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%% Cell type:markdown id:fd5c3005-f237-4495-9185-2d4d474cafd5 tags: %% Cell type:markdown id:fd5c3005-f237-4495-9185-2d4d474cafd5 tags:
# Tutorial 1: The assimilation step # 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. 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. The data for this experiment is accessible for students on the server srvx1.
%% Cell type:markdown id:93d59d4d-c514-414e-81fa-4ff390290811 tags: %% Cell type:markdown id:93d59d4d-c514-414e-81fa-4ff390290811 tags:
### Configuring the experiment ### Configuring the experiment
Firstly, you need to configure the experiment in `config/cfg.py`. Firstly, you need to configure the experiment in `config/cfg.py`.
Let's go through the most important settings: Let's go through the most important settings:
- expname should be a unique identifier and will be used as folder name - expname should be a unique identifier and will be used as folder name
- model_dx is the model resolution in meters - model_dx is the model resolution in meters
- n_ens is the ensemble size - n_ens is the ensemble size
- update_vars are the WRF variables which shall be updated by the assimilation - update_vars are the WRF variables which shall be updated by the assimilation
- filter_kind is 1 for the EAKF (see the DART documentation for more) - filter_kind is 1 for the EAKF (see the DART documentation for more)
- prior and post_inflation defines what inflation we want (see the DART docs) - prior and post_inflation defines what inflation we want (see the DART docs)
- sec is the statistical sampling error correction from Anderson (2012) - sec is the statistical sampling error correction from Anderson (2012)
```python ```python
exp = utils.Experiment() exp = utils.Experiment()
exp.expname = "test_newcode" exp.expname = "test_newcode"
exp.model_dx = 2000 exp.model_dx = 2000
exp.n_ens = 10 exp.n_ens = 10
exp.update_vars = ['U', 'V', 'W', 'THM', 'PH', 'MU', 'QVAPOR', 'QCLOUD', 'QICE', 'PSFC'] exp.update_vars = ['U', 'V', 'W', 'THM', 'PH', 'MU', 'QVAPOR', 'QCLOUD', 'QICE', 'PSFC']
exp.filter_kind = 1 exp.filter_kind = 1
exp.prior_inflation = 0 exp.prior_inflation = 0
exp.post_inflation = 4 exp.post_inflation = 4
exp.sec = True exp.sec = True
``` ```
In case you want to generate new observations like for an observing system simulations experiment, OSSE), set In case you want to generate new observations like for an observing system simulations experiment, OSSE), set
```python ```python
exp.use_existing_obsseq = False`. exp.use_existing_obsseq = False`.
``` ```
`exp.nature` defines which WRF files will be used to draw observations from, e.g.: `exp.nature` defines which WRF files will be used to draw observations from, e.g.:
```python ```python
exp.nature = '/users/students/lehre/advDA_s2023/data/sample_nature/' exp.nature = '/users/students/lehre/advDA_s2023/data/sample_nature/'
``` ```
`exp.input_profile` is used, if you create initial conditions from a so called wrf_profile (see WRF guide). `exp.input_profile` is used, if you create initial conditions from a so called wrf_profile (see WRF guide).
```python ```python
exp.input_profile = '/doesnt_exist/initial_profiles/wrf/ens/raso.fc.<iens>.wrfprof' exp.input_profile = '/doesnt_exist/initial_profiles/wrf/ens/raso.fc.<iens>.wrfprof'
``` ```
For horizontal localization half-width of 20 km and 3 km vertically, set For horizontal localization half-width of 20 km and 3 km vertically, set
```python ```python
exp.cov_loc_vert_km_horiz_km = (3, 20) exp.cov_loc_vert_km_horiz_km = (3, 20)
``` ```
You can also set it to False for no vertical localization. You can also set it to False for no vertical localization.
#### Single observation #### Single observation
Set your desired observations like this. Set your desired observations like this.
