Overview

Details

The SSF Dataset is constructed by climate variables representing the condition of atmosphere, land, and ocean from 1980 until the recent, which are collected from a diverse collection of data sources. In addition, we include some climate indexes monitoring El Niño-Southern Oscillation (Niño indexes) and North Atlantic Oscillation (NAO), etc. All the climate variables are interpolated to 0.5 degree latitude by 0.5 degree longitude grid and daily temporal resolution from 1980 to the recent. The climate variables of each year are saved as multi-index pandas DataFrames and can be read using python 3.

SubX (NCEP-CFSv2 and GMAO-GEOS)

• the climatology of tmp2m of weeks 3 and 4 computed from the corresponding hindcast periods

• the SubX hindcasts of tmp2m anomalies of weeks 3 and 4 over the western U.S. (1999 - 2015)

• the SubX forecasts of tmp2m anomalies of weeks 3 and 4 over the western U.S. (2017 - 2020)

• the initialization dates in the forecast periods of NCEP-CFSv2 and GMAO-GEOS

Groundtruth (computed from the NOAA’s Climate Prediction Center (CPC) Global Gridded Temperature dataset)

• the week 3 & 4 temperature anomalies over the western U.S. from 1990 to 2020

• the climatology computed from 1990 to 2017 for each grid point over the western U.S. and each month-day combination

Preprocessed data

• pca-zscore: The folder contains covariates after preprocessing (pca first then z-scored) from 1986 to 2018.

• train-validation: The folder contains the training and validation data used for hyper parameter tuning.

• train-test: The folder contain the training and test data used for tmp2m forecasting over 2017-2018

Sample Data

Downloads

Please download the dataset using Google Drive.

The original sources of the climate variables can be found in the Appendix B.1 of "Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances and the Table 2 in Learning and Dynamical Models for Sub-seasonal Climate Forecasting: Comparison and Collaboration.

Citation

@inproceedings{ssf_dataset,

title={Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances},

author={He, Sijie and Li, Xinyan and DelSole, Timothy and Ravikumar, Pradeep and Banerjee, Arindam},

booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},

volume={35},

number={1},

pages={169--177},

year={2021}}

@article{ssf_mip,

title={Learning and Dynamical Models for Sub-seasonal Climate Forecasting: Comparison and Collaboration},

author={He, Sijie and Li, Xinyan and Trenary, Laurie and Cash, Benjamin and DelSole, Timothy and Banerjee, Arindam},

journal={arXiv preprint arXiv:2110.05196},

year={2021}

}