Metadata-Version: 2.1
Name: subseasonal-toolkit
Version: 0.0.5
Summary: Models for subseasonal forecasting of temperature and precipitation 2-6 weeks in advance.
Home-page: https://github.com/microsoft/subseasonal_toolkit
License: UNKNOWN
Platform: UNKNOWN
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: scikit-learn
Requires-Dist: jupyter
Requires-Dist: filelock
Requires-Dist: catboost
Requires-Dist: pyarrow
Requires-Dist: ttictoc
Requires-Dist: subseasonal-data
Requires-Dist: poold
Requires-Dist: torch
Requires-Dist: nbeats-pytorch
Requires-Dist: pytorch-lightning
Requires-Dist: tables

# Subseasonal Forecasting Toolkit

The `subseasonal_toolkit` package provides implementations of the subseasonal forecasting ABC model of

[Adaptive Bias Correction for Subseasonal Forecasting](https://arxiv.org/pdf/2209.10666.pdf)  
Soukayna Mouatadid, Paulo Orenstein, Genevieve Flaspohler, Judah Cohen, Miruna Oprescu, Ernest Fraenkel, and Lester Mackey.  June 2023.

```
@article{mouatadid2023adaptive,
	author = {Mouatadid, Soukayna and Orenstein, Paulo and Flaspohler, Genevieve and Cohen, Judah and Oprescu, Miruna and Fraenkel, Ernest and Mackey, Lester},
	journal = {Nature Communications},
	number = {1},
	pages = {3482},
	title = {Adaptive bias correction for improved subseasonal forecasting},
	volume = {14},
	year = {2023}}
```

