DART-WRF

This code runs an Ensemble Data Assimilation system with the software packages DART and WRF. All workflow steps are submitted to the cluster manager SLURM, which takes care of the dependencies (the order in which tasks are done).

• Why should I use it?

  • It's pythonic: see what it does at first glance, modular, flexible
  • It handles dependencies using SLURM without 'sleep loops in the bash script'. Functions return a SLURM ID which can be used to trigger the start of another function (interface by brentp/slurmpy).

• Can I use it for real weather?

  • Yes, but you need to convert your observations into DART format.

A possible workflow:

scheduler.py

### define your functions gen_synth_obs, assimilate, run_ENS, ...

# create initial conditions
id = prep_osse()  

# spin up the ensemble
background_init_time = dt.datetime(2008, 7, 30, 6, 0)
integration_end_time = dt.datetime(2008, 7, 30, 11, 0)
id = run_ENS(begin=background_init_time,
             end=integration_end_time,
             depends_on=id)
             
time = integration_end_time  # time now

# now, start the ensemble data assimilation cycles
timedelta_integrate = dt.timedelta(minutes=15)
timedelta_btw_assim = dt.timedelta(minutes=15)

while time < dt.datetime(2008, 7, 30, 16, 15):
     assim_time = time
     id = gen_synth_obs(assim_time, depends_on=id)
     id = assimilate(assim_time,
                     background_init_time,
                     depends_on=id)

     background_init_time = assim_time  # start integration now
     integration_end_time = assim_time + timedelta_integrate
     id = run_ENS(begin=background_init_time,
                  end=integration_end_time,
                  depends_on=id)

     time += timedelta_btw_assim

SLURM submissions

scheduler.py 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 one node, but model integration is done in a SLURM job array across e.g. 5 nodes:

$ squeue -u `whoami` --sort=i
            308377  mem_0384 ideal-01  lkugler PD       0:00      1 (Resources)
            308378  mem_0384 prerun-a  lkugler PD       0:00      1 (Priority)
      308379_[1-5]  mem_0384 EnsWRF-3  lkugler PD       0:00      1 (Dependency)
            308380  mem_0384 pregensy  lkugler PD       0:00      1 (Dependency)
            308381  mem_0384 gensynth  lkugler PD       0:00      1 (Dependency)
            308382  mem_0384 preassim  lkugler PD       0:00      1 (Dependency)
            308383  mem_0384 assim-37  lkugler PD       0:00      1 (Dependency)
            308384  mem_0384 postassi  lkugler PD       0:00      1 (Dependency)
            308385  mem_0384 prerun-e  lkugler PD       0:00      1 (Dependency)
      308386_[1-5]  mem_0384 EnsWRF-3  lkugler PD       0:00      1 (Dependency)
            308387  mem_0384 pregensy  lkugler PD       0:00      1 (Dependency)
            308388  mem_0384 gensynth  lkugler PD       0:00      1 (Dependency)
            308389  mem_0384 preassim  lkugler PD       0:00      1 (Dependency)
            308390  mem_0384 assim-37  lkugler PD       0:00      1 (Dependency)
            308391  mem_0384 postassi  lkugler PD       0:00      1 (Dependency)
            308392  mem_0384 prerun-6  lkugler PD       0:00      1 (Dependency)
      308393_[1-5]  mem_0384 EnsWRF-3  lkugler PD       0:00      1 (Dependency)

Configure your experiment

Define simulation specific variables in config/cfg.py.

Easily switch between clusters

Define cluster specific variables in config/clusters.py :

clusterA = ClusterConfig()
clusterA.name = 'vsc'
clusterA.userdir = '/home/pathA/myuser/'
...
clusterB = ClusterConfig()
clusterB.name = 'jet'
clusterB.userdir = '/home/pathB/myuser/'

References

This workflow was created following the DART-WRF Tutorial. DART is available at github: @NCAR/DART

License

This repo is licensed under Apache License 2.0

@NCAR/DART is licensed under the Apache License, Version 2.0 Copyright 2019 University Corporation for Atmospheric Research