中文摘要：

人工影响天气研究需对云中降水粒子的相态和分布结构进行准确识别,以便提高人工影响天气作业效率。中国科学院大气物理研究所的车载X波段双极化雷达可提供与云中降水粒子大小、形状、相态等特征密切相关的4个极化参数：反射率因子、差分反射率、差分相移率、水平和垂直极化相关系数。利用这4个极化参数加上环境温度作为5个输入参量,建立了降水粒子相态模糊逻辑识别算法,识别的降水粒子有10种：毛毛雨、雨、湿霰、干霰、小雹、大雹、雨加雹、湿雪、干雪、冰晶。利用此雷达的实际观测资料,并与地面和飞机空中实测资料对照,对我国南、北方地区观测的降水天气过程进行分析,结果表明：建立的模糊逻辑算法对云内水凝物粒子的相态识别分类合理。

英文摘要：

The study of weather modification must exactly identify the phases of cloud hydrometeor particles to improve the weather modification performance. The vehicle-borne X-band dual-polarization radar system set up by the Laboratory of Cloud-Precipitation and Severe Storms （LACS）, Institute of Atmospheric Physics （IAP）, Chinese Academy of Sciences, can provide several dual-polarization radar observables, including radar reflectivity, differential reflectivity, specific differential propagation phase, and correlation coefficient, which are related to the sizes, shapes, and phases of hydrometeor particles. In this paper, the four polarimetric observables combined with environmental temperature are con- sidered as five input parameters, and a fuzzy logic algorithm for hydrometeor particle identification is developed and implemented to discriminate ten different hydrometeor types including drizzle, rain, wet graupel, dry graupel, small hail, large hail, rain and hail mixture, wet snow, dry snow, and ice crystals. The identification algorithm is tested and estimated by using the radar data observed in southern and northern China, and comparing the results with the surface fieldobservation and airborne instrument observations. The classification results indicate that the fuzzy logic algorithm is rea- sonable and practicable.