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Subseasonal-to-Seasonal Extreme Precipitation Events in the Contiguous United States: Generation of a Database and Climatology

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  • 1 a School of Meteorology, University of Oklahoma, Norman, Oklahoma
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Abstract

Extreme precipitation across multiple time scales is a natural hazard that creates a significant risk to life, with a commensurately large cost through property loss. We devise a method to create 14-day extreme-event windows that characterize precipitation events in the contiguous United States (CONUS) for the years 1915–2018. Our algorithm imposes thresholds for both total precipitation and the duration of the precipitation to identify events with sufficient length to accentuate the synoptic and longer time scale contribution to the precipitation event. Kernel density estimation is employed to create extreme-event polygons that are formed into a database spanning from 1915 through 2018. Using the developed database, we clustered events into regions using a k-means algorithm. We define the “hybrid index,” a weighted composite of silhouette score and number of clustered events, to show that the optimal number of clusters is 15. We also show that 14-day extreme precipitation events are increasing in the CONUS, specifically in the Dakotas and much of New England. The algorithm presented in this work is designed to be sufficiently flexible to be extended to any desired number of days on the subseasonal-to-seasonal (S2S) time scale (e.g., 30 days). Additional databases generated using this framework are available for download from our GitHub. Consequently, these S2S databases can be analyzed in future works to determine the climatology of S2S extreme precipitation events and be used for predictability studies for identified events.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Ty A. Dickinson, ty.dickinson@ou.edu

Abstract

Extreme precipitation across multiple time scales is a natural hazard that creates a significant risk to life, with a commensurately large cost through property loss. We devise a method to create 14-day extreme-event windows that characterize precipitation events in the contiguous United States (CONUS) for the years 1915–2018. Our algorithm imposes thresholds for both total precipitation and the duration of the precipitation to identify events with sufficient length to accentuate the synoptic and longer time scale contribution to the precipitation event. Kernel density estimation is employed to create extreme-event polygons that are formed into a database spanning from 1915 through 2018. Using the developed database, we clustered events into regions using a k-means algorithm. We define the “hybrid index,” a weighted composite of silhouette score and number of clustered events, to show that the optimal number of clusters is 15. We also show that 14-day extreme precipitation events are increasing in the CONUS, specifically in the Dakotas and much of New England. The algorithm presented in this work is designed to be sufficiently flexible to be extended to any desired number of days on the subseasonal-to-seasonal (S2S) time scale (e.g., 30 days). Additional databases generated using this framework are available for download from our GitHub. Consequently, these S2S databases can be analyzed in future works to determine the climatology of S2S extreme precipitation events and be used for predictability studies for identified events.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Ty A. Dickinson, ty.dickinson@ou.edu

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