Journal article
Evaluation of Radar-Derived Polarimetric Precipitation Estimates for Extreme Rain Events Using a Dense Network of Rain Gauges
Journal of hydrometeorology, Vol.27(5)
04/06/2026
DOI: 10.1175/JHM-D-25-0127.1
Abstract
The study evaluates radar-based quantitative precipitation estimates (QPE) for ten extreme rain events that occurred between 2013 and 2019 in the Kansas City Metropolitan area, United States. These precipitation estimates were derived at hourly and approximately 0.5 km scales using two polarimetric QPE algorithms—one based on specific attenuation ( A ) and the other on specific differential phase ( K DP )—for the study area covered by two overlapping radars in Topeka, Kansas and Kansas City, Missouri. The polarimetric QPE assessment for extreme rain events was motivated by improved flood forecasting and precipitation frequency analysis. The analysis utilizes ground reference observations from a dense network of about 170 rain gauges over the study area to quantitatively assess the accuracy of these polarimetric rainfall ( R ) estimates. The comparison of R ( A ) and R ( K DP ) with the conventional algorithm based on radar reflectivity observations reveals that the two polarimetric algorithms outperform the reflectivity-based approach. While R ( K DP ) shows a systematic conditional feature (i.e., underestimation at high rain rates) with reduced scatter, R ( A ) appears to be less biased but with relatively large scatter. R ( A )’s significant overestimation for one of the extreme events was attributed to the misestimation of its key parameter (α), which resulted from hail contaminated data samples. To examine the observed underestimation tendency of R ( K DP ), we characterized the magnitude of underestimation (bias) with rainfall spatial variability as this variability may account for different rainfall regimes or the smoothing effect of K DP to reduce its inherent noisiness. Our result demonstrates that the underestimation tendency of R ( K DP ) becomes more pronounced as rainfall spatial variability increases.
Details
- Title: Subtitle
- Evaluation of Radar-Derived Polarimetric Precipitation Estimates for Extreme Rain Events Using a Dense Network of Rain Gauges
- Creators
- Bong-Chul Seo - Missouri University of Science and TechnologyWitold F. Krajewski - University of IowaJames A. Smith - Princeton UniversityAlexander V. Ryzhkov - University of Oklahoma
- Resource Type
- Journal article
- Publication Details
- Journal of hydrometeorology, Vol.27(5)
- DOI
- 10.1175/JHM-D-25-0127.1
- ISSN
- 1525-755X
- eISSN
- 1525-7541
- Publisher
- American Meteorological Society
- Grant note
- NOAA Cooperative Institute Program: NA22NWS4320003
This study was supported by the Department of Civil, Architectural and Environmental Engineering at Missouri University of Science and Technology and the Iowa Flood Center at the University of Iowa. The first author sincerely appreciates Jian Zhang and Stephen Cocks at the National Severe Storms Laboratory for their valuable suggestions and insights regarding the performance evaluation of R (A) and R (KDP). This work was also partially supported by the Cooperative Institute for Researchto Operations in Hydrology (CIROH) with funding under award NA22NWS4320003 from the NOAA Cooperative Institute Program. The statements, findings, conclusions, and recommendations are those of the authors and do not necessarily reflect the opinions of NOAA.
- Language
- English
- Electronic publication date
- 04/06/2026
- Academic Unit
- Civil and Environmental Engineering
- Record Identifier
- 9985153397502771
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