A toolkit for WeatherBench based on PyTorch
pip install wxbtoolFor detailed installation instructions, see the Installation Guide.
wxb data-serve -m wxbtool.specs.res5_625.t850weyn -s Setting3dwxb train -m wxbtool.zoo.res5_625.unet.t850d3sm_weynwxb test -m wxbtool.zoo.res5_625.unet.t850d3sm_weynwxb forecast -m wxbtool.zoo.res5_625.unet.t850d3sm_weyn -t 2023-01-01 -o output.pngNote: For deterministic forecast, -t must be in YYYY-MM-DD (date only).
wxb forecast -m wxbtool.zoo.res5_625.unet.t850d3sm_weyn -t 2023-01-01T00:00:00 -G true -s 10 -o output.ncNote: For GAN forecast, -t must be in YYYY-MM-DDTHH:MM:SS (date and time).
wxb data-serve -m wxbtool.specs.res5_625.t850weyn -s Setting3d -b 0.0.0.0:8088Note: Use --bind to specify the address. The --port option is currently not used by the implementation.
wxb train -m wxbtool.zoo.res5_625.unet.t850d3sm_weyn -d unix:/tmp/test.sockwxb backtest -m wxbtool.zoo.res5_625.unet.t850d3sm_weyn -t 2023-01-01 -o output.ncThis will write outputs under output/2023-01-01/ and, when using .nc, also create var_day_rmse.json containing day-by-day RMSE.
wxb data-download -m wxbtool.zoo.res5_625.unet.t850d3sm_weyn --coverage weeklyFor more detailed examples and explanations, see the Quick Start Guide.
- Installation Guide
- Quick Start Guide
- Data Handling Guide
- Training Guide
- Evaluation Guide
- Inference Guide
- Troubleshooting Guide
See the comprehensive documentation in the docs directory.
uv build
uv publish
git tag va.b.c master
git push origin va.b.c- Mingli Yuan (Mountain)
- Ren Lu