Potential of Snowfall Nowcasting Using Polarimetric Radar Data and Its Link to Ice Microphysics: Study of Two Snowstorms in East China

Abstract

AbstractThe microphysical characteristics, including the ice types and growth mechanisms, in two snowstorms over East China in 2016 (E16) and 2018 (E18) were explored using joint observations of a polarimetric radar and a disdrometer. In both events, the dendritic‐growth‐layer (DGL) signature (enhancements in differential reflectivity ZDR and specific differential phase KDP) was observed, indicative of the existence of horizontally‐oriented dendrites and plate‐like crystals. The KDP in the DGL, related to the total mass of ice particles, was used for snowfall nowcasting. According to the lagged correlation analysis, the reflectivity factor (ZH) near the ground, a proxy for snowfall intensity, could be well predicted by the KDP in the DGL with a 40‐min lead time and a correlation coefficient over 0.7 in E16. In contrast, E18 had a worse nowcasting performance, with the maximum correlation coefficient being ∼0.53, which could be attributed to its more complex ice microphysical structures and processes. The snow in E16 mainly grew from the deposition in the DGL (mainly dendrites and plates) and aggregation below. In comparison, the snowstorm was deeper in E18, and the less oblate crystals (with larger aspect ratios) from above the DGL could grow and mix with the dendrites and plates in the DGL, weakening the correlation between the KDP and ice mass. Below the DGL, snow growth in E18 was contributed by aggregation and more active riming according to both radar and disdrometer observations, which further lowered the nowcasting performance of snowfall by the KDP in the DGL.