2 research outputs found
Analysis, Characterization, Prediction and Attribution of Extreme Atmospheric Events with Machine Learning: a Review
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.Comment: 93 pages, 18 figures, under revie