Advisor

Paul Loikith

Date of Award

6-13-2018

Document Type

Thesis

Degree Name

Master of Science (M.S.) in Geography

Department

Geography

Physical Description

1 online resource (ix, 66 pages)

DOI

10.15760/etd.6334

Abstract

An extreme precipitation categorization scheme, developed to temporally and spatially visualize and track the multi-scale variability of extreme precipitation climatology, is introduced over the continental United States and used as the basis for an observational dataset intercomparison. The categorization scheme groups three-day precipitation totals exceeding 100 mm into five precipitation categories, or "P-Cats". To assess observational uncertainty across a range of precipitation measurement approaches, we compare in situ station data from the Global Historical Climatology Network-Daily (GHCN-D), satellite derived data from the Tropical Rainfall Measuring Mission (TRMM), 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 (MERRA 2), and regional reanalysis from the North American Regional Reanalysis (NARR). While all datasets capture the principal spatial patterns of extreme precipitation climatology, results show considerable variability across the five-platform suite in P-Cat frequency, spatial extent, and magnitude. Higher resolution datasets, PRISM and TRMM, most closely resemble GHCN-D and capture a greater frequency of high-end totals relative to lower resolution products, NARR and MERRA-2. When all datasets are regridded to a common coarser grid, differences persist with datasets originally constructed at a high resolution maintaining the highest frequency and magnitude of P-Cats. Potential future applications of this scheme include tracking change in P-Cats over space and time, climate model evaluation, and assessment of model projected change.

Persistent Identifier

https://archives.pdx.edu/ds/psu/25688

Included in

Geography Commons

Share

COinS