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%% 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 experiment
## 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']
```
In case you want to generate new observations like for an observing system simulations experiment, OSSE), set
### 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
```
Else, you can use pre-existing observation files:
```python
exp.use_existing_obsseq = '/users/students/lehre/advDA_s2023/dartwrf_tutorial/very_cold_observation.out'
```
`exp.nature` defines the path from where observations can be generated (necessary if `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'
```
`exp.input_profile` is used, if you create initial conditions from a so called wrf_profile (see WRF guide).
### Using pre-existing observation files
You can use pre-existing observation files with
```python
exp.input_profile = '/doesnt_exist/initial_profiles/wrf/ens/raso.fc.<iens>.wrfprof'
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.
#### Single observation
### 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
```
#### Multiple observations
### 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 = [vis,]
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 hardware
In case you use a cluster which is not supported, configure paths inside `config/clusters.py`.
### Assimilate observations
## 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
```
......
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