```python ```python
t = dict(plotname='Temperature', plotunits='[K]', t = dict(plotname='Temperature', plotunits='[K]',
kind='RADIOSONDE_TEMPERATURE', kind='RADIOSONDE_TEMPERATURE',
n_obs=1, # number of observations n_obs=1, # number of observations
obs_locations=[(45., 0.)], # location of observations obs_locations=[(45., 0.)], # location of observations
error_generate=0.2, # observation error used to generate observations error_generate=0.2, # observation error used to generate observations
error_assimilate=0.2, # observation error used for assimilation error_assimilate=0.2, # observation error used for assimilation
heights=[1000,], # for radiosondes, use range(1000, 17001, 2000) heights=[1000,], # for radiosondes, use range(1000, 17001, 2000)
cov_loc_radius_km=50) # horizontal localization half-width cov_loc_radius_km=50) # horizontal localization half-width
exp.observations = [t,] # select observations for assimilation exp.observations = [t,] # select observations for assimilation
``` ```
#### Multiple observations #### Multiple observations
To generate a grid of observations, use To generate a grid of observations, use
```python ```python
vis = dict(plotname='VIS 0.6µm', plotunits='[1]', vis = dict(plotname='VIS 0.6µm', plotunits='[1]',
kind='MSG_4_SEVIRI_BDRF', sat_channel=1, kind='MSG_4_SEVIRI_BDRF', sat_channel=1,
n_obs=961, obs_locations='square_array_evenly_on_grid', n_obs=961, obs_locations='square_array_evenly_on_grid',
error_generate=0.03, error_assimilate=0.03, error_generate=0.03, error_assimilate=0.03,
cov_loc_radius_km=20) cov_loc_radius_km=20)
``` ```
But caution, n_obs should only be one of the following: But caution, n_obs should only be one of the following:
- 22500 for 2km observation density/resolution - 22500 for 2km observation density/resolution
- 5776 for 4km; - 5776 for 4km;
- 961 for 10km; - 961 for 10km;
- 256 for 20km; - 256 for 20km;
- 121 for 30km - 121 for 30km
For vertically resolved data, like radar, n_obs is the number of observations at each observation height level. 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: %% Cell type:markdown id:16bd3521-f98f-4c4f-8019-31029fd678ae tags:
### Configuring the hardware ### Configuring the hardware
In case you use a cluster which is not supported, configure paths inside `config/clusters.py`. In case you use a cluster which is not supported, configure paths inside `config/clusters.py`.
### Assimilate observations ### Assimilate observations
We start by importing some modules: We start by importing some modules:
```python ```python
import datetime as dt import datetime as dt
from dartwrf.workflows import WorkFlows from dartwrf.workflows import WorkFlows
``` ```
To assimilate observations at dt.datetime `time` we set the directory paths and times of the prior ensemble forecasts: To assimilate observations at dt.datetime `time` we set the directory paths and times of the prior ensemble forecasts:
```python ```python
prior_path_exp = '/users/students/lehre/advDA_s2023/data/sample_ensemble/' prior_path_exp = '/users/students/lehre/advDA_s2023/data/sample_ensemble/'
prior_init_time = dt.datetime(2008,7,30,12) prior_init_time = dt.datetime(2008,7,30,12)
prior_valid_time = dt.datetime(2008,7,30,12,30) prior_valid_time = dt.datetime(2008,7,30,12,30)
assim_time = prior_valid_time assim_time = prior_valid_time
``` ```
Finally, we run the data assimilation by calling Finally, we run the data assimilation by calling
```python ```python
w = WorkFlows(exp_config='cfg.py', server_config='srvx1.py') w = WorkFlows(exp_config='cfg.py', server_config='srvx1.py')
w.assimilate(assim_time, prior_init_time, prior_valid_time, prior_path_exp) w.assimilate(assim_time, prior_init_time, prior_valid_time, prior_path_exp)
``` ```
Congratulations! You're done! Congratulations! You're done!
%% Cell type:code id:82e809a8-5972-47f3-ad78-6290afe4ae17 tags: %% Cell type:code id:82e809a8-5972-47f3-ad78-6290afe4ae17 tags:
``` python ``` python
``` ```
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