CAREER: Temporal Clustering of Hydrometeorological Extremes

这个职业生涯建议的主要目标是检查极端水文气象事件是否及时集群。时间聚类是指事件在时间上同时发生的趋势，极端事件的发生影响后续事件的可能性。拟议的工作分为三个阶段，研究的动机是以下主要问题：极端水文气象事件表现出时间聚类，如果是这样，哪些关键的物理过程解释这种行为？大气环流模式（GCM）的输出能否再现观测到的聚集行为？ 是否有可能利用时间聚类来改进水文气象极端事件的预报？基本假设是极端水文气象事件表现出受气候过程控制的时间聚类。美国大陆将是重点，因为这一地区受到大量自然灾害的困扰，产生广泛的社会经济影响。一些在这个一般地区最具破坏性的危害将包括：洪水和暴雨，高温和低温极端，热带和热带风暴。将开发考克斯回归模型，以研究极端事件对气候过程的依赖性。这些方法以PI广泛使用的分析工具和数据集为基础。这项研究是在了解极端事件的频率和原因方面向前迈出的全面一步;这具有广泛的影响。

The main goal of this CAREER proposal is to examine whether extreme hydrometeorological events cluster in time. Temporal clustering refers to the tendency of events to occur together in time, with the occurrence of an extreme event affecting the likelihood of a subsequent event. The proposed work is delineated in three phases, with research motivated by the following main questions: Do extreme hydrometeorological events exhibit temporal clustering, and, if so, which key physical processes explain this behavior? Can outputs from General Circulation Models (GCMs) reproduce observed clustered behavior? Is it possible to take advantage of temporal clustering to improve the forecasting of hydrometeorological extreme events? The underlying hypothesis is that extreme hydrometeorological events exhibit temporal clustering that is controlled by climate processes. The continental United States will be the focus as this area is plagued by a large array of natural hazards yielding extensive socio-economic impacts. Some of the most damaging hazards in this general area will be included: flooding and heavy rainfall, high and low temperature extremes, and tropical and extra-tropical storms. Cox regression models will be developed to examine the dependence of extreme events on climate processes. The methodologies build on analysis tools and data sets that the PI has used extensively. This research represents a comprehensive step forward in the understanding of the frequency and causes of extreme events; this has substantial broader impacts.