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

TL;DR

This review discusses how machine learning techniques are increasingly used to analyze, predict, and attribute extreme atmospheric events, highlighting recent advances, challenges, and future perspectives in the field.

Contribution

It provides a comprehensive review of ML algorithms applied to atmospheric EEs, summarizing current methods and offering critical insights and future outlooks.

Findings

ML methods are effective in analyzing EEs

Recent advances improve prediction accuracy

Critical review highlights research gaps

Abstract

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.