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    An Extreme Precipitation Categorization Scheme and its Observational Uncertainty over the Continental United States

    Source: Journal of Hydrometeorology:;2019:;volume 020:;issue 006::page 1029
    Author:
    Slinskey, Emily A.
    ,
    Loikith, Paul C.
    ,
    Waliser, Duane E.
    ,
    Goodman, Alexander
    DOI: 10.1175/JHM-D-18-0148.1
    Publisher: American Meteorological Society
    Abstract: AbstractAn extreme precipitation categorization scheme, used to temporally and spatially visualize and track the multiscale variability of extreme precipitation climatology, is applied over the continental United States. The scheme groups 3-day precipitation totals exceeding 100 mm into one of five precipitation categories, or ?P-Cats.? To demonstrate the categorization scheme and assess its observational uncertainty across a range of precipitation measurement approaches, we compare the climatology of P-Cats defined using in situ station data from the Global Historical Climatology Network-Daily (GHCN-D); satellite-derived data from the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA); gridded station data from the Parameter-Elevation Regression on Independent Slopes Model (PRISM); global reanalysis from the Modern-Era Retrospective Analysis for Research and Applications, version 2; and regional reanalysis from the North American Regional Reanalysis. While all datasets capture the principal spatial patterns of P-Cat climatology, results show considerable variability across the suite in frequency, spatial extent, and magnitude. Higher-resolution datasets, PRISM and TMPA, most closely resemble GHCN-D and capture a greater frequency of high-end P-Cats relative to the lower-resolution products. When all datasets are rescaled to a common coarser grid, differences persist with datasets originally constructed at a high resolution maintaining a higher frequency and magnitude of P-Cats. Results imply that dataset choice matters when applying the P-Cat scheme to track extreme precipitation over space and time. Potential future applications of the P-Cat scheme include providing a target for climate model evaluation and a basis for characterizing future change in extreme precipitation as projected by climate model simulations.
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      An Extreme Precipitation Categorization Scheme and its Observational Uncertainty over the Continental United States

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4263485
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    contributor authorSlinskey, Emily A.
    contributor authorLoikith, Paul C.
    contributor authorWaliser, Duane E.
    contributor authorGoodman, Alexander
    date accessioned2019-10-05T06:48:35Z
    date available2019-10-05T06:48:35Z
    date copyright4/22/2019 12:00:00 AM
    date issued2019
    identifier otherJHM-D-18-0148.1.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4263485
    description abstractAbstractAn extreme precipitation categorization scheme, used to temporally and spatially visualize and track the multiscale variability of extreme precipitation climatology, is applied over the continental United States. The scheme groups 3-day precipitation totals exceeding 100 mm into one of five precipitation categories, or ?P-Cats.? To demonstrate the categorization scheme and assess its observational uncertainty across a range of precipitation measurement approaches, we compare the climatology of P-Cats defined using in situ station data from the Global Historical Climatology Network-Daily (GHCN-D); satellite-derived data from the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA); gridded station data from the Parameter-Elevation Regression on Independent Slopes Model (PRISM); global reanalysis from the Modern-Era Retrospective Analysis for Research and Applications, version 2; and regional reanalysis from the North American Regional Reanalysis. While all datasets capture the principal spatial patterns of P-Cat climatology, results show considerable variability across the suite in frequency, spatial extent, and magnitude. Higher-resolution datasets, PRISM and TMPA, most closely resemble GHCN-D and capture a greater frequency of high-end P-Cats relative to the lower-resolution products. When all datasets are rescaled to a common coarser grid, differences persist with datasets originally constructed at a high resolution maintaining a higher frequency and magnitude of P-Cats. Results imply that dataset choice matters when applying the P-Cat scheme to track extreme precipitation over space and time. Potential future applications of the P-Cat scheme include providing a target for climate model evaluation and a basis for characterizing future change in extreme precipitation as projected by climate model simulations.
    publisherAmerican Meteorological Society
    titleAn Extreme Precipitation Categorization Scheme and its Observational Uncertainty over the Continental United States
    typeJournal Paper
    journal volume20
    journal issue6
    journal titleJournal of Hydrometeorology
    identifier doi10.1175/JHM-D-18-0148.1
    journal fristpage1029
    journal lastpage1052
    treeJournal of Hydrometeorology:;2019:;volume 020:;issue 006
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
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