Dependence Between Extremes of Satellite and Ground Station Precipitation

• Activity Number: 122 - Analysis of Extreme Events
• Type: Invited
• Date/Time: Monday, August 8, 2022 : 10:30 AM to 12:20 PM
• Sponsor: Section on Risk Analysis
• Author(s): Brook T Russell* and Whitney Huang and Yiren T Ding and Jamie Dyer
• Companies: Clemson University School of Mathematical and Statistical Sciences and Clemson University and Clemson University School of Mathematical and Statistical Sciences and Mississippi State University
• Keywords: asymptotic dependence; bivariate regular variation; concurrent extremes; conditional distribution function

Abstract

The use of satellite precipitation products (SPP) allows for precipitation data to be collected globally, but questions remain regarding their ability to reproduce extreme precipitation over mountainous terrain. In this work, we assess the ability of one SPP to capture daily precipitation extremes, by comparing its output versus corresponding station data in the summer at remote locations in the northern US Rocky Mountains of Wyoming, Idaho, and Montana. Our analysis utilizes the regular variation framework from extreme value theory, and consists of two approaches. We first assess the SPP's ability to model precipitation extremes through pointwise inference on an asymptotic dependence parameter. We then propose an approach to estimate the conditional distribution function for station precipitation, given that the SPP value is extreme. Uncertainty is estimated via the non-parametric bootstrap. We also investigate the degree to which elevation and topographic heterogeneity impact the level of asymptotic dependence between these data sources over our study region, and find evidence that the level of asymptotic dependence between these data sources is lower at higher elevations.