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Machine Learning Methods for Weather Forecasting: A Survey

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Zhang, Huijun, Liu, Yaxin, Zhang, Chongyu, Li, Ningyun·2025·Atmosphere
DOI 10.3390/atmos16010082
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Abstract

Weather forecasting, a vital task for agriculture, transportation, energy, etc., has evolved significantly over the years. Comprehensive surveys play a crucial role in synthesizing knowledge, identifying trends, and addressing emerging challenges in this dynamic field. In this survey, we critically examines machine learning (ML)-based weather forecasting methods, which demonstrate exceptional capability in handling complex, high-dimensional datasets and leveraging large volumes of historical and real-time data, enabling the identification of subtle patterns and relationships among weather variables. Research on specific tasks such as global weather forecasting, downscaling, extreme weather prediction, and how to combine machine learning methods with physical principles are very active in the current field. However, several unresolved or challenging issues remain, including the interpretability of models and the ability to predict rare weather events. By identifying these gaps, this research provides a roadmap for advancing machine learning-based weather forecasting techniques to complement and enhance weather prediction results.

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Research works printed but not linked: 15

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Metadata

Authors
Zhang, H., Liu, Y., Zhang, C., & Li, N.
Affiliations
  • Huaneng Clean Energy Research Institute (CN)
  • Beijing Big Data Center, No. 3 Courtyard, Liuzhuang Road, Tongzhou District, Beijing 101117, China
Corresponding author
Zhang, Huijun
ORCID
Venue
Atmosphere
Publisher
MDPI AG
Published
2025-01-14
Volume
16
Issue
1
Pages
82
ISSN
2073-4433 (ISSN-L)
DOI
10.3390/atmos16010082
OpenAlex
W4406362609
Cited by
113
References
162
Citations by year
2026: 61 · 2025: 52
FWCI
49.29
Type
journal article
Language
en
Open access
Licence
Locations
  • Atmosphere (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
China Huaneng Clean Energy Research Institute (QNYJJ24-02)
Topics
  • Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Meteorological Phenomena and Simulations
  • Physical Sciences › Environmental Science › Environmental Engineering › Hydrological Forecasting Using AI
  • Social Sciences › Transportation › Traffic Prediction and Management Techniques
Keywords
global weather forecasting, downscaling, extreme weather prediction, model interpretability

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