Abstract

The study evaluated radar-derived polarimetric rainfall estimates for extreme rain events that occurred in the Kansas City Metropolitan area in the United States. To derive quantitative precipitation estimates (QPE), we implemented two polarimetric algorithms based on specific attenuation (A) and specific differential phase (KDP), along with the reflectivity (Z) based one using data from two radars in the study area. The analysis to assess radar-rainfall estimates (R) utilizes ground observations from a dense network of about 170 rain gauges. Based on our analysis results, the two polarimetric estimates from R(A) and R(KDP) outperform the conventional estimation R(Z). R(A) appeared to be less biased with relatively large scatter while R(KDP) underestimates at high rainfall rate with less scatter compared to R(A). To generate robust rainfall estimates by accounting for the error structure of the individual algorithms, we decomposed the errors into systematic and random components, conditioned on the magnitude of radar estimates. These conditional features were then used to generate composite weighted rainfall estimates. The composite estimates derived from two polarimetric algorithms, R(A) and R(KDP), showed significant improvement, particularly for reduction in bias and variability. The spatial averaging of these composite estimates over an experimental domain demonstrates their potential for streamflow prediction.

Department(s)

Civil, Architectural and Environmental Engineering

Keywords and Phrases

Extreme; Polarimetric radar; QPE; Rainfall

International Standard Serial Number (ISSN)

2375-5318; 1097-5764

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2025 Institute of Electrical and Electronics Engineers, All rights reserved.

Publication Date

01 Jan 2025

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