WRF

Weather Research and Forecasting (WRF) model is a common atmospheric modeling system developed by NCAR. It is suitable for both meteorological research and operational weather prediction.

Official website: https://www2.mmm.ucar.edu/wrf/users/

Updated: May 2023

Available version

WRF Version	Module name	Note
4.4.2	WRF/4.4.2-DMSM-cpeCray-23.03	Aggressive optimization

WPS Version	Module name	Optimization
4.4	WPS/4.4-DM-cpeCray-23.03	Aggressive optimization

1. Input file

1.1 To run the WRF model, time-dependent meteorological data (global model output/background state) is required. It can be downloaded from, for example, WRF - Free Data and NCEP GFS / GDAS.

1.2 To configure the domain and simulation time, the namelist.wps is needed. A brief description of it can be found here. It is recommended to use the WRF Domain Wizard or the GIS4WRF plug-in for QGIS to define WRF domains.

Users can utilize static geographical data already available on LANTA by specifying
geo_data_path = '/project/common/WPS_Static/geog'

1.3 To run the WRF model, the namelist.input is required. A concise description of the essential parameters can be found here, while the full description is available in chapter 5 of the WRF user's guide.

2. Job submission script

Below is an example of a WRF submission script. It can be created using vi submitWRF.sh. To ensure whole node allocation, please verify that 128 x (Number of nodes) = (Number of MPI processes) x (Number of OpenMP threads per MPI processes).

#!/bin/bash
#SBATCH -p compute             # Partition
#SBATCH -N 1                   # Number of nodes
#SBATCH --ntasks=32            # Number of MPI processes
#SBATCH --cpus-per-task=4      # Number of OpenMP threads per MPI process
#SBATCH -t 02:00:00            # Job runtime limit
#SBATCH -J WRF                 # Job name
#SBATCH -A ltxxxxxx            # Account *** {USER EDIT} *** 

module purge
module load WPS/4.4-DM-cpeCray-23.03
module load WRF/4.4.2-DMSM-cpeCray-23.03

export OMP_STACKSIZE="32M"
export OMP_NUM_THREADS=${SLURM_CPUS_PER_TASK}

ulimit -s unlimited

# *** {USER EDIT} *** #
# Please check that namelist.wps and namelist.input are in this same directory
link_grib /--Path-to-your-meteorological-data--/
link_vtable /--Name-of-Vtable-to-parse-the-above-met-data--/

# -- WPS -- #
link_wps
srun -n${SLURM_NTASKS} ./geogrid.exe
srun -N1 -n1 ./ungrib.exe
srun -n${SLURM_NTASKS} ./metgrid.exe
unlink_wps

# -- WRF -- #
link_emreal
srun -n${SLURM_NTASKS} ./real.exe
srun -n${SLURM_NTASKS} -c${SLURM_CPUS_PER_TASK} ./wrf.exe
unlink_emreal

Additional information about ThaiSC custom commands for the WPS and WRF modules can be accessed by using commands such as link_wps --help, man link_emreal or module help WRF.

Some options do not support hybrid run. To determine if this is the case, do the following:

use #SBATCH --cpus-per-task=1 and export OMP_NUM_THREADS=1,
increase the total number of tasks, for example, #SBATCH --ntasks=64
specify the number of tasks for each executable explicitly; for instance, use
srun -n16 ./real.exe
srun -n64 -c1 ./wrf.exe

3. Job submission

To submit jobs to the SLURM queuing system on LANTA, execute sbatch submitWRF.sh.

If WPS and WRF jobs need be submitted separately, users could use the --dependency option of sbatch command to ensure that WRF starts running only after WPS is completed.

A complete example is available at /project/common/WRF/Example1. To run it within a directory, use cp /project/common/WRF/Example1/* ., edit the account details by using vi submitWRF.sh and then issue the command sbatch submitWRF.sh.

4. Post-processing

Several tools for processing NetCDF files are available in Conda environments such as netcdf-py39. To use NCL, for instance, add

module load Miniconda3
conda activate netcdf-py39

# For NCL only
export NCARG_ROOT=${CONDA_PREFIX}
export NCARG_RANGS=/project/common/WPS_Static/rangs
export NCARG_SHAPEFILE=/project/common/WPS_Static/shapefile  # (If used)

# Commands such as 'srun -n1 ncl xxx' or 'srun -n1 python xxx'

Other main packages installed in netcdf-py39 are NCO, CDO, netcdf4-python, wrf-python, pyngl, pynio and cartopy.

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