Simultaneous concurrence of extreme values across multiple climate variables can result in large societal and environmental impacts. Therefore, there is growing interest in understanding these concurrent extremes. In many applications, not only the frequency but also the magnitude of concurrent extremes are of interest. One way to approach this problem is to study the distribution of one climate variable given that another is extreme. In this work we develop a statistical framework for estimating bivariate concurrent extremes via a conditional approach, where univariate extreme value modeling is combined with dependence modeling of the conditional tail distribution using techniques from quantile regression and extreme value analysis to quantify concurrent extremes. We focus on the distribution of daily wind speed conditioned on daily precipitation taking its seasonal maximum. The Canadian Regional Climate Model large ensemble is used to assess the performance of the proposed framework both via a simulation study with specified dependence structure and via an analysis of the climate model-simulated dependence structure.
翻译:在这项工作中,我们开发了一个统计框架,通过有条件的方法估算双轨并存极端,其中单值极端值模型与依赖利用量化回归和极端值分析技术对有条件尾料分布进行模型分析相结合,以量化同时存在的极端值。我们注重每日风速的分布,以每日降雨量达到季节性最大量为条件。加拿大区域气候模型大型剧团被用于评估拟议框架的绩效,通过对特定依赖性结构进行模拟研究,并通过对气候模型模拟依赖性结构的分析,评估拟议框架的绩效。