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docs/source/index.rst
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self
tutorial1
tutorial2
tutorial3
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docs/source/tutorial2.ipynb
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%% Cell type:markdown id:fd5c3005-f237-4495-9185-2d4d474cafd5 tags:
# Tutorial 2: Cycled experiment
**Goal**: To run a cycled data assimilation experiment.
[`cycled_exp.py`](https://github.com/lkugler/DART-WRF/blob/master/generate_free.py) contains an example which will be explained here:
#### Configure your experiment
See tutorial 1.
We start again by configuring `config/cfg.py`.
#### Prepare initial conditions from previous forecasts
Before starting, let's set up the directory structure with
Then we write a script (or edit an existing one) in the main directory `DART-WRF/`.
`nano new_experiment.py`
---
Any script needs to import some modules:
```python
begin = dt.datetime(2008, 7, 30, 6)
prepare_WRFrundir(begin)
import os, sys, shutil
import datetime as dt
from dartwrf import utils
from config.cfg import exp
from config.clusters import cluster
```
#### Run a forecast
Let's say you
- want to run a forecast starting at 6 UTC until 12 UTC
- do not want WRF restart files
---
Now there are two options:
- To start a forecast from an existing forecast, i.e. from WRF restart files
- To start a forecast from defined thermodynamic profiles, i.e. from a `wrf_profile`
#### Run a forecast from initial conditions of a previous forecasts
Let's say you want to run a forecast starting at 9 UTC until 12 UTC.
Initial conditions shall be taken from a previous experiment in `/user/test/data/sim_archive/exp_abc` which was initialized at 6 UTC and there are WRF restart files for 9 UTC.
Then the code would be
then the required code is
```python
begin = dt.datetime(2008, 7, 30, 6)
prior_path_exp = '/user/test/data/sim_archive/exp_abc'
prior_init_time = dt.datetime(2008,7,30,6)
prior_valid_time = dt.datetime(2008,7,30,9)
cluster.setup()
begin = dt.datetime(2008, 7, 30, 9)
end = dt.datetime(2008, 7, 30, 12)
id = run_ENS(begin=begin, # start integration from here
end=end, # integrate until here
output_restart_interval=9999, # do not write WRF restart files
depends_on=id)
prepare_WRFrundir(begin)
prepare_IC_from_prior(prior_path_exp, prior_init_time, prior_valid_time)
run_ENS(begin=begin, # start integration from here
end=end, # integrate until here
output_restart_interval=9999, # do not write WRF restart files
)
```
Note that `begin` and `end` are `dt.datetime` objects.
Note that we use predefined workflow functions like `run_ENS`.
#### Assimilate
#### Assimilate observations with a prior given by previous forecasts
To assimilate observations at dt.datetime `time` use this command:
`id = assimilate(time, prior_init_time, prior_valid_time, prior_path_exp, depends_on=id)`
```python
prior_path_exp = '/user/test/data/sim_archive/exp_abc'
prior_init_time = dt.datetime(2008,7,30,6)
prior_valid_time = dt.datetime(2008,7,30,9)
time = dt.datetime(2008,7,30,9) # time of assimilation
assimilate(time, prior_init_time, prior_valid_time, prior_path_exp)
```
#### Update initial conditions from Data Assimilation
In order to continue after assimilation you need the posterior = prior (1) + increments (2)
1. Set prior with this function:
`id = prepare_IC_from_prior(prior_path_exp, prior_init_time, prior_valid_time, depends_on=id)`
where path is `str`, times are `dt.datetime`.
2. To update the model state with assimilation increments, you need to update the WRF restart files by running
`id = update_IC_from_DA(time, depends_on=id)`
After this, the wrfrst files are updated with assimilation increments (filter_restart) and copied to the WRF's run directories so you can continue to run the ENS after assimilation using function `run_ENS()`.
#### Cycled data assimilation code
Then the loop looks like
#### Job scheduling status
The script submits jobs into the SLURM queue with dependencies so that SLURM starts the jobs itself as soon as resources are available. Most jobs need only a few cores, but model integration is done across many nodes:
```bash
$ squeue -u `whoami` --sort=i
JOBID PARTITION NAME USER ST TIME NODES NODELIST(REASON)
1710274 mem_0384 prepwrfr lkugler PD 0:00 1 (Priority)
1710275 mem_0384 IC-prior lkugler PD 0:00 1 (Dependency)
1710276 mem_0384 Assim-42 lkugler PD 0:00 1 (Dependency)
1710277 mem_0384 IC-prior lkugler PD 0:00 1 (Dependency)
1710278 mem_0384 IC-updat lkugler PD 0:00 1 (Dependency)
1710279 mem_0384 preWRF2- lkugler PD 0:00 1 (Dependency)
1710280_[1-10] mem_0384 runWRF2- lkugler PD 0:00 1 (Dependency)
1710281 mem_0384 pRTTOV-6 lkugler PD 0:00 1 (Dependency)
1710282 mem_0384 Assim-3a lkugler PD 0:00 1 (Dependency)
1710283 mem_0384 IC-prior lkugler PD 0:00 1 (Dependency)
1710284 mem_0384 IC-updat lkugler PD 0:00 1 (Dependency)
1710285 mem_0384 preWRF2- lkugler PD 0:00 1 (Dependency)
1710286_[1-10] mem_0384 runWRF2- lkugler PD 0:00 1 (Dependency)
1710287 mem_0384 pRTTOV-7 lkugler PD 0:00 1 (Dependency)
```
%% Cell type:code id:400244f1-098b-46ea-b29d-2226c7cbc827 tags:
``` python
```
......
