CAREER: Temporal Clustering of Hydrometeorological Extremes

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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.