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README.md
Subseasonal Forecasting Toolkit
The subseasonal_toolkit package provides implementations of the subseasonal forecasting ABC model of
Adaptive Bias Correction for Subseasonal Forecasting
Soukayna Mouatadid, Paulo Orenstein, Genevieve Flaspohler, Judah Cohen, Miruna Oprescu, Ernest Fraenkel, and Lester Mackey. Sep. 2022.
@article{
mouatadid2022adaptive,
title={Adaptive Bias Correction for Subseasonal Forecasting},
author={Soukayna Mouatadid, Paulo Orenstein, Genevieve Flaspohler, Judah Cohen, Miruna Oprescu, Ernest Fraenkel, and Lester Mackey},
journal={arXiv preprint arXiv:},
year={2022}
}
and the machine learning models and meteorological baselines of
Learned Benchmarks for Subseasonal Forecasting
Soukayna Mouatadid, Paulo Orenstein, Genevieve Flaspohler, Miruna Oprescu, Judah Cohen, Franklyn Wang, Sean Knight, Maria Geogdzhayeva, Sam Levang, Ernest Fraenkel, and Lester Mackey. Sep. 2021.
@article{
mouatadid2021toolkit,
title={Learned Benchmarks for Subseasonal Forecasting},
author={Soukayna Mouatadid, Paulo Orenstein, Genevieve Flaspohler, Miruna Oprescu, Judah Cohen, Franklyn Wang, Sean Knight, Maria Geogdzhayeva, Sam Levang, Ernest Fraenkel, and Lester Mackey},
journal={arXiv preprint arXiv:2109.10399},
year={2021}
}
Getting Started
- Install the subseasonal toolkit package:
pip install subseasonal-toolkit - Define the environment variable
$SUBSEASONALDATA_PATHto point to your desired data directory; any data files needed by a model will be read from, saved to, or synced with this directory
Generating Model Forecasts
The following examples demonstrate how to generate contiguous US forecasts for the target dates evaluated in "Learned Benchmarks for Subseasonal Forecasting" using each implemented model.
- ABC:
python -m subseasonal_toolkit.generate_predictions -t std_paper_forecast -e -u -m abc - AutoKNN:
python -m subseasonal_toolkit.generate_predictions -t std_paper -u -m autoknn - CCSM4++:
- First generate predictions for each model configuration
python -m subseasonal_toolkit.generate_predictions -t std_paper_eval -e -u -b -m ccsm4pp - Then select a model configuration using the tuner
python -m subseasonal_toolkit.generate_predictions -t std_paper_forecast -e -u -tu -m ccsm4pp
- First generate predictions for each model configuration
- CFSv2++:
- First generate predictions for each model configuration
python -m subseasonal_toolkit.generate_predictions -t std_paper_eval -e -u -b -m cfsv2pp - Then select a model configuration using the tuner
python -m subseasonal_toolkit.generate_predictions -t std_paper_forecast -e -u -tu -m cfsv2pp
- First generate predictions for each model configuration
- Climatology:
python -m subseasonal_toolkit.generate_predictions -t std_paper_forecast -e -u -m climatology - Climatology++:
- First generate predictions for each model configuration
python -m subseasonal_toolkit.generate_predictions -t std_paper_eval -e -u -b -m climpp - Then select a model configuration using the tuner
python -m subseasonal_toolkit.generate_predictions -t std_paper_forecast -e -u -tu -m climpp
- First generate predictions for each model configuration
- Debiased CFSv2:
python -m subseasonal_toolkit.generate_predictions -t std_paper_forecast -e -u -m deb_cfsv2 - Debiased ECMWF Control and Ensemble:
- First, select your desired source (control or ensemble) for debiasing and forecasting in
subseasonal_toolkit/models/deb_ecmwf/selected_submodel.jsonby setting theforecast_withanddebias_withkeys as described indeb_ecmwf.ipynb. - Then, run the selected model:
python -m subseasonal_toolkit.generate_predictions -t std_paper_forecast -e -u -m deb_ecmwf
- First, select your desired source (control or ensemble) for debiasing and forecasting in
- ECMWF++:
- First generate predictions for each model configuration
python -m subseasonal_toolkit.generate_predictions -t std_paper_eval -e -u -b -m ecmwfpp - Then select a model configuration using the tuner
python -m subseasonal_toolkit.generate_predictions -t std_paper_forecast -e -u -tu -m ecmwfpp
- First generate predictions for each model configuration
- FIMr1p1++:
- First generate predictions for each model configuration
python -m subseasonal_toolkit.generate_predictions -t std_paper_eval -e -u -b -m fimr1p1pp - Then select a model configuration using the tuner
python -m subseasonal_toolkit.generate_predictions -t std_paper_forecast -e -u -tu -m fimr1p1pp
- First generate predictions for each model configuration
- GEFS++:
- First generate predictions for each model configuration
python -m subseasonal_toolkit.generate_predictions -t std_paper_eval -e -u -b -m gefspp - Then select a model configuration using the tuner
python -m subseasonal_toolkit.generate_predictions -t std_paper_forecast -e -u -tu -m gefspp
- First generate predictions for each model configuration
- GEOS++:
- First generate predictions for each model configuration
python -m subseasonal_toolkit.generate_predictions -t std_paper_eval -e -u -b -m geos_v2p1pp - Then select a model configuration using the tuner
python -m subseasonal_toolkit.generate_predictions -t std_paper_forecast -e -u -tu -m geos_v2p1pp
- First generate predictions for each model configuration
- GEPS++:
- First generate predictions for each model configuration
python -m subseasonal_toolkit.generate_predictions -t std_paper_eval -e -u -b -m gepspp - Then select a model configuration using the tuner
python -m subseasonal_toolkit.generate_predictions -t std_paper_forecast -e -u -tu -m gepspp
- First generate predictions for each model configuration
- Informer:
python -m subseasonal_toolkit.generate_predictions -t std_paper -u -m informer - LocalBoosting:
- First generate predictions for each model configuration
python -m subseasonal_toolkit.generate_predictions -t std_paper_eval -u -b -m localboosting - Then select a model configuration using the tuner
python -m subseasonal_toolkit.generate_predictions -t std_paper -u -tu -m localboosting
- First generate predictions for each model configuration
- MultiLLR:
python -m subseasonal_toolkit.generate_predictions -t std_paper -u -m multillr - N-BEATS:
python -m subseasonal_toolkit.generate_predictions -t std_paper -u -m nbeats - Online Ensemble:
python -m subseasonal_toolkit.generate_predictions -t std_paper -u -m online_learning - Persistence:
python -m subseasonal_toolkit.generate_predictions -t std_paper -u -m persistence - Persistence++ CCSM4:
python -m subseasonal_toolkit.generate_predictions -t std_paper_forecast -u -e -m perpp_ccsm4 - Persistence++ CFSv2:
python -m subseasonal_toolkit.generate_predictions -t std_paper_forecast -u -e -m perpp_cfsv2 - Persistence++ ECMWF:
python -m subseasonal_toolkit.generate_predictions -t std_paper_forecast -u -e -m perpp_ecmwf - Persistence++ FIMr1p1:
python -m subseasonal_toolkit.generate_predictions -t std_paper_forecast -m perpp_fimr1p1 -u -e - Persistence++ GEFS:
python -m subseasonal_toolkit.generate_predictions -t std_paper_forecast -m perpp_gefs -u -e - Persistence++ GEOS_v2p1:
python -m subseasonal_toolkit.generate_predictions -t std_paper_forecast -m perpp_geos_v2p1 -u -e - Persistence++ GEPS:
python -m subseasonal_toolkit.generate_predictions -t std_paper_forecast -m perpp_geps -u -e - Persistence++ NESM:
python -m subseasonal_toolkit.generate_predictions -t std_paper_forecast -m perpp_nesm -u -e - Prophet:
python -m subseasonal_toolkit.generate_predictions -t std_paper -u -m prophet - Salient2:
- First generate predictions for each model configuration
python -m subseasonal_toolkit.generate_predictions -t std_paper_eval -u -b -m salient2 - Then select a model configuration using the tuner
python -m subseasonal_toolkit.generate_predictions -t std_paper -u -tu -m salient2
- First generate predictions for each model configuration
- Uniform Ensemble:
python -m subseasonal_toolkit.generate_predictions -t std_paper_forecast -e -u -m linear_ensemble
For Developers
Installation
After cloning this repository, install from source in editable mode using pip install -e . in this directory or pip install -e path/to/directory from another directory.
Contributing
This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.
Trademarks
This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.