复合型极端气候：机器学习，统计方法和动力模拟

最近Frontiers in Earth Science期刊组织一个专刊/专题 “复合型极端气候：机器学习，统计方法和动力模拟”。主要关注洪水，干旱，热浪，极端降水，风暴潮，飓风/台风等自然灾害存在同时发生或者依次发生的现象。

Compound Climate Extremes in the Present and Future Climates: Machine Learning, Statistical Methods and Dynamical Modelling

About this Research Topic

Compound extremes, referred to as simultaneous, concurrent, or coincident extremes, may lead to larger impacts to human society and the environment than individual extremes alone. There are a wide range of compound events that occur on a variety of spatial and temporal scales. Some typical examples of compounding extremes include drought coupled with heat waves, coastal flooding coupled with wind hazards, sea level rise and storm surge, and tropical cyclones followed by heat waves. Currently, we are yet to fully understand all types of compound extremes, the dynamical and physical processes associated with the extremes, the framework and the methods to analyse the extremes, and the risk of the extremes in the present and future climates.

The goals of this Research Topic are to:

• Advance knowledge about the processes and dynamical linkage associated with different types of compounding extremes;
• Showcase the development of new statistical methods and machine learning techniques for efficiently examining the extremes;
• Quantify the potential risks of compound extremes in the present and future climates.

Considering these goals, this Research Topic welcomes manuscripts addressing the following topics:

• Processes responsible for different types of compound extremes (e.g., drought/heat stress and tropical cyclones/heat waves);
• Statistical models and machine learning technology for processing compounding extremes;
• Quantify the risk of compounding extremes in the present and future climates using the projection experiments of climate models.

Keywords: compound extremes, extreme weather and climate, machine learning, statistical modelling, dynamical models

Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

参考资料

[1]链接: https://www.frontiersin.org/research-topics/14520/compound-climate-extremes-in-the-present-and-future-climates-machine-learning-statistical-methods-an/