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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 35 / No. 1: AAAI-21 Technical Tracks 1

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

February 1, 2023

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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:

10.1609/aaai.v35i1.16090


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.

Topics: AAAI

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HOW TO CITE:

Sijie He||Xinyan Li||Timothy DelSole||Pradeep Ravikumar||Arindam Banerjee Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances Proceedings of the AAAI Conference on Artificial Intelligence (2021) 169-177.

Sijie He||Xinyan Li||Timothy DelSole||Pradeep Ravikumar||Arindam Banerjee Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances AAAI 2021, 169-177.

Sijie He||Xinyan Li||Timothy DelSole||Pradeep Ravikumar||Arindam Banerjee (2021). Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances. Proceedings of the AAAI Conference on Artificial Intelligence, 169-177.

Sijie He||Xinyan Li||Timothy DelSole||Pradeep Ravikumar||Arindam Banerjee. Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances. Proceedings of the AAAI Conference on Artificial Intelligence 2021 p.169-177.

Sijie He||Xinyan Li||Timothy DelSole||Pradeep Ravikumar||Arindam Banerjee. 2021. Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances. "Proceedings of the AAAI Conference on Artificial Intelligence". 169-177.

Sijie He||Xinyan Li||Timothy DelSole||Pradeep Ravikumar||Arindam Banerjee. (2021) "Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances", Proceedings of the AAAI Conference on Artificial Intelligence, p.169-177

Sijie He||Xinyan Li||Timothy DelSole||Pradeep Ravikumar||Arindam Banerjee, "Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances", AAAI, p.169-177, 2021.

Sijie He||Xinyan Li||Timothy DelSole||Pradeep Ravikumar||Arindam Banerjee. "Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances". Proceedings of the AAAI Conference on Artificial Intelligence, 2021, p.169-177.

Sijie He||Xinyan Li||Timothy DelSole||Pradeep Ravikumar||Arindam Banerjee. "Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances". Proceedings of the AAAI Conference on Artificial Intelligence, (2021): 169-177.

Sijie He||Xinyan Li||Timothy DelSole||Pradeep Ravikumar||Arindam Banerjee. Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances. AAAI[Internet]. 2021[cited 2023]; 169-177.


ISSN: 2374-3468


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