App for building and running the MPAS-Model
Clone the app and navigate to its directory:
git clone https://github.com/NOAA-GSL/mpas_app.git --recursive
cd mpas_app
If you forget the --recursive flag when you clone, or if you switch branches on mpas_app, from the clone:
git submodule update --init --recursive
Currently Hera and Jet are the only platforms supported. To run the default build script:
./build.sh -p=<platform>
To see the different build options (including MPAS build options):
./build.sh -h
This builds the MPAS-Model and installs Miniconda inside the local clone. The ungrib conda environment installed in the process includes a pre-built package to run WPS Ungrib tool.
default_config.yaml is the default yaml config file located in the ush directory of mpas_app.
The grid_files field references the decomposed domain files from the previous step.
The fields under prepare_ungrib will retrieve whatever data you need for GFS initial conditions and lateral boundary conditions from AWS by default, and will ungrib them.
Next, the create_ics part of the workflow creates the MPAS initial conditions using 4 cores and copies and links the files needed from when the model was built. It also updates the init_atmosphere namelist. Additional files like the runtime tables from the MPAS physics_wrf/files directory will go in this section of your user config yaml. The input/output file names are modified in the streams: field and the keys correspond to the template in the parm/ directory.
A similar process is followed to create the lateral boundary conditions in the create_lbcs part of the workflow, the namelist and streams fields can be modified in the user config yaml.
Finally, the forecast step runs the MPAS atmosphere executable. If you want to add additional physics, you would add them in the physics field of the atmosphere namelist user config (see below).
Your user config (e.g. <your_name>.yaml) is how you update the default configuration with different settings. Rather than going through and changing all of the different namelist and streams files that the MPAS Model produces, you only need to create and update the single user config file in the ush directory. The file itself can be as simple as:
user:
experiment_dir: /path/to/exp/dir
platform: jet
platform:
account: wrfruc
To update additional fields, you add the nested structure from default_config.yaml with the additional information. For example, to modify the physics for the atmosphere executable to include Thompson microphysics, you would add the following to the user config yaml:
forecast:
mpas:
namelist:
update_values:
physics:
config_microp_scheme = 'mp_thompson'
Prior to running the experiment, you must run the command source load_wflow_modules.sh <platform> from the mpas_app directory.
When you have a completed user config yaml, you can run the experiment_gen python script to generate the MPAS experiment:
python experiment_gen.py <user_config.yaml>
This will create an experiment directory with your experiment.yaml file, which contains the user modifications to the default yaml. The experiment directory also contains a Rocoto XML file, which is ready to use with the command rocotorun -w rocoto.xml -d rocoto.db. You will have to iteratively run this command until all steps have been completed. You can check the status of these steps by running rocotostat -w rocoto.xml -d rocoto.db.
Logs are populated for each of the different tasks in the workflow, and workflow.log contains the submission and completion statuses in text format.
To remap the model output to a lat/lon grid you can copy the convert_mpas executable to the directory with the model output:
cp /lfs4/BMC/wrfruc/jderrico/mpas/exec/convert_mpas
The convert_mpas executable requires an additional include_fields file and a target_domain file, more information can be found here.