Merge branch 'master' of https://github.com/lkugler/DART-WRF

00a19a3f · lkugler · 93f4573c · 40f3a60e · 93f4573c · 00a19a3f
Commit 00a19a3f authored 2 years ago by lkugler
--- a/docs/source/api.rst
+++ b/docs/source/api.rst
-.. autosummary::
-   :toctree: generated
-
-   dartwrf
-
--- a/docs/source/dartwrf.rst
+++ b/docs/source/dartwrf.rst
@@ -13,6 +13,14 @@ Subpackages
 Submodules
 ----------

+dartwrf.workflows module
+------------------------
+
+.. automodule:: dartwrf.workflows
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
 dartwrf.assim\_synth\_obs module
 --------------------------------

@@ -141,14 +149,6 @@ dartwrf.utils module
   :undoc-members:
   :show-inheritance:

-dartwrf.workflows module
------------------------
-
-.. automodule:: dartwrf.workflows
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
 dartwrf.wrfinput\_add\_geo module
 ---------------------------------


--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -29,12 +29,19 @@ Other helpful resources

 .. toctree::
   :maxdepth: 2
-   :caption: Tutorials:
+   :caption: Tutorials

   notebooks/tutorial1
   notebooks/tutorial2
   notebooks/tutorial3
-
+   
+.. toctree::
+   :hidden:
+   :caption: Reference
+   
+   modules
+   genindex 
+   

 API
 ===
@@ -42,5 +49,3 @@ API
 * :ref:`genindex`
 * :ref:`modindex`
 * :ref:`search`
-
-
--- a/docs/source/modules.rst
+++ b/docs/source/modules.rst
-dartwrf
-=======
+API Reference
+=============

 .. toctree::
   :maxdepth: 4

--- a/docs/source/notebooks/tutorial2.ipynb
+++ b/docs/source/notebooks/tutorial2.ipynb
@@ -24,11 +24,11 @@
    "\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",
+    "exp.input_profile = '/users/students/lehre/advDA_s2023/data/initial_profiles/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",
+    "Then, we set up the workflows as usual and prepare the input profiles.\n",
    "```python\n",
    "import datetime as dt\n",
    "from dartwrf.workflows import WorkFlows\n",
@@ -78,13 +78,13 @@
    "\n",
    "1. Prepare initial conditions from a prior forecast:\n",
    "```python\n",
-    "id = w.prepare_IC_from_prior(prior_path_exp, prior_init_time, prior_valid_time, depends_on=id)\n",
+    "w.prepare_IC_from_prior(prior_path_exp, prior_init_time, prior_valid_time)\n",
    "```\n",
    "\n",
    "\n",
    "2. Update the initial conditions from data assimilation:\n",
    "```python\n",
-    "id = w.update_IC_from_DA(time, depends_on=id)\n",
+    "w.update_IC_from_DA(time)\n",
    "```\n",
    "\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",
@@ -107,7 +107,7 @@
    "timedelta_btw_assim = dt.timedelta(minutes=15)\n",
    "output_restart_interval = timedelta_btw_assim.total_seconds()/60\n",
    "\n",
-    "id = w.run_ENS(begin=time,  # start integration from here\n",
+    "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",
    "               )\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

 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'
+exp.input_profile = '/users/students/lehre/advDA_s2023/data/initial_profiles/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`.
+Then, we set up the workflows as usual and prepare the input profiles.
 ```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)
+w.prepare_IC_from_prior(prior_path_exp, prior_init_time, prior_valid_time)
 ```


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

 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
+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
 ```