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fe0bad23 · lkugler · 4056d0ea · fe0bad23
Commit fe0bad23 authored 1 year ago by lkugler
--- a/docs/source/notebooks/tutorial1.ipynb
+++ b/docs/source/notebooks/tutorial1.ipynb
@@ -60,7 +60,7 @@
    "\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",
+    "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",

 %% Cell type:markdown id:fd5c3005-f237-4495-9185-2d4d474cafd5 tags:
 # Tutorial 1: The assimilation step
 DART-WRF is a python package which automates many things like configuration, saving configuration and output, handling computing resources, etc.
 The data for this experiment is accessible for students on the server srvx1.
 %% Cell type:markdown id:93d59d4d-c514-414e-81fa-4ff390290811 tags:
 ## Configuring the hardware
 In case you use a cluster which is not supported, copy an existing cluster configuration and modify it, e.g. `config/jet.py`.
 ## Configuring the experiment
 Firstly, you need to configure the experiment.
 Copy the existing template and modify it `cp config/exp_template.py config/exp1.py`.
 Customize your settings:
 - expname should be a unique identifier and will be used as folder name
 - model_dx is the model resolution in meters
 - n_ens is the ensemble size
 - update_vars are the WRF variables which shall be updated by the assimilation
 ```python
 exp = utils.Experiment()
 exp.expname = "test_newcode"
 exp.model_dx = 2000
 exp.n_ens = 40
 exp.update_vars = ['U', 'V', 'W', 'THM', 'PH', 'MU', 'QVAPOR', 'QCLOUD', 'QICE', 'PSFC']
 ```
 ### Generating observations
 In case you want to generate new observations, like for an observing system simulations experiment (OSSE), set
 ```python
 exp.use_existing_obsseq = False
 ```
 in this case, you need to set the path to WRF nature run files from where DART can generate observations:
 ```python
 exp.nature_wrfout_pattern = '/usr/data/sim_archive/exp_v1_nature/*/1/wrfout_d01_%Y-%m-%d_%H:%M:%S'
 ```
 ### Using pre-existing observation files
 You can use pre-existing observation files with
 ```python
 exp.use_existing_obsseq = '/usr/data/sim_archive/exp_ABC/obs_seq_out/%Y-%m-%d_%H:%M_obs_seq.out'
 ```
 where times are filled, depending on the assimilation time.
 ### Customizing the DART namelist
 By default, the DART namelist of the build directory will be used (copied).
-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/>`_.
+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/).
 ```python
 exp.dart_nml = {'&assim_tools_nml':
                    dict(filter_kind='1',
                         sampling_error_correction='.true.',
                        ),
                '&filter_nml':
                    dict(ens_size=exp.n_ens,
                         num_output_state_members=exp.n_ens,
                         num_output_obs_members=exp.n_ens,
                         inf_flavor=['0', '4'],
                         output_members='.true.',
                         output_mean='.true.',
                         output_sd='.true.',
                         stages_to_write='output',
                        ),
                '&quality_control_nml':
                    dict(outlier_threshold='-1',
                        ),
                '&location_nml':
                    dict(horiz_dist_only='.false.',
                '&model_nml':
                    dict(wrf_state_variables =
                        [['U',     'QTY_U_WIND_COMPONENT',     'TYPE_U',    'UPDATE','999',],
                         ['V',     'QTY_V_WIND_COMPONENT',     'TYPE_V',    'UPDATE','999',],
                         ['W',     'QTY_VERTICAL_VELOCITY',    'TYPE_W',    'UPDATE','999',],
                         ['PH',    'QTY_GEOPOTENTIAL_HEIGHT',  'TYPE_GZ',   'UPDATE','999',],
                         ['THM',   'QTY_POTENTIAL_TEMPERATURE','TYPE_T',    'UPDATE','999',],
                         ['MU',    'QTY_PRESSURE',             'TYPE_MU',   'UPDATE','999',],
                         ['QVAPOR','QTY_VAPOR_MIXING_RATIO',   'TYPE_QV',   'UPDATE','999',],
                         ['QICE',  'QTY_ICE_MIXING_RATIO',     'TYPE_QI',   'UPDATE','999',],
                         ['QCLOUD','QTY_CLOUDWATER_MIXING_RATIO','TYPE_QC', 'UPDATE','999',],
                         ['CLDFRA','QTY_CLOUD_FRACTION',       'TYPE_CFRAC','UPDATE','999',],
                         ['PSFC',  'QTY_SURFACE_PRESSURE',     'TYPE_PSFC', 'UPDATE','999',],
                         ['T2',    'QTY_2M_TEMPERATURE',       'TYPE_T',    'UPDATE','999',],
                         ['TSK',   'QTY_SKIN_TEMPERATURE',     'TYPE_T',    'UPDATE','999',],
                         ['REFL_10CM','QTY_RADAR_REFLECTIVITY','TYPE_REFL', 'UPDATE','999',]]),
                }
 ```
 Any parameters in this dictionary will be overwritten compared to the default namelist.
 ### Single observation experiment
 If you want to assimilate one observation, use
 ```python
 t = dict(plotname='Temperature', plotunits='[K]',
         kind='RADIOSONDE_TEMPERATURE',
         n_obs=1,                    # number of observations
         obs_locations=[(45., 0.)],  # location of observations
         error_generate=0.2,    # observation error used to generate observations
         error_assimilate=0.2,  # observation error used for assimilation
         heights=[1000,],       # for radiosondes, use range(1000, 17001, 2000)
         loc_horiz_km=50,       # horizontal localization half-width
         loc_vert_km=2.5        # vertical localization half-width
        )
 exp.observations = [t,]  # select observations for assimilation
 ```
 ### Assimilating multiple observations
 To generate a grid of observations, use
 ```python
 vis = dict(plotname='VIS 0.6µm', plotunits='[1]',
           kind='MSG_4_SEVIRI_BDRF', sat_channel=1,
           n_obs=961, obs_locations='square_array_evenly_on_grid',
           error_generate=0.03, error_assimilate=0.03,
           loc_horiz_km=50)
 exp.observations = [t, vis,]
 ```
 Caution, n_obs should only be one of the following:
 - 22500 for 2km observation density/resolution
 - 5776 for 4km;
 - 961 for 10km;
 - 256 for 20km;
 - 121 for 30km
 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 tags:
 ## Configuring the assimilation experiment
 We start by importing some modules:
 ```python
 import datetime as dt
 from dartwrf.workflows import WorkFlows
 ```
 To assimilate observations at dt.datetime `time` we set the directory paths and times of the prior ensemble forecasts:
 ```python
 prior_path_exp = '/users/students/lehre/advDA_s2023/data/sample_ensemble/'
 prior_init_time = dt.datetime(2008,7,30,12)
 prior_valid_time = dt.datetime(2008,7,30,12,30)
 assim_time = prior_valid_time
 ```
 To set up the experiment, call
 ```python
 w = WorkFlows(exp_config='exp1.py', server_config='srvx1.py')
 ```
 It will also create the output folders and backup the configuration files and scripts.
 Finally, we run the data assimilation by calling
 ```python
 w.assimilate(assim_time, prior_init_time, prior_valid_time, prior_path_exp)
 ```
 Congratulations! You're done!
 %% Cell type:code id:82e809a8-5972-47f3-ad78-6290afe4ae17 tags:
 ``` python
 ```