A Localized Quantitative Precipitation Estimation for S-Band Polarimetric Radar in Taiwan

Abstract A polarimetric radar quantitative precipitation estimation (QPE) to estimate the rain rate R from specific attenuation A has been applied in Taiwan’s operational Quantitative Precipitation Estimation and Segregation Using Multiple Sensors (QPESUMS) system since 2016. A 3-yr (2016–18) drop size distribution (DSD) dataset from an operational Particle Size and Velocity (Parsivel) network was used to derive a localized coefficient as well as the α(K) function in the R(A) scheme for S-band radar, where α is a key parameter in the estimation of A and K is the linear fitted slope of differential reflectivity ZDR versus reflectivity Z. The local drop size distribution data were also used to derive the localized R(Z) and R(KDP) relationships, and the relationships were evaluated using radar observations in heavy rain cases. A synthetic quantitative precipitation estimate combining the localized R(A), R(Z), and R(KDP) relationships is compared to its operational counterpart and showed about 8% reduction in the normalized mean error for the mei-yu cases. Typhoon cases exhibited similar improvements by the localized QPE relationships but showed higher uncertainties than in the mei-yu cases. The higher uncertainties in the typhoon QPE verification were likely due to the stronger winds in typhoons than in the mei-yu events that caused greater mismatches between the radar observations at an altitude and the gauges at the ground. Overall, the results demonstrated advantages of localized radar rainfall relationships derived from the disdrometer data to improve the accuracy of the operational rainfall estimation products. Significance Statement A 3-yr (2016–18) drop size distribution (DSD) dataset from the operational Parsivel network in Taiwan was utilized to localize parameters in the QPE relationships for S-band dual-polarimetric radar. These relationships were evaluated using radar observations in the mei-yu front and typhoon events in Taiwan. The results show that using localized radar rainfall relationships derived from the disdrometer data can enhance the accuracy of the operational rainfall products.