Editorial Type:
Article
Article Type:
Research Article

Range Dependence of Polarimetric Radar Estimates for Extreme Flood-Producing Rainfall in Urban Watersheds

Molly Margaret Chaney aCivil and Environmental Engineering, Princeton University, Princeton, New Jersey

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James A Smith aCivil and Environmental Engineering, Princeton University, Princeton, New Jersey

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Mary Lynn Baeck aCivil and Environmental Engineering, Princeton University, Princeton, New Jersey

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Online Publication:
11 Aug 2022
Print Publication:
01 Aug 2022
DOI:
https://doi.org/10.1175/JHM-D-21-0191.1
Page(s):
1205–1226
Restricted access

Abstract

We examine polarimetric rainfall estimates of extreme rainfall through intercomparisons of radar rainfall estimates with rainfall observations from a dense network of rain gauges in Kansas City. The setting provides unique capabilities for examining range dependence in polarimetric rainfall estimates due to the overlapping coverage of the Kansas City, Missouri, and Topeka, Kansas, WSR-88D radars. We focus on polarimetric measurements of specific differential phase shift, KDP, for estimating extreme rainfall. Gauge–radar intercomparisons from the “close-range” Kansas City radar and from the “far-range” Topeka radar show that KDP can provide major improvements in estimating extreme rainfall, but the advantages of KDP rainfall estimates diminish with range. Storm-to-storm variability of multiplicative bias remains an important issue for polarimetric rainfall estimates; variability in bias is comparable at both close and far range from the radar. “Conditional bias,” in which peak radar rainfall estimates are lower than rain gauge observations, is a systematic feature of polarimetric rainfall estimates, but is more severe at far range. The Kansas City region has experienced record flooding in urban watersheds since the polarimetric upgrade of the Kansas City and Topeka radars in 2012. Polarimetric rainfall estimates from the far-range Topeka radar provide useful quantitative information on basin-average rainfall, but the ability to resolve spatial variation of the most extreme rain rates diminishes significantly with range from the radar.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Molly Margaret Chaney, mchaney@princeton.edu

Abstract

We examine polarimetric rainfall estimates of extreme rainfall through intercomparisons of radar rainfall estimates with rainfall observations from a dense network of rain gauges in Kansas City. The setting provides unique capabilities for examining range dependence in polarimetric rainfall estimates due to the overlapping coverage of the Kansas City, Missouri, and Topeka, Kansas, WSR-88D radars. We focus on polarimetric measurements of specific differential phase shift, KDP, for estimating extreme rainfall. Gauge–radar intercomparisons from the “close-range” Kansas City radar and from the “far-range” Topeka radar show that KDP can provide major improvements in estimating extreme rainfall, but the advantages of KDP rainfall estimates diminish with range. Storm-to-storm variability of multiplicative bias remains an important issue for polarimetric rainfall estimates; variability in bias is comparable at both close and far range from the radar. “Conditional bias,” in which peak radar rainfall estimates are lower than rain gauge observations, is a systematic feature of polarimetric rainfall estimates, but is more severe at far range. The Kansas City region has experienced record flooding in urban watersheds since the polarimetric upgrade of the Kansas City and Topeka radars in 2012. Polarimetric rainfall estimates from the far-range Topeka radar provide useful quantitative information on basin-average rainfall, but the ability to resolve spatial variation of the most extreme rain rates diminishes significantly with range from the radar.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Molly Margaret Chaney, mchaney@princeton.edu
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