Research

Speaker: Ian Cornejo - University of Wisconsin, Madison

Extreme rainfall contributes to socioeconomical disasters such as flood, landslides, and crop destruction. Being able to understand the microphysical processes that produce extreme rainfall is crucial for risk prevention. Dual-polarization radar is a valuable tool for inferring microphysical processes and quantifying the resulting rainfall. The recent 2022 Prediction of Rainfall Extremes Campaign in the Pacific (PRECIP), with its deployment of the NCAR S-Pol radar, seeks to expand our knowledge of microphysical processes leading to extreme rainfall. During the campaign, this S-band, dual-polarization radar operated nearly continuously for 3 months on the western coastline of Taiwan with the steep Central Mountain Range to its east. S-Pol measured a myriad of heavy rainfall events varying from isolated afternoon convective cells to widespread precipitation systems along Mei-yu fronts, including those encountering Taiwan’s complex terrain. While our larger goal is to improve our process-level understanding of heavy rainfall in this variety of events, the first step is to quantify the rainfall from S-Pol across the range of observed rain rates. Radar-based rain rate algorithms struggle to capture intense orographic precipitation owing to effects of partial beam blockage and terrain-induced ground clutter. Recent studies have shown that the utilization of specific attenuation by a radar for rain rates can overcome partial beam blockage impacts by relying on radial profiles of differential phase. This work aims to apply this specific attenuation-based rain rate algorithm to the PRECIP S-Pol radar dataset to provide reliable estimates of intense rain rates, including over the mountains. These results are compared with the more commonly used NCAR hybrid rain rate algorithm, for which coefficients for individual rain rate estimators are tuned for PRECIP using PARticle SIze VELocity (PARSIVEL) disdrometers. A broader goal is to implement this attenuation-based algorithm into the Lidar Radar Open Software Environment (LROSE) for applications to other radars. Additionally, the S-Pol rain rate product will be used to link radar-inferred microphysics to study extreme rainfall in Taiwan. Transcript