Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances

Authors

  • Sijie He University of Minnesota
  • Xinyan Li University of Minnesota
  • Timothy DelSole George Mason University
  • Pradeep Ravikumar Carnegie Mellon University
  • Arindam Banerjee University of Illinois Urbana-Champaign

DOI:

https://doi.org/10.1609/aaai.v35i1.16090

Keywords:

Energy, Environment & Sustainability, Mining of Spatial, Temporal or Spatio-Temporal Da, Applications

Abstract

Sub-seasonal forecasting (SSF) focuses on predicting key variables such as temperature and precipitation on the 2-week to 2-month time scale. Skillful SSF would have immense societal value in such areas as agricultural productivity, water resource management, and emergency planning for extreme weather events. However, SSF is considered more challenging than either weather prediction or even seasonal prediction, and is still a largely understudied problem. In this paper, we carefully investigate 10 Machine Learning (ML) approaches to sub-seasonal temperature forecasting over the contiguous U.S. on the SSF dataset we collect, including a variety of climate variables from the atmosphere, ocean, and land. Because of the complicated atmosphere-land-ocean couplings and the limited amount of good quality observational data, SSF imposes a great challenge for ML despite the recent advances in various domains. Our results indicate that suitable ML models, e.g., XGBoost, to some extent, capture the predictability on sub-seasonal time scales and can outperform the climatological baselines, while Deep Learning (DL) models barely manage to match the best results with carefully designed architecture. Besides, our analysis and exploration provide insights on important aspects to improve the quality of sub-seasonal forecasts, e.g., feature representation and model architecture. The SSF dataset and code are released with this paper for use by the broader research community.

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Published

2021-05-18

How to Cite

He, S., Li, X., DelSole, T., Ravikumar, P., & Banerjee, A. (2021). Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances. Proceedings of the AAAI Conference on Artificial Intelligence, 35(1), 169-177. https://doi.org/10.1609/aaai.v35i1.16090

Issue

Section

AAAI Technical Track on Application Domains