docs/source/tutorial3.ipynb 0 → 100644
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%% Cell type:markdown id:fd5c3005-f237-4495-9185-2d4d474cafd5 tags:
# Tutorial 3: Cycled experiment
**Goal**: To run a cycled data assimilation experiment.
[`cycled_exp.py`](https://github.com/lkugler/DART-WRF/blob/master/generate_free.py) contains an example which will be explained here:
After configuring your experiment the loop looks like
```python
prior_path_exp = '/jetfs/home/lkugler/data/sim_archive/exp_v1.19_P2_noDA'
init_time = dt.datetime(2008, 7, 30, 13)
time = dt.datetime(2008, 7, 30, 14)
last_assim_time = dt.datetime(2008, 7, 30, 14)
forecast_until = dt.datetime(2008, 7, 30, 14, 15)
prepare_WRFrundir(init_time)
id = run_ideal(depends_on=id)
prior_init_time = init_time
prior_valid_time = time
while time <= last_assim_time:
# usually we take the prior from the current time
# but one could use a prior from a different time from another run
# i.e. 13z as a prior to assimilate 12z observations
prior_valid_time = time
id = assimilate(time, prior_init_time, prior_valid_time, prior_path_exp, depends_on=id)
# 1) Set posterior = prior
id = prepare_IC_from_prior(prior_path_exp, prior_init_time, prior_valid_time, depends_on=id)
# 2) Update posterior += updates from assimilation
id = update_IC_from_DA(time, depends_on=id)
# How long shall we integrate?
timedelta_integrate = timedelta_btw_assim
output_restart_interval = timedelta_btw_assim.total_seconds()/60
if time == last_assim_time: #this_forecast_init.minute in [0,]: # longer forecast every full hour
timedelta_integrate = forecast_until - last_assim_time # dt.timedelta(hours=4)
output_restart_interval = 9999 # no restart file after last assim
# 3) Run WRF ensemble
id = run_ENS(begin=time, # start integration from here
end=time + timedelta_integrate, # integrate until here
output_restart_interval=output_restart_interval,
depends_on=id)
# as we have WRF output, we can use own exp path as prior
prior_path_exp = cluster.archivedir
id_sat = create_satimages(time, depends_on=id)
# increment time
time += timedelta_btw_assim
# update time variables
prior_init_time = time - timedelta_btw_assim
verify_sat(id_sat)
verify_wrf(id)
verify_fast(id)
```
#### Job scheduling status
The script submits jobs into the SLURM queue with dependencies so that SLURM starts the jobs itself as soon as resources are available. Most jobs need only a few cores, but model integration is done across many nodes:
```bash
$ squeue -u `whoami` --sort=i
JOBID PARTITION NAME USER ST TIME NODES NODELIST(REASON)
1710274 mem_0384 prepwrfr lkugler PD 0:00 1 (Priority)
1710275 mem_0384 IC-prior lkugler PD 0:00 1 (Dependency)
1710276 mem_0384 Assim-42 lkugler PD 0:00 1 (Dependency)
1710277 mem_0384 IC-prior lkugler PD 0:00 1 (Dependency)
1710278 mem_0384 IC-updat lkugler PD 0:00 1 (Dependency)
1710279 mem_0384 preWRF2- lkugler PD 0:00 1 (Dependency)
1710280_[1-10] mem_0384 runWRF2- lkugler PD 0:00 1 (Dependency)
1710281 mem_0384 pRTTOV-6 lkugler PD 0:00 1 (Dependency)
1710282 mem_0384 Assim-3a lkugler PD 0:00 1 (Dependency)
1710283 mem_0384 IC-prior lkugler PD 0:00 1 (Dependency)
1710284 mem_0384 IC-updat lkugler PD 0:00 1 (Dependency)
1710285 mem_0384 preWRF2- lkugler PD 0:00 1 (Dependency)
1710286_[1-10] mem_0384 runWRF2- lkugler PD 0:00 1 (Dependency)
1710287 mem_0384 pRTTOV-7 lkugler PD 0:00 1 (Dependency)
```
%% Cell type:code id:400244f1-098b-46ea-b29d-2226c7cbc827 tags:
``` python
```
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