An Extreme Precipitation Categorization Scheme and its Observational Uncertainty over the Continental United StatesSource: Journal of Hydrometeorology:;2019:;volume 020:;issue 006::page 1029DOI: 10.1175/JHM-D-18-0148.1Publisher: 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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contributor author | Slinskey, Emily A. | |
contributor author | Loikith, Paul C. | |
contributor author | Waliser, Duane E. | |
contributor author | Goodman, Alexander | |
date accessioned | 2019-10-05T06:48:35Z | |
date available | 2019-10-05T06:48:35Z | |
date copyright | 4/22/2019 12:00:00 AM | |
date issued | 2019 | |
identifier other | JHM-D-18-0148.1.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4263485 | |
description 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. | |
publisher | American Meteorological Society | |
title | An Extreme Precipitation Categorization Scheme and its Observational Uncertainty over the Continental United States | |
type | Journal Paper | |
journal volume | 20 | |
journal issue | 6 | |
journal title | Journal of Hydrometeorology | |
identifier doi | 10.1175/JHM-D-18-0148.1 | |
journal fristpage | 1029 | |
journal lastpage | 1052 | |
tree | Journal of Hydrometeorology:;2019:;volume 020:;issue 006 | |
contenttype | Fulltext |