and the machine learning models and meteorological baselines of

[SubseasonalClimateUSA: A Dataset for Subseasonal Forecasting and Benchmarking](https://arxiv.org/pdf/2109.10399.pdf)  
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={SubseasonalClimateUSA: A Dataset for Subseasonal Forecasting and Benchmarking},
  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}
}
```

## System Requirements

This package has been tested with the following operating system and Python pairings:
+ macOS Monterey 12.6.3 with Python 3.9.12
+ Linux CentOS 7 with Python 3.7.9

A complete list of Python dependencies can be found in `setup.cfg`; these dependencies are required upon installation.

## Getting Started

- Install the subseasonal toolkit package: `pip install subseasonal-toolkit`
  - Installation completed in under 1 minute with pip 22.2.2 on a 2021 MacBook Pro with 16 GB of RAM running macOS Monterey version 12.6.3.
- 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
- Run the following demo which generates and evaluates Raw CFSv2 precipitation forecasts across the contiguous U.S. for the 2018-2021 `std_paper_forecast` evaluation period of "Adaptive Bias Correction for Subseasonal Forecasting":
  `python -m subseasonal_toolkit.generate_predictions -t std_paper_forecast -u -e -m raw_cfsv2 --task us_tmp2m_1.5x1.5_34w`
  - This demo ran to completion in 7 minutes with Python 3.9.12 on a 2021 MacBook Pro with 16 GB of RAM running macOS Monterey version 12.6.3.
  - Expected outputs 
    - A forecast folder `models/cfsv2pp/submodel_forecasts/cfsv2pp-debiasFalse_years12_margin0_days1-1_leads15-15_lossmse/us_tmp2m_1.5x1.5_34w/` containing daily forecast files from 20180101 through 20211231
    - A metrics folder `eval/metrics/raw_cfsv2/submodel_forecasts/cfsv2pp-debiasFalse_years12_margin0_days1-1_leads15-15_lossmse/us_tmp2m_1.5x1.5_34w/` containing 6 evaluation metrics:
      - `lat_lon_error-us_tmp2m_1.5x1.5_34w-std_paper_forecast.h5`
      - `lat_lon_rmse-us_tmp2m_1.5x1.5_34w-std_paper_forecast.h5`
      - `lat_lon_skill-us_tmp2m_1.5x1.5_34w-std_paper_forecast.h5`
      - `rmse-us_tmp2m_1.5x1.5_34w-std_paper_forecast.h5`
      - `score-us_tmp2m_1.5x1.5_34w-std_paper_forecast.h5`
      - `skill-us_tmp2m_1.5x1.5_34w-std_paper_forecast.h5`

## Generating Model Forecasts

The following examples demonstrate how to generate contiguous US forecasts for the target dates evaluated in "Adaptive Bias Correction for Subseasonal Forecasting" or "SubseasonalClimateUSA: A Dataset for Subseasonal Forecasting and Benchmarking" using each implemented model.

- ABC-CCSM4:
  - Generate predictions for each Climatology++ model configuration
  `python -m subseasonal_toolkit.generate_predictions -t std_paper_eval -e -u -b -m climpp`
  - Generate predictions for each CCSM4++ model configuration
  `python -m subseasonal_toolkit.generate_predictions -t std_paper_eval -e -u -b -m ccsm4pp`
  - Run ABC
  `python -m subseasonal_toolkit.generate_predictions -t std_paper_forecast -e -u -a -m ccsm4`
- ABC-CFSv2:
  - Generate predictions for each Climatology++ model configuration
  `python -m subseasonal_toolkit.generate_predictions -t std_paper_eval -e -u -b -m climpp`
  - Generate predictions for each CFSv2++ model configuration
  `python -m subseasonal_toolkit.generate_predictions -t std_paper_eval -e -u -b -m cfsv2pp`
  - Run ABC
  `python -m subseasonal_toolkit.generate_predictions -t std_paper_forecast -e -u -a -m cfsv2`
- ABC-ECMWF:
  - Generate predictions for each Climatology++ model configuration
  `python -m subseasonal_toolkit.generate_predictions -t std_paper_eval -e -u -b -m climpp`
  - Generate predictions for each ECMWF++ model configuration
  `python -m subseasonal_toolkit.generate_predictions -t std_paper_eval -e -u -b -m ecmwfpp`
  - Run ABC
  `python -m subseasonal_toolkit.generate_predictions -t std_paper_forecast -e -u -a -m ecmwf`
- ABC-FIMr1p1:
  - Generate predictions for each Climatology++ model configuration
  `python -m subseasonal_toolkit.generate_predictions -t std_paper_eval -e -u -b -m climpp`
  - Generate predictions for each FIMr1p1++ model configuration
  `python -m subseasonal_toolkit.generate_predictions -t std_paper_eval -e -u -b -m fimr1p1pp`
  - Run ABC
  `python -m subseasonal_toolkit.generate_predictions -t std_paper_forecast -e -u -a -m fimr1p1`
- ABC-GEFS:
  - Generate predictions for each Climatology++ model configuration
  `python -m subseasonal_toolkit.generate_predictions -t std_paper_eval -e -u -b -m climpp`
  - Generate predictions for each GEFS++ model configuration
  `python -m subseasonal_toolkit.generate_predictions -t std_paper_eval -e -u -b -m gefspp`
  - Run ABC
  `python -m subseasonal_toolkit.generate_predictions -t std_paper_forecast -e -u -a -m gefs`
- ABC-GEMS:
  - Generate predictions for each Climatology++ model configuration
  `python -m subseasonal_toolkit.generate_predictions -t std_paper_eval -e -u -b -m climpp`
  - Generate predictions for each GEMS++ model configuration
