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Machine Learning Methods for Weather Forecasting: A Survey
Open accessAbstract
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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“For that reason, deep learning methods have received increasing attention in recent years because they have shown good results in spatiotemporal prediction problems [14, 15].”
From GPU-Accelerated Deep Learning for Heatwave Prediction and Urban Heat Risk Assessment (2026) · cites this paper as [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
- Venue
- Atmosphere
- Publisher
- MDPI AG
- Published
- 2025-01-14
- Volume
- 16
- Issue
- 1
- Pages
- 82
- ISSN
- 2073-4433 (ISSN-L)
- OpenAlex
- W4406362609
- Cited by
- 113
- References
- 162
- Citations by year
- 2026: 61 · 2025: 52
- FWCI
- 49.29
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- 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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