mlsanityData to 7 Oct 2026

Analysis, Characterization, Prediction and Attribution of Extreme Atmospheric Events with Machine Learning: a Review

arXiv 2207.07580 ↗First posted 3 Jun 2022cs.LG cs.NE physics.ao-phcited by 3

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Abstract (arXiv)

Atmospheric Extreme Events (EEs) cause severe damages to human societies and ecosystems. The frequency and intensity of EEs and other associated events are increasing in the current climate change and global warming risk. The accurate prediction, characterization, and attribution of atmospheric EEs is therefore a key research field, in which many groups are currently working by applying different methodologies and computational tools. Machine Learning (ML) methods have arisen in the last years as powerful techniques to tackle many of the problems related to atmospheric EEs. This paper reviews the ML algorithms applied to the analysis, characterization, prediction, and attribution of the most important atmospheric EEs. A summary of the most used ML techniques in this area, and a comprehensive critical review of literature related to ML in EEs, are provided. A number of examples is discussed and perspectives and outlooks on the field are drawn.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 24 of its 265 references that are in the map. First posted on arXiv 3 Jun 2022.

Builds on

24 of 265 references.

References · 24

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12 more
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Cited by year

Citing papers per year: 2022: 1; 2023: 2.

3 papers.

  1. 2023Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulationcited by 60
  2. 2022Accurate Long-term Air Temperature Prediction with a Fusion of Artificial Intelligence and Data Reduction Techniquescited by 1
  3. 2023Spain on Fire: A novel wildfire risk assessment model based on image satellite processing and atmospheric informationcited by 1