Characterization of Raindrop Size Distribution over Santa Clara Valley Atmosphere

This study presents a year-long (January 2019–April 2020) analysis of the Z–R relationship and drop size distribution (DSD) scaling parameters for size, concentration, and shape of rain events over Santa Clara Valley, CA. External influences were analyzed based on synoptic variability and seasons. For the former, 850 hPa winds were separated into groups based on direction and magnitude. Results show that greater drop size, lower concentration, and larger shape parameters for spring, while winter and fall showed smaller drop sizes, higher concentrations, and smaller shape parameters. For synoptic variability, southeasterly-to-southwesterly flow was associated with larger drop sizes, larger concentrations, and smaller shape parameters relative to northwesterly flow. Differences in the DSD scaling parameter values and Z–R relationship were also observed between strong and weak low-level flow. The results of this study suggest that it is beneficial to derive specific microphysical relationships based on seasons and different synoptic events to improve radar rain rate retrieval algorithms using the Z–R relationship. Grant no. NA22SEC4810015

本研究针对2019年1月至2020年4月为期一年的观测时段，对美国加利福尼亚州圣克拉拉谷区域降雨事件的Z-R关系、雨滴尺寸分布（drop size distribution, DSD）尺度参数——涵盖雨滴尺寸、数浓度与形状特征——开展系统性分析。研究从天气尺度变率与季节差异两个维度探究外部影响因素：针对天气尺度变率，按风向与风速将850百帕风场划分为不同组别。结果显示，春季的雨滴尺寸更大、数浓度更低，形状参数值更高；而冬季与秋季的雨滴尺寸更小、数浓度更高，形状参数值更低。就天气尺度变率而言，相较于西北气流，东南至西南气流对应着更大的雨滴尺寸、更高的数浓度与更小的形状参数。此外，强弱低层气流之间的DSD尺度参数与Z-R关系亦存在显著差异。本研究结果表明，基于季节与不同天气过程推导针对性的微物理关系，有助于改进基于Z-R关系的雷达降雨率反演算法。本研究受资助编号NA22SEC4810015资助。