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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.
%% 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:
```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
exp.cov_loc_vert_km_horiz_km = (3, 20) exp.cov_loc_vert_km_horiz_km = (3, 20)
``` ```
In case you want to generate new observations (observing system simulations experiment, OSSE), set `sxp.use_existing_obsseq = False`. In case you want to generate new observations (observing system simulations experiment, OSSE), set `sxp.use_existing_obsseq = False`.
`exp.nature` defines where observations will be drawn from, e.g.: `exp.nature` defines where observations will be drawn from, e.g.:
```python ```python
exp.nature = '/mnt/jetfs/scratch/lkugler/data/sim_archive/exp_v1.18_P1_nature/2008-07-30_06:00/1' exp.nature = '/mnt/jetfs/scratch/lkugler/data/sim_archive/exp_v1.18_P1_nature/2008-07-30_06:00/1'
``` ```
`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 = '/mnt/jetfs/home/lkugler/data/initial_profiles/wrf/ens/2022-03-31/raso.fc.<iens>.wrfprof' exp.input_profile = '/mnt/jetfs/home/lkugler/data/initial_profiles/wrf/ens/2022-03-31/raso.fc.<iens>.wrfprof'
``` ```
Vertical localization is tricky to set. Vertical localization is tricky to set.
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
`exp.cov_loc_vert_km_horiz_km = (3, 20)` `exp.cov_loc_vert_km_horiz_km = (3, 20)`
You can also set it to zero for no vertical localization. You can also set it to zero for no vertical localization.
Set you desired observations like this. Set you 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, obs_locations=[(45., 0.)], n_obs=1, obs_locations=[(45., 0.)],
error_generate=0.2, error_assimilate=0.2, error_generate=0.2, error_assimilate=0.2,
heights=[1000,], # range(1000, 17001, 2000), heights=[1000,], # range(1000, 17001, 2000),
cov_loc_radius_km=50) cov_loc_radius_km=50)
exp.observations = [t,] exp.observations = [t,]
``` ```
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 with a prior given by previous forecasts ### 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 = '/mnt/jetfs/scratch/lkugler/data/sim_archive/exp_v1.19_P3_wbub7_noDA' prior_path_exp = '/mnt/jetfs/scratch/lkugler/data/sim_archive/exp_v1.19_P3_wbub7_noDA'
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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