows you to
+* run the `weather research and forecast model` (`WRF <https://www2.mmm.ucar.edu/wrf/users/docs/docs_and_pubs.html>`_),
+* generate (satellite) observations from a nature run,
+* and assimilate these observations in ensemble data assimilation using `DART <https://docs.dart.ucar.edu/en/latest/>`_,
+* on a computing cluster or on your local machine.
-**DART-WRF** is a python package to run an Ensemble Data Assimilation system using the data assimilation suite `DART <https://docs.dart.ucar.edu/en/latest/README.html>`_ and the weather research and forecast model `WRF <https://www2.mmm.ucar.edu/wrf/users/docs/docs_and_pubs.html>`_.
Installation
-------------
+*************
-DART-WRF is available at `github.com/lkugler/DART-WRF <https://github.com/lkugler/DART-WRF>`_ using the command line. To use it, you don't need to install it, but only its requirements:
+DART-WRF can be downloaded from `github.com/lkugler/DART-WRF <https://github.com/lkugler/DART-WRF>`_. To use it, you don't need to install it, but only its requirements:
.. code-block::
git clone https://github.com/lkugler/DART-WRF.git
pip install xarray netCDF4 docopt pysolar==0.10.0
-Note that `pysolar` is only necessary if you will be using satellite observations.
-
+Note that `pysolar` is necessary to generate synthetic satellite observations.
-Other helpful resources
------------------------
-
-**DART documentation** `[here] <https://docs.dart.ucar.edu/en/latest/README.html>`_
-**WRF user guide** `[here] <http://www2.mmm.ucar.edu/wrf/users/docs/user_guide_v4/v4.2/WRFUsersGuide_v42.pdf>`_
+First steps
+************
+To get started, go through the tutorials in the :ref:`tutorials` section.
+:ref:`tutorial1` shows you how to configure DART-WRF, generate observations and assimilate them.
+:ref:`tutorial2` shows you how to run a WRF forecast with the output from data assimilation.
+:ref:`tutorial3` shows you how assimilation and forecast can be run in a cycle.
.. toctree::
:hidden:
@@ -37,6 +42,16 @@ Other helpful resources
notebooks/tutorial2
notebooks/tutorial3
custom_scripts
+
+
+Other helpful resources
+=======================
+
+**DART documentation** `[here] <https://docs.dart.ucar.edu/en/latest/README.html>`_
+**WRF user guide** `[here] <http://www2.mmm.ucar.edu/wrf/users/docs/user_guide_v4/v4.2/WRFUsersGuide_v42.pdf>`_
+
+
+
.. toctree::
:hidden:
diff --git a/docs/source/notebooks/tutorial1.ipynb b/docs/source/notebooks/tutorial1.ipynb
deleted file mode 100644
index b0f99f4513f83bae657e70cec42a6884d4f8d044..0000000000000000000000000000000000000000
--- a/docs/source/notebooks/tutorial1.ipynb
+++ /dev/null
@@ -1,214 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "fd5c3005-f237-4495-9185-2d4d474cafd5",
- "metadata": {},
- "source": [
- "# Tutorial 1: The assimilation step\n",
- "DART-WRF is a python package which automates many things like configuration, saving configuration and output, handling computing resources, etc.\n",
- "\n",
- "The data for this experiment is accessible for students on the server srvx1.\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "93d59d4d-c514-414e-81fa-4ff390290811",
- "metadata": {},
- "source": [
- "## Configuring the hardware\n",
- "In case you use a cluster which is not supported, copy an existing cluster configuration and modify it, e.g. `config/jet.py`.\n",
- "\n",
- "\n",
