diff --git a/docs/source/tutorial1.ipynb b/docs/source/tutorial1.ipynb
index 798d6b941dc14d3bca583e5c14f92de2b8c18fdb..f425edbfe6521bad6b6ae9f8e88d3a51f8c15d65 100644
--- a/docs/source/tutorial1.ipynb
+++ b/docs/source/tutorial1.ipynb
@@ -2,144 +2,28 @@
  "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": {},
+   "id": "09371891-3ca3-404e-aeb3-7b58b900f563",
+   "metadata": {
+    "tags": []
+   },
    "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."
+    "# Test1"
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "16bd3521-f98f-4c4f-8019-31029fd678ae",
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "ab8f6d86-dc41-4d5c-b6d9-a9f396c1ec70",
    "metadata": {},
+   "outputs": [],
    "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!"
+    "import os, sys"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "82e809a8-5972-47f3-ad78-6290afe4ae17",
+   "id": "3bc1d633-cf91-46ad-96d2-256dda6fa335",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/docs/source/tutorial2.ipynb b/docs/source/tutorial2.ipynb
index df58b78b35dd5099e742d2e50933d4395150e716..74576f45257c7f7b0f6fc111bbd41461dcce1c29 100644
--- a/docs/source/tutorial2.ipynb
+++ b/docs/source/tutorial2.ipynb
@@ -2,100 +2,23 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "fd5c3005-f237-4495-9185-2d4d474cafd5",
+   "id": "02ac2a1f-272e-4f23-960d-e7f95d683b53",
    "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"
+    "# Test2"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "400244f1-098b-46ea-b29d-2226c7cbc827",
+   "execution_count": 1,
+   "id": "afdee309-2ee0-418d-89db-47d057386a29",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "import os, sys"
+   ]
   }
  ],
  "metadata": {
diff --git a/docs/source/tutorial3.ipynb b/docs/source/tutorial3.ipynb
index d3018ab95b378770ca3b05b1e2fc9adb547a188e..f88f409d13ec50b58df3fdff437a2c8751107f78 100644
--- a/docs/source/tutorial3.ipynb
+++ b/docs/source/tutorial3.ipynb
@@ -2,107 +2,23 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "fd5c3005-f237-4495-9185-2d4d474cafd5",
+   "id": "8ced2b8b-6829-4b16-acb7-03fd1b8b0ff8",
    "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"
+    "# Test3"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "400244f1-098b-46ea-b29d-2226c7cbc827",
+   "execution_count": 1,
+   "id": "b8d43e28-c9cb-403e-915c-79266b5a03c7",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "import os, sys"
+   ]
   }
  ],
  "metadata": {