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

Abstract

Abstract An 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.