- "## Configuring the experiment\n",
- "Firstly, you need to configure the experiment.\n",
- "Copy the existing template and modify it `cp config/exp_template.py config/exp1.py`.\n",
- "\n",
- "Customize your settings:\n",
- "\n",
- "- expname should be a unique identifier and will be used as folder name\n",
- "- model_dx is the model resolution in meters\n",
- "- n_ens is the ensemble size\n",
- "- update_vars are the WRF variables which shall be updated by the assimilation\n",
- "\n",
- "```python\n",
- "exp = utils.Experiment()\n",
- "exp.expname = \"test_newcode\"\n",
- "exp.model_dx = 2000\n",
- "exp.n_ens = 40\n",
- "exp.update_vars = ['U', 'V', 'W', 'THM', 'PH', 'MU', 'QVAPOR', 'QCLOUD', 'QICE', 'PSFC']\n",
- "\n",
- "```\n",
- "\n",
- "### Generating observations\n",
- "In case you want to generate new observations, like for an observing system simulations experiment (OSSE), set \n",
- "```python\n",
- "exp.use_existing_obsseq = False\n",
- "```\n",
- "in this case, you need to set the path to WRF nature run files from where DART can generate observations:\n",
- "```python\n",
- "exp.nature_wrfout_pattern = '/usr/data/sim_archive/exp_v1_nature/*/1/wrfout_d01_%Y-%m-%d_%H:%M:%S'\n",
- "```\n",
- "\n",
- "### Using pre-existing observation files\n",
- "\n",
- "You can use pre-existing observation files with\n",
- "```python\n",
- "exp.use_existing_obsseq = '/usr/data/sim_archive/exp_ABC/obs_seq_out/%Y-%m-%d_%H:%M_obs_seq.out'\n",
- "```\n",
- "where times are filled, depending on the assimilation time.\n",
- "\n",
- "### Customizing the DART namelist\n",
- "By default, the DART namelist of the build directory will be used (copied). \n",
- "If you want to modify any parameters, specify your changes in a python dictionary like below. For a description of the parameters, see [the official DART documentation](https://docs.dart.ucar.edu/).\n",
- "```python\n",
- "exp.dart_nml = {'&assim_tools_nml':\n",
- " dict(filter_kind='1',\n",
- " sampling_error_correction='.true.',\n",
- " ),\n",
- " '&filter_nml':\n",
- " dict(ens_size=exp.n_ens,\n",
- " num_output_state_members=exp.n_ens,\n",
- " num_output_obs_members=exp.n_ens,\n",
- " inf_flavor=['0', '4'],\n",
- " output_members='.true.',\n",
- " output_mean='.true.',\n",
- " output_sd='.true.',\n",
- " stages_to_write='output',\n",
- " ),\n",
- " '&quality_control_nml':\n",
- " dict(outlier_threshold='-1',\n",
- " ),\n",
- " '&location_nml':\n",
- " dict(horiz_dist_only='.false.',\n",
- " '&model_nml':\n",
- " dict(wrf_state_variables =\n",
- " [['U', 'QTY_U_WIND_COMPONENT', 'TYPE_U', 'UPDATE','999',],\n",
- " ['V', 'QTY_V_WIND_COMPONENT', 'TYPE_V', 'UPDATE','999',],\n",
- " ['W', 'QTY_VERTICAL_VELOCITY', 'TYPE_W', 'UPDATE','999',],\n",
- " ['PH', 'QTY_GEOPOTENTIAL_HEIGHT', 'TYPE_GZ', 'UPDATE','999',],\n",
- " ['THM', 'QTY_POTENTIAL_TEMPERATURE','TYPE_T', 'UPDATE','999',],\n",
- " ['MU', 'QTY_PRESSURE', 'TYPE_MU', 'UPDATE','999',],\n",
- " ['QVAPOR','QTY_VAPOR_MIXING_RATIO', 'TYPE_QV', 'UPDATE','999',],\n",
- " ['QICE', 'QTY_ICE_MIXING_RATIO', 'TYPE_QI', 'UPDATE','999',],\n",
- " ['QCLOUD','QTY_CLOUDWATER_MIXING_RATIO','TYPE_QC', 'UPDATE','999',],\n",
- " ['CLDFRA','QTY_CLOUD_FRACTION', 'TYPE_CFRAC','UPDATE','999',],\n",
- " ['PSFC', 'QTY_SURFACE_PRESSURE', 'TYPE_PSFC', 'UPDATE','999',],\n",
- " ['T2', 'QTY_2M_TEMPERATURE', 'TYPE_T', 'UPDATE','999',],\n",
