A Spatial-Temporal Extreme Precipitation Database from GPM IMERG

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dc.contributor.authorZhou, Yaping
dc.contributor.authorNelson, Kevin
dc.contributor.authorMohr, Karen I.
dc.contributor.authorHuffman, George J.
dc.contributor.authorLevy, Robert
dc.contributor.authorGrecu, Mircea
dc.date.accessioned2022-02-17T17:12:56Z
dc.date.available2022-02-17T17:12:56Z
dc.date.issued2021-08-30
dc.description.abstractExtreme precipitation events (EPEs) have the potential to create catastrophic flooding, landslides, and infrastructure damage. We diagnose the spatial and temporal characteristics of EPEs by using the Integrated Multi-SatellitE Retrievals for Global Precipitation Measurement mission (GPM; IMERG) precipitation estimates to construct spatial-temporal (xy-t) EPEs that depict both the spatial extent and temporal evolution of precipitation systems. EPEs were constructed using a recursive-fractal approach to classify the precipitating grids across space and time as belonging to the same system, thus identifying events. This classification enables the accurate depiction of duration, areal coverage, total volume, and propagation of each EPE over its entire life cycle. Results from 4 years of IMERG statistics over the contiguous United States show that the most frequent EPEs have duration between 3 and 6 hr, an affected area of 103–5 × 104 km2, and a total precipitation volume of 106–108 m3. Spatially, EPEs occur most frequently in the northwest and northeast in the winter and spring and the southwest and southeast in summer. Fall has the least number of EPEs, and summer exhibits some of the heaviest and largest precipitation events. The diurnal cycle in frequency and precipitation volume is most prominent in summer, weaker in spring and fall, and is not discernible in winter, especially for events lasting fewer than 6 hr. The event propagation speeds indicate the influence of large-scale circulations as winter events tend to move faster than those in the other seasons.en_US
dc.description.sponsorshipWe thank two anonymous reviewers formany thoughtful comments of theinitial version of this manuscript. Thefirst author would like to thank LevonDemirdjian for discussion about theextreme event algorithm. Support fromNASA NNH13ZDA001N‐TERAQEA,NNH14ZDA001N‐DSCOVR, andSCEX22019D is acknowledged. TheIMERG data were downloaded fromNASA GES DISC (https://doi.org/10.5067/GPM/IMERG/3B‐HH/05). Theextreme event statistics can be found inhttps://data.mendeley.com/datasets/r9d66gnvtm/draft?a = 0c85c966‐b92c‐4ba1‐8597‐3a1cad09ece9.10,362en_US
dc.description.urihttps://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2019JD030449en_US
dc.format.extent20 pagesen_US
dc.genrejournal articlesen_US
dc.identifierdoi:10.13016/m2ldia-a7aa
dc.identifier.citationZhou, Y., Nelson, K., Mohr, K. I., Huffman, G. J., Levy, R., & Grecu, M.(2019). A spatial‐temporal extreme precipitation database from GPM IMERG. Journal of Geophysical Research: Atmospheres, 124, 10,344–10,363en_US
dc.identifier.urihttps://doi.org/10.1029/2019JD030449
dc.identifier.urihttp://hdl.handle.net/11603/24290
dc.language.isoen_USen_US
dc.publisherAGUen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Joint Center for Earth Systems Technology
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.en_US
dc.rightsPublic Domain Mark 1.0*
dc.rights.urihttp://creativecommons.org/publicdomain/mark/1.0/*
dc.titleA Spatial-Temporal Extreme Precipitation Database from GPM IMERGen_US
dc.typeTexten_US
dcterms.creatorhttps://orcid.org/0000-0002-7812-851Xen_US

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