论文标题
通过机器学习对极端大气事件的分析，表征，预测和归因：评论
Analysis, Characterization, Prediction and Attribution of Extreme Atmospheric Events with Machine Learning: a Review
论文作者
论文摘要
大气极端事件（EES）对人类社会和生态系统造成严重损害。 EES和其他相关事件的频率和强度在当前的气候变化和全球变暖风险中增加。因此，大气EES的准确预测，表征和归因是一个关键的研究领域，其中许多小组目前通过应用不同的方法和计算工具来工作。在过去的几年中，机器学习（ML）方法是解决与大气EE相关的许多问题的强大技术。本文回顾了最重要大气EE的分析，表征，预测和归因的ML算法。提供了该领域最常用的ML技术的摘要，并提供了与EES中与ML有关的文献的全面批判性审查。讨论了许多示例，并列出了现场的观点和前景。
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.
