Evaluation of Radar-Derived Polarimetric Precipitation Estimates for Extreme Rain Events Using a Dense Network of Rain Gauges

Abstract The study evaluates radar-based quantitative precipitation estimations (QPEs) for 10 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. The significanct overestimation of R ( A ) 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.