为了提高雷达定量降水估测的精度，建立一套高精度的双偏振雷达定量降水估测方法，并对其在业务应用中的表现进行评估。本文利用雨滴谱仪数据使用非球形粒子的散射模型（T-Matrix模型）进行不同偏振量的模拟计算，根据计算结果对实测雨滴谱数据（DSD）进行分类拟合，实现对CSU-HIDRO（Colorado State University-Hydrometeor Identification Rainfall Optimization）优化降水估测算法的改进。为了评估改进后CSU-HIDRO优化算法（简称CSU-HIDRO_I）的应用效果，本文选取2016～2017年两年汛期发生于中国华南地区的6次大范围强降水过程为评估对象，分别采用单偏振雷达定量降水估测的R(ZH)关系法（WSR-88D Precipitation Processing System，简称PPS法）和CSU-HIDRO_I法进行小时降水量估测。按照不同降水率大小以及距离雷达20～60 km和60~100 km范围分别对两种降水估测方法进行评估，并将雷达估测的小时降水量同地面雨量计小时降水量资料进行对比，结果表明：（1）CSU-HIDRO_I法在应用评估过程中取得了较好的评估效果，其估测精度及稳定性均较好。（2）PPS法对小雨（降水率R<2.5 mm/h）存在一定的高估，对大雨及暴雨（R>8 mm/h）存在明显低估，而CSU-HIDRO_I法能够有效的降低强降水的低估情况，同时提高了小雨的估测精度。与PPS法相比，CSU-HIDRO_I法对小雨、中雨、大雨及暴雨的估测偏差分别降低了38%、24%、17%、15%。（3）PPS法在降水估测中对离雷达的距离更为敏感，相同降水率下不同距离处的相对误差波动较大，CSU-HIDRO_I法对距离敏感性较弱，相同降水率强度下，相对误差随距离的变化波动较小。
To improve the accuracy of radar quantitative precipitation estimation (QPE), a high-precision dual-polarization radar QPE method is established, and its performance in operational application is evaluated. In this study, a nonspherical particle scattering model (i.e., T-matrix model) is used to simulate and calculate different polarization quantities on the basis of the data obtained using an LPA10 disdrometer. On the basis of the calculated results, the measured raindrop spectrum data are classified and fitted to optimize the precipitation estimation algorithm of CSU-HIDRO (Colorado State University-Hydrometeor Identification Rainfall Optimization). Two rainfall cases occurring in 2016 and 2017 in South China are selected to assess the performance of the modified algorithm (CSU-HIDRO_I). The R(ZH) method PPS (WSR-88D Precipitation Processing System) and the CSU-HIDRO_I method are used to estimate the hourly precipitation. On the basis of different rainfall intensities and ranges (i.e., 20-60 km and 60-100 km) obtained by radar, the two precipitation estimation methods are evaluated. Moreover, the hourly precipitation estimated by radar is compared with that estimated by rain gauges. The main results are as follows: (1) The CSU-HIDRO_I method achieves good QPE results, and its estimation accuracy and stability are better than that of the R(ZH) method. (2) The PