Abstract
Understanding and modeling the determinants of extreme hourly rainfall intensity is of utmost importance for the management of flash-flood risk. Increasing evidence shows that mesoscale convective systems (MCS) are the principal driver of extreme rainfall intensity in the United States. We use extreme value statistics to investigate the relationship between MCS activity and extreme hourly rainfall intensity in Greater St. Louis, an area particularly vulnerable to flash floods. Using a block maxima approach with monthly blocks, we find that the impact of MCS activity on monthly maxima is not homogeneous within the month/block. To appropriately capture this relationship, we develop a mixed-frequency extreme value regression framework accommodating a covariate sampled at a frequency higher than that of the extreme observation.
Funding Statement
The first author was supported by the Natural Sciences and Engineering Research Council of Canada RGPIN-2016-04114 and the HEC Foundation.
Acknowledgments
The authors wish to thank the Associate Editor and two anonymous referees for helpful comments that improved the paper. The authors also wish to thank Ruby Leung and Zhe Feng for providing the data and helpful discussion.
Citation
Debbie J. Dupuis. Luca Trapin. "Mixed-frequency extreme value regression: Estimating the effect of mesoscale convective systems on extreme rainfall intensity." Ann. Appl. Stat. 17 (2) 1398 - 1418, June 2023. https://doi.org/10.1214/22-AOAS1675
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