diff --git a/docs/source/backup/tutorial1.ipynb b/docs/source/backup/tutorial1.ipynb
deleted file mode 100644
index 798d6b941dc14d3bca583e5c14f92de2b8c18fdb..0000000000000000000000000000000000000000
--- a/docs/source/backup/tutorial1.ipynb
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@@ -1,169 +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 experiment\n",
- "Firstly, you need to configure the experiment in `config/cfg.py`.\n",
- "\n",
- "Let's go through the most important 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",
- "- filter_kind is 1 for the EAKF (see the DART documentation for more)\n",
- "- prior and post_inflation defines what inflation we want (see the DART docs)\n",
- "- sec is the statistical sampling error correction from Anderson (2012)\n",
- "\n",
- "```python\n",
- "exp = utils.Experiment()\n",
- "exp.expname = \"test_newcode\"\n",
- "exp.model_dx = 2000\n",
- "exp.n_ens = 10\n",
- "exp.update_vars = ['U', 'V', 'W', 'THM', 'PH', 'MU', 'QVAPOR', 'QCLOUD', 'QICE', 'PSFC']\n",
- "exp.filter_kind = 1\n",
- "exp.prior_inflation = 0\n",
- "exp.post_inflation = 4\n",
- "exp.sec = True\n",
- "\n",
- "```\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",
- "\n",
- "`exp.nature` defines which WRF files will be used to draw observations from, e.g.: \n",
- "```python\n",
- "exp.nature = '/users/students/lehre/advDA_s2023/data/sample_nature/'\n",
- "```\n",
- "\n",
- "`exp.input_profile` is used, if you create initial conditions from a so called wrf_profile (see WRF guide).\n",
- "```python\n",
- "exp.input_profile = '/doesnt_exist/initial_profiles/wrf/ens/raso.fc.<iens>.wrfprof'\n",
- "```\n",
- "\n",
- "\n",
- "For horizontal localization half-width of 20 km and 3 km vertically, set\n",
- "```python\n",
- "exp.cov_loc_vert_km_horiz_km = (3, 20)\n",
- "```\n",
- "You can also set it to False for no vertical localization.\n",
- "\n",
- "#### Single observation\n",
- "Set your desired observations like this. \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",
- " cov_loc_radius_km=50) # horizontal localization half-width\n",
- "\n",
- "exp.observations = [t,] # select observations for assimilation\n",
- "```\n",
- "\n",
- "#### 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",
- " cov_loc_radius_km=20)\n",
- "```\n",
- "\n",
- "But 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": [
- "### Configuring the hardware\n",
- "In case you use a cluster which is not supported, configure paths inside `config/clusters.py`.\n",
- "\n",
- "\n",
- "\n",
- "\n",
- "### Assimilate observations\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",
- "Finally, we run the data assimilation by calling\n",
- "```python\n",
- "w = WorkFlows(exp_config='cfg.py', server_config='srvx1.py')\n",
- "\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
-}
diff --git a/docs/source/backup/tutorial2.ipynb b/docs/source/backup/tutorial2.ipynb
deleted file mode 100644
index df58b78b35dd5099e742d2e50933d4395150e716..0000000000000000000000000000000000000000
--- a/docs/source/backup/tutorial2.ipynb
+++ /dev/null
@@ -1,122 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "fd5c3005-f237-4495-9185-2d4d474cafd5",
- "metadata": {
- "tags": []
- },
- "source": [
- "# Tutorial 2: Forecast after DA\n",
- "\n",
- "\n",
- "**Goal**: To run a cycled data assimilation experiment.\n",
- "[`cycled_exp.py`](https://github.com/lkugler/DART-WRF/blob/master/generate_free.py) contains an example which will be explained here:\n",
- "\n",
- "Now there are two options:\n",
- "1) To start a forecast from an existing forecast, i.e. from WRF restart files\n",
- "2) To start a forecast from defined thermodynamic profiles, i.e. from a `wrf_profile`\n",
- "\n",
- "\n",
- "### Restart a forecast\n",
