Analysis, Characterization, Prediction and Attribution of Extreme Atmospheric Events with Machine Learning: a Review
It addresses the increasing frequency and intensity of atmospheric extreme events due to climate change, offering a review for researchers in this field, but it is incremental as it synthesizes existing work.
This paper reviews machine learning methods applied to the analysis, characterization, prediction, and attribution of atmospheric extreme events, summarizing techniques and providing a critical literature review with examples and outlooks.
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.