MIXED-FREQUENCY EXTREME VALUE REGRESSION: ESTIMATING THE EFFECT OF MESOSCALE CONVECTIVE SYSTEMS ON EXTREME RAINFALL INTENSITY

• 成果类型: Article
• 署名作者: Dupuis, Debbie J.; Trapin, Luca
• 署名单位: Universite de Montreal; HEC Montreal; University of Bologna
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/22-AOAS1675
• 发表日期: 2023
• 页码: 1398-1418
• 关键词: precipitation extremes; diurnal cycle; UNITED-STATES; intensification; simulation; TRENDS; storms

摘要

Understanding and modeling the determinants of extreme hourly rainfall intensity is of utmost importance for the management of flash-flood risk. In-creasing evidence shows that mesoscale convective systems (MCS) are the principal driver of extreme rainfall intensity in the United States. We use ex-treme value statistics to investigate the relationship between MCS activity and extreme hourly rainfall intensity in Greater St. Louis, an area particularly vul-nerable to flash floods. Using a block maxima approach with monthly blocks, we find that the impact of MCS activity on monthly maxima is not homo-geneous within the month/block. To appropriately capture this relationship, we develop a mixed-frequency extreme value regression framework accom-modating a covariate sampled at a frequency higher than that of the extreme observation.