- "To run a forecast from initial conditions of a previous forecasts, we import these modules\n",
- "```python\n",
- "import datetime as dt\n",
- "from dartwrf.workflows import WorkFlows\n",
- "```\n",
- "\n",
- "Let's say you want to run a forecast starting at 9 UTC until 12 UTC.\n",
- "Initial conditions shall be taken from a previous experiment in `/user/test/data/sim_archive/exp_abc` which was initialized at 6 UTC and there are WRF restart files for 9 UTC.\n",
- "Then the code would be\n",
- "\n",
- "```python\n",
- "prior_path_exp = '/user/test/data/sim_archive/exp_abc'\n",
- "prior_init_time = dt.datetime(2008,7,30,6)\n",
- "prior_valid_time = dt.datetime(2008,7,30,9)\n",
- "\n",
- "w = WorkFlows(exp_config='cfg.py', server_config='srvx1.py')\n",
- "\n",
- "begin = dt.datetime(2008, 7, 30, 9)\n",
- "end = dt.datetime(2008, 7, 30, 12)\n",
- "\n",
- "w.prepare_WRFrundir(begin)\n",
- "\n",
- "w.prepare_IC_from_prior(prior_path_exp, prior_init_time, prior_valid_time)\n",
- "\n",
- "w.run_ENS(begin=begin, # start integration from here\n",
- " end=end, # integrate until here\n",
- " output_restart_interval=9999, # do not write WRF restart files\n",
- " )\n",
- "```\n",
- "Note that we use predefined workflow functions like `run_ENS`.\n",
- "\n",
- "\n",
- "### Forecast run after Data Assimilation\n",
- "In order to continue after assimilation you need the posterior = prior (1) + increments (2)\n",
- "\n",
- "1. Set posterior = prior\n",
- "```python\n",
- "id = w.prepare_IC_from_prior(prior_path_exp, prior_init_time, prior_valid_time, depends_on=id)\n",
- "```\n",
- "\n",
- "2) Update posterior with increments from assimilation\n",
- "After this, the wrfrst files are updated with assimilation increments from DART output and copied to the WRF's run directories so you can continue to run the forecast ensemble.\n",
- "```python\n",
- "id = w.update_IC_from_DA(time, depends_on=id)\n",
- "```\n",
- "\n",
- "3) Define how long you want to run the forecast and when you want WRF restart files. Since they take a lot of space, we want as few as possible.\n",
- "\n",
- "```python\n",
- "timedelta_integrate = dt.timedelta(hours=5)\n",
- "output_restart_interval = 9999 # any value larger than the forecast duration\n",
- "```\n",
- "\n",
- "If you run DA in cycles of 15 minutes, it will be\n",
- "```python\n",
- "timedelta_integrate = dt.timedelta(hours=5)\n",
- "timedelta_btw_assim = dt.timedelta(minutes=15)\n",
- "output_restart_interval = timedelta_btw_assim.total_seconds()/60\n",
- "```\n",
- "\n",
- "\n",
- "3) Run WRF ensemble\n",
- "```python\n",
- "id = w.run_ENS(begin=time, # start integration from here\n",
- " end=time + timedelta_integrate, # integrate until here\n",
- " output_restart_interval=output_restart_interval,\n",
- " depends_on=id)\n",
- "```\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "400244f1-098b-46ea-b29d-2226c7cbc827",
- "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
-}
diff --git a/docs/source/backup/tutorial3.ipynb b/docs/source/backup/tutorial3.ipynb
deleted file mode 100644
index d3018ab95b378770ca3b05b1e2fc9adb547a188e..0000000000000000000000000000000000000000
--- a/docs/source/backup/tutorial3.ipynb
+++ /dev/null
@@ -1,129 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "fd5c3005-f237-4495-9185-2d4d474cafd5",
- "metadata": {
- "tags": []
- },
- "source": [
- "# Tutorial 3: Cycle forecast and assimilation\n",
- "\n",
- "\n",
- "**Goal**: To run a cycled data assimilation experiment.\n",
- "[`cycled_exp.py`](https://github.com/lkugler/DART-WRF/blob/master/generate_free.py) contains an example which will be explained here:\n",
- "\n",
- "For example, your experiment can look like this\n",
- "\n",
- "```python\n",
- "prior_path_exp = '/jetfs/home/lkugler/data/sim_archive/exp_v1.19_P2_noDA'\n",
- "\n",
- "init_time = dt.datetime(2008, 7, 30, 13)\n",
