QG model

A quasi-geostrophic model (qg), written in Fortran by Dr. Shafer Smith, is implemented in NEDAS as a test model.

A Docker image provides a demonstration of the offline filter analysis scheme.

docker pull myying/nedas-qgmodel-benchmark

Start a container with

docker run -it --rm -v YOUR_WORK_PATH:/work myying/nedas-qgmodel-benchmark

where YOUR_WORK_PATH is the local disk location where you want to save the output files.

When the container starts running, you can first run

./prepare_files.sh

to generate the truth and intial ensemble member files.

Then start the cycling data assimilation:

python -m NEDAS -c /app/config.yml

Files generated at runtime is located in /work/cycle, additional netCDF output files are saved at /work/output.

For benchmarking of the performance, you can change parameters by adding runtime arguments:

  • --nens=NENS changes the ensemble size (int)

  • --nproc=NPROC changes the number of processors to use (int)

To make more detailed changes (such as localization and inflation parameters), you can make a copy of the YAML configuration file

then edit it externally from YOUR_WORK_PATH/new-config.yml and run it with

You can also turn on debug mode --debug=on to have more detailed runtime messages.