- " ['TSK', 'QTY_SKIN_TEMPERATURE', 'TYPE_T', 'UPDATE','999',],\n",
- " ['REFL_10CM','QTY_RADAR_REFLECTIVITY','TYPE_REFL', 'UPDATE','999',]]),\n",
- " }\n",
- "```\n",
- "Any parameters in this dictionary will be overwritten compared to the default namelist.\n",
- "\n",
- "\n",
- "\n",
- "### Single observation experiment\n",
- "If you want to assimilate one observation, use \n",
- "```python\n",
- "t = dict(plotname='Temperature', plotunits='[K]',\n",
- " kind='RADIOSONDE_TEMPERATURE', \n",
- " n_obs=1, # number of observations\n",
- " obs_locations=[(45., 0.)], # location of observations\n",
- " error_generate=0.2, # observation error used to generate observations\n",
- " error_assimilate=0.2, # observation error used for assimilation\n",
- " heights=[1000,], # for radiosondes, use range(1000, 17001, 2000)\n",
- " loc_horiz_km=50, # horizontal localization half-width\n",
- " loc_vert_km=2.5 # vertical localization half-width\n",
- " ) \n",
- "\n",
- "exp.observations = [t,] # select observations for assimilation\n",
- "```\n",
- "\n",
- "### Assimilating multiple observations\n",
- "To generate a grid of observations, use\n",
- "```python\n",
- "vis = dict(plotname='VIS 0.6µm', plotunits='[1]',\n",
- " kind='MSG_4_SEVIRI_BDRF', sat_channel=1, \n",
- " n_obs=961, obs_locations='square_array_evenly_on_grid',\n",
- " error_generate=0.03, error_assimilate=0.03,\n",
- " loc_horiz_km=50)\n",
- "exp.observations = [t, vis,]\n",
- "```\n",
- "\n",
- "Caution, n_obs should only be one of the following:\n",
- "\n",
- "- 22500 for 2km observation density/resolution \n",
- "- 5776 for 4km; \n",
- "- 961 for 10km; \n",
- "- 256 for 20km; \n",
- "- 121 for 30km\n",
- "\n",
- "For vertically resolved data, like radar, `n_obs` is the number of observations at each observation height level."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "16bd3521-f98f-4c4f-8019-31029fd678ae",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "\n",
- "## Configuring the assimilation experiment\n",
- "We start by importing some modules:\n",
- "```python\n",
- "import datetime as dt\n",
- "from dartwrf.workflows import WorkFlows\n",
- "```\n",
- "\n",
- "To assimilate observations at dt.datetime `time` we set the directory paths and times of the prior ensemble forecasts:\n",
- "\n",
- "```python\n",
- "prior_path_exp = '/users/students/lehre/advDA_s2023/data/sample_ensemble/'\n",
- "prior_init_time = dt.datetime(2008,7,30,12)\n",
- "prior_valid_time = dt.datetime(2008,7,30,12,30)\n",
- "assim_time = prior_valid_time\n",
- "```\n",
- "\n",
- "To set up the experiment, call\n",
- "```python\n",
- "w = WorkFlows(exp_config='exp1.py', server_config='srvx1.py')\n",
- "```\n",
- "It will also create the output folders and backup the configuration files and scripts.\n",
- "\n",
- "\n",
- "Finally, we run the data assimilation by calling\n",
- "```python\n",
- "w.assimilate(assim_time, prior_init_time, prior_valid_time, prior_path_exp)\n",
- "```\n",
- "\n",
- "Congratulations! You're done!"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "82e809a8-5972-47f3-ad78-6290afe4ae17",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "nwp 2023.1 - 3.10",
- "language": "python",
- "name": "nwp2023.1"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.9"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}