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Cited by 51Climate variability and modelsMeteorological Phenomena and SimulationsHydrological Forecasting Using AI

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

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He, Sijie, Li, Xinyan, DelSole, Timothy, Ravikumar, Pradeep, Banerjee, Arindam·2021·Proceedings of the AAAI Conference on Artificial Intelligence
DOI 10.1609/aaai.v35i1.16090
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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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    From A machine learning model that outperforms conventional global subseasonal forecast models (2024) · cites this paper as [34]

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Authors
He, S., Li, X., DelSole, T., Ravikumar, P., & Banerjee, A.
Affiliations
  • University of Minnesota (US)
  • George Mason University (US)
  • Carnegie Mellon University (US)
  • University of Illinois Urbana-Champaign (US)
ORCID
Venue
Proceedings of the AAAI Conference on Artificial Intelligence
Publisher
Association for the Advancement of Artificial Intelligence (AAAI)
Published
2021-05-18
Volume
35
Issue
1
Pages
169-177
ISSN
  • 2159-5399 (ISSN-L)
  • 2374-3468
DOI
10.1609/aaai.v35i1.16090
OpenAlex
W3174383901
Cited by
51
References
2
Citations by year
2026: 10 · 2025: 10 · 2024: 12 · 2023: 9 · 2022: 6 · 2021: 3 · 2020: 1
FWCI
22.92
Type
conference paper
Language
en
Open access
Locations
  • Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • National Science Foundation (1447566, 1934634, 1908104, 1447574, 1563950, 1422557, 1451986)
  • University of Minnesota
Topics
  • Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
  • Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Meteorological Phenomena and Simulations
  • Physical Sciences › Environmental Science › Environmental Engineering › Hydrological Forecasting Using AI
Keywords
temperature prediction, precipitation forecasting, XGBoost, deep learning, atmospheric predictability, feature representation, model architecture, contiguous United States

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