- "time = dt.datetime(2008, 7, 30, 14)\n",
- "last_assim_time = dt.datetime(2008, 7, 30, 14)\n",
- "forecast_until = dt.datetime(2008, 7, 30, 14, 15)\n",
- "\n",
- "w.prepare_WRFrundir(init_time)\n",
- "id = w.run_ideal(depends_on=id)\n",
- "\n",
- "prior_init_time = init_time\n",
- "prior_valid_time = time\n",
- "\n",
- "while time <= last_assim_time:\n",
- "\n",
- " # usually we take the prior from the current time\n",
- " # but one could use a prior from a different time from another run\n",
- " # i.e. 13z as a prior to assimilate 12z observations\n",
- " prior_valid_time = time\n",
- "\n",
- " id = w.assimilate(time, prior_init_time, prior_valid_time, prior_path_exp, depends_on=id)\n",
- "\n",
- " # 1) Set posterior = prior\n",
- " id = w.prepare_IC_from_prior(prior_path_exp, prior_init_time, prior_valid_time, depends_on=id)\n",
- "\n",
- " # 2) Update posterior += updates from assimilation\n",
- " id = w.update_IC_from_DA(time, depends_on=id)\n",
- "\n",
- " # How long shall we integrate?\n",
- " timedelta_integrate = timedelta_btw_assim\n",
- " output_restart_interval = timedelta_btw_assim.total_seconds()/60\n",
- " if time == last_assim_time: #this_forecast_init.minute in [0,]: # longer forecast every full hour\n",
- " timedelta_integrate = forecast_until - last_assim_time # dt.timedelta(hours=4)\n",
- " output_restart_interval = 9999 # no restart file after last assim\n",
- "\n",
- " # 3) Run WRF ensemble\n",
- " id = w.run_ENS(begin=time, # start integration from here\n",
- " end=time + timedelta_integrate, # integrate until here\n",
- " output_restart_interval=output_restart_interval,\n",
- " depends_on=id)\n",
- "\n",
- " # as we have WRF output, we can use own exp path as prior\n",
- " prior_path_exp = cluster.archivedir \n",
- "\n",
- " id_sat = w.create_satimages(time, depends_on=id)\n",
- "\n",
- " # increment time\n",
- " time += timedelta_btw_assim\n",
- "\n",
- " # update time variables\n",
- " prior_init_time = time - timedelta_btw_assim\n",
- " \n",
- "w.verify_sat(id_sat)\n",
- "w.verify_wrf(id)\n",
- "w.verify_fast(id)\n",
- "```\n",
- "\n",
- "#### Job scheduling status\n",
- "If you work on a server with a queueing system, the script submits jobs into the SLURM queue with dependencies so that SLURM starts the jobs itself as soon as resources are available. Most jobs need only a few cores, but model integration is done across many nodes. You can look at the status with\n",
- "```bash\n",
- "$ squeue -u `whoami` --sort=i\n",
- " JOBID PARTITION NAME USER ST TIME NODES NODELIST(REASON)\n",
- " 1710274 mem_0384 prepwrfr lkugler PD 0:00 1 (Priority)\n",
- " 1710275 mem_0384 IC-prior lkugler PD 0:00 1 (Dependency)\n",
- " 1710276 mem_0384 Assim-42 lkugler PD 0:00 1 (Dependency)\n",
- " 1710277 mem_0384 IC-prior lkugler PD 0:00 1 (Dependency)\n",
- " 1710278 mem_0384 IC-updat lkugler PD 0:00 1 (Dependency)\n",
- " 1710279 mem_0384 preWRF2- lkugler PD 0:00 1 (Dependency)\n",
- " 1710280_[1-10] mem_0384 runWRF2- lkugler PD 0:00 1 (Dependency)\n",
- " 1710281 mem_0384 pRTTOV-6 lkugler PD 0:00 1 (Dependency)\n",
- " 1710282 mem_0384 Assim-3a lkugler PD 0:00 1 (Dependency)\n",
- " 1710283 mem_0384 IC-prior lkugler PD 0:00 1 (Dependency)\n",
- " 1710284 mem_0384 IC-updat lkugler PD 0:00 1 (Dependency)\n",
- " 1710285 mem_0384 preWRF2- lkugler PD 0:00 1 (Dependency)\n",
- " 1710286_[1-10] mem_0384 runWRF2- lkugler PD 0:00 1 (Dependency)\n",
- " 1710287 mem_0384 pRTTOV-7 lkugler PD 0:00 1 (Dependency)\n",
- "```\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "400244f1-098b-46ea-b29d-2226c7cbc827",
- "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",
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- "pygments_lexer": "ipython3",
- "version": "3.10.9"
- }
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-}