Skip to content
Snippets Groups Projects
Commit b7a77743 authored by lkugler's avatar lkugler
Browse files

docs

parent 11922cc4
No related branches found
No related tags found
No related merge requests found
Hide whitespace changes
Inline Side-by-side
docs/source/notebooks/tutorial1.ipynb
+ 18
− 17
View file @ b7a77743
%% 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
```
......
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Please register or to comment