update

437b4111 · lkugler · 1690f8d5 · 437b4111
Commit 437b4111 authored 2 years ago by lkugler
--- a/docs/source/notebooks/tutorial2.ipynb
+++ b/docs/source/notebooks/tutorial2.ipynb
@@ -14,6 +14,7 @@
    "[free_forecast.py ](https://github.com/lkugler/DART-WRF/blob/master/free_forecast.py) contains examples.\n",
    "\n",
    "Initialize the forecast with either (1) or (2). Run the forecast with (3)\n",
+    "\n",
    "1) Initialize a forecast from defined profiles of temperature, humidity and wind, i.e. from a `wrf_profile` (see WRF guide)\n",
    "2) Initialize a forecast from an existing forecast, i.e. from WRF restart files - optionally with updates from data assimilation.\n",
    "3) Run the forecast\n",
@@ -21,7 +22,29 @@
    "\n",
    "### 1) Initialize from sounding profiles\n",
    "\n",
-    "tutorial missing\n",
+    "It is necessary to set the path to the prepared WRF input soundings in `config/cfg.py` like this\n",
+    "```python\n",
+    "exp.input_profile = '/user/test/data/initial_profiles/wrf/ens/raso.fc.<iens>.wrfprof'\n",
+    "```\n",
+    "where `<iens>` is a placeholder, since there is one file for each member, from `raso.fc.001.wrfprof` to `raso.fc.040.wrfprof`.\n",
+    "\n",
+    "Then, we set up the workflows as usual and prepare the input profiles using `prepare_WRFrundir`.\n",
+    "```python\n",
+    "import datetime as dt\n",
+    "from dartwrf.workflows import WorkFlows\n",
+    "\n",
+    "w = WorkFlows(exp_config='cfg.py', server_config='srvx1.py')\n",
+    "\n",
+    "begin = dt.datetime(2008, 7, 30, 7)\n",
+    "w.prepare_WRFrundir(begin)\n",
+    "```\n",
+    "\n",
+    "Finally, the WRF's ideal.exe program is called for all ensemble members to create initial condition files, called `wrfinput_d01`, for each member.\n",
+    "```python\n",
+    "w.run_ideal()\n",
+    "```\n",
+    "Now we can go to step 3 to run the forecast.\n",
+    "\n",
    "\n",
    "### 2) Initialize a forecast from a previous forecast\n",
    "To run a forecast from initial conditions of a previous forecasts, we import these modules\n",

 %% Cell type:markdown id:fd5c3005-f237-4495-9185-2d4d474cafd5 tags:

 # Tutorial 2: Forecast after DA


 **Goal**: To run an ensemble of forecasts.
 [free_forecast.py ](https://github.com/lkugler/DART-WRF/blob/master/free_forecast.py) contains examples.

 Initialize the forecast with either (1) or (2). Run the forecast with (3)
+
 1) Initialize a forecast from defined profiles of temperature, humidity and wind, i.e. from a `wrf_profile` (see WRF guide)
 2) Initialize a forecast from an existing forecast, i.e. from WRF restart files - optionally with updates from data assimilation.
 3) Run the forecast


 ### 1) Initialize from sounding profiles

-tutorial missing
+It is necessary to set the path to the prepared WRF input soundings in `config/cfg.py` like this
+```python
+exp.input_profile = '/user/test/data/initial_profiles/wrf/ens/raso.fc.<iens>.wrfprof'
+```
+where `<iens>` is a placeholder, since there is one file for each member, from `raso.fc.001.wrfprof` to `raso.fc.040.wrfprof`.
+
+Then, we set up the workflows as usual and prepare the input profiles using `prepare_WRFrundir`.
+```python
+import datetime as dt
+from dartwrf.workflows import WorkFlows
+
+w = WorkFlows(exp_config='cfg.py', server_config='srvx1.py')
+
+begin = dt.datetime(2008, 7, 30, 7)
+w.prepare_WRFrundir(begin)
+```
+
+Finally, the WRF's ideal.exe program is called for all ensemble members to create initial condition files, called `wrfinput_d01`, for each member.
+```python
+w.run_ideal()
+```
+Now we can go to step 3 to run the forecast.
+

 ### 2) Initialize a forecast from a previous forecast
 To run a forecast from initial conditions of a previous forecasts, we import these modules
 ```python
 import datetime as dt
 from dartwrf.workflows import WorkFlows
 ```

 Let's say you want to run a forecast starting at 9 UTC until 12 UTC.
 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.
 Then the code would be

 ```python
 prior_path_exp = '/user/test/data/sim_archive/exp_abc'
 prior_init_time = dt.datetime(2008,7,30,6)
 prior_valid_time = dt.datetime(2008,7,30,9)

 w = WorkFlows(exp_config='cfg.py', server_config='srvx1.py')

 begin = dt.datetime(2008, 7, 30, 9)
 end = dt.datetime(2008, 7, 30, 12)

 w.prepare_WRFrundir(begin)

 w.prepare_IC_from_prior(prior_path_exp, prior_init_time, prior_valid_time)
 ```

 #### 2b) Optional: Update posterior with increments from assimilation

 In order to continue a forecast after assimilation you need the posterior = prior (1) + increments (2)

 1. Prepare initial conditions from a prior forecast:
 ```python
 id = w.prepare_IC_from_prior(prior_path_exp, prior_init_time, prior_valid_time, depends_on=id)
 ```


 2. Update the initial conditions from data assimilation:
 ```python
 id = w.update_IC_from_DA(time, depends_on=id)
 ```

 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.

 ### 3) Run the forecast
 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.

 ```python
 timedelta_integrate = dt.timedelta(hours=5)

 w.run_ENS(begin=begin,  # start integration from here
          end=time + timedelta_integrate,  # integrate until here
          output_restart_interval=9999,  # do not write WRF restart files
          )
 ```

 If you want to assimilate in 15 minutes again, use
 ```python
 timedelta_integrate = dt.timedelta(hours=5)
 timedelta_btw_assim = dt.timedelta(minutes=15)
 output_restart_interval = timedelta_btw_assim.total_seconds()/60

 id = w.run_ENS(begin=time,  # start integration from here
               end=time + timedelta_integrate,  # integrate until here
               output_restart_interval=output_restart_interval
               )
 ```

 %% Cell type:code id:400244f1-098b-46ea-b29d-2226c7cbc827 tags:

 ``` python
 ```