subseasonal-toolkit 0.0.3

Models for subseasonal forecasting of temperature and precipitation 2-6 weeks in advance.

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:2209.10666},
  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_PATH to 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
• 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
• 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
• 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.json by setting the forecast_with and debias_with keys as described in deb_ecmwf.ipynb.
  • Then, run the selected model: python -m subseasonal_toolkit.generate_predictions -t std_paper_forecast -e -u -m deb_ecmwf
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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

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