Evaluation of the Specific Attenuation Method for Radar-Based Quantitative Precipitation Estimation: Improvements and Practical Challenges

Abstract

Abstract This study demonstrates an implementation of the prototype quantitative precipitation R estimation algorithm using specific attenuation A for S-band polarimetric radar. The performance of R ( A ) algorithm is assessed, compared to the conventional algorithm using radar reflectivity Z , at multiple temporal scales. Because the factor α , defined as the net ratio of A to specific differential phase, is a key parameter of the algorithm characterized by drop size distributions (e.g., differential reflectivity Z dr dependence on Z ), the estimation equations of α and a proper number of Z dr – Z samples required for a reliable α estimation are examined. Based on the dynamic estimation of α , the event-based evaluation using hourly rain gauge observations reveals that the performance of R ( A ) is superior to that of R ( Z ), with better agreement and lower variability. Despite its superiority, the study finds that R ( A ) leads to quite consistent overestimations of about 10%–30%. It is demonstrated that the application of uniform α over the entire radar domain yields the observed uncertainty because of the heterogeneity of precipitation in the domain. A climatological range-dependent feature of R ( A ) and R ( Z ) is inspected in the multiyear evaluation at yearly scale using rain totals for April–October. While R ( Z ) exposes a systematic shift and overestimation, each of which arise from the radar miscalibration and bright band effects, R ( A ) combining with multiple R ( Z ) values for solid/mixed precipitation shows relatively robust performance without those effects. The immunity of R ( A ) to partial beam blockage (PBB) based on both qualitative and quantitative analyses is also verified. However, the capability of R ( A ) regarding PBB is limited by the presence of the melting layer and its application requirement for the total span of differential phase (e.g., 3°), which is another challenge for light rain.