  `python -m subseasonal_toolkit.generate_predictions -t std_paper_eval -e -u -b -m gemspp`
  - Run ABC
  `python -m subseasonal_toolkit.generate_predictions -t std_paper_forecast -e -u -a -m gems`
- ABC-GEOS_v2p1:
  - Generate predictions for each Climatology++ model configuration
  `python -m subseasonal_toolkit.generate_predictions -t std_paper_eval -e -u -b -m climpp`
  - Generate predictions for each GEOS_v2p1++ model configuration
  `python -m subseasonal_toolkit.generate_predictions -t std_paper_eval -e -u -b -m geos_v2p1pp`
  - Run ABC
  `python -m subseasonal_toolkit.generate_predictions -t std_paper_forecast -e -u -a -m geos_v2p1`
- ABC-NESM:
  - Generate predictions for each Climatology++ model configuration
  `python -m subseasonal_toolkit.generate_predictions -t std_paper_eval -e -u -b -m climpp`
  - Generate predictions for each NESM++ model configuration
  `python -m subseasonal_toolkit.generate_predictions -t std_paper_eval -e -u -b -m nesmpp`
  - Run ABC
  `python -m subseasonal_toolkit.generate_predictions -t std_paper_forecast -e -u -a -m nesm`
- ABC-SubX:
  - Generate predictions for each Climatology++ model configuration
  `python -m subseasonal_toolkit.generate_predictions -t std_paper_eval -e -u -b -m climpp`
  - Generate predictions for each SubX++ model configuration
  `python -m subseasonal_toolkit.generate_predictions -t std_paper_eval -e -u -b -m subx_meanpp`
  - Run ABC
  `python -m subseasonal_toolkit.generate_predictions -t std_paper_forecast -e -u -a -m subx_mean`
- 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`
- Debiased LOESS CFSv2:
  `python -m subseasonal_toolkit.generate_predictions -t std_paper_forecast -e -u -m deb_loess_cfsv2`
- Debiased LOESS ECMWF:
  `python -m subseasonal_toolkit.generate_predictions -t std_paper_forecast -e -u -m deb_loess_ecmwf`
- Debiased Quantile Mapping CFSv2:
  `python -m subseasonal_toolkit.generate_predictions -t std_paper_forecast -e -u -m deb_quantile_cfsv2`
- Debiased Quantile Mapping ECMWF:
  `python -m subseasonal_toolkit.generate_predictions -t std_paper_forecast -e -u -m deb_quantile_ecmwf`
- Debiased SubX:
  `python -m subseasonal_toolkit.generate_predictions -t std_paper_forecast -e -u -m deb_subx_mean`
- 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`
- GEM++:
  - First generate predictions for each model configuration
  `python -m subseasonal_toolkit.generate_predictions -t std_paper_eval -e -u -b -m gempp`
  - Then select a model configuration using the tuner
  `python -m subseasonal_toolkit.generate_predictions -t std_paper_forecast -e -u -tu -m gempp`
- 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`
- 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`
- NN-A:
  `python -m subseasonal_toolkit.generate_predictions -t std_paper_forecast -e -u -m nn-a`
- 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++ GEM:
  `python -m subseasonal_toolkit.generate_predictions -t std_paper_forecast -m perpp_gem -u -e`
- Persistence++ GEOS_v2p1:
  `python -m subseasonal_toolkit.generate_predictions -t std_paper_forecast -m perpp_geos_v2p1 -u -e`
- Persistence++ NESM:
  `python -m subseasonal_toolkit.generate_predictions -t std_paper_forecast -m perpp_nesm -u -e`
- Persistence++ SubX:
  `python -m subseasonal_toolkit.generate_predictions -t std_paper_forecast -m perpp_subx_mean -u -e`
- Prophet:
  `python -m subseasonal_toolkit.generate_predictions -t std_paper -u -m prophet`
- Raw CCSM4:
  `python -m subseasonal_toolkit.generate_predictions -t std_paper_forecast -u -e -m raw_ccsm4`
- Raw CFSv2:
  `python -m subseasonal_toolkit.generate_predictions -t std_paper_forecast -u -e -m raw_cfsv2`
- Raw ECMWF:
  `python -m subseasonal_toolkit.generate_predictions -t std_paper_forecast -u -e -m raw_ecmwf`
- Raw FIMr1p1:
  `python -m subseasonal_toolkit.generate_predictions -t std_paper_forecast -m raw_fimr1p1 -u -e`
- Raw GEFS:
  `python -m subseasonal_toolkit.generate_predictions -t std_paper_forecast -m raw_gefs -u -e`
- Raw GEM:
  `python -m subseasonal_toolkit.generate_predictions -t std_paper_forecast -m raw_gem -u -e`
- Raw GEOS_v2p1:
  `python -m subseasonal_toolkit.generate_predictions -t std_paper_forecast -m raw_geos_v2p1 -u -e`
- Raw NESM:
  `python -m subseasonal_toolkit.generate_predictions -t std_paper_forecast -m raw_nesm -u -e`
- Raw SubX:
  `python -m subseasonal_toolkit.generate_predictions -t std_paper_forecast -m raw_subx_mean -u -e`
- 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`
- SubX++:
  - First generate predictions for each model configuration
  `python -m subseasonal_toolkit.generate_predictions -t std_paper_eval -e -u -b -m subx_meanpp`
  - Then select a model configuration using the tuner
  `python -m subseasonal_toolkit.generate_predictions -t std_paper_forecast -e -u -tu -m subx_meanpp`
- Uniform Ensemble:
  `python -m subseasonal_toolkit.generate_predictions -t std_paper_forecast -e -u -m linear_ensemble`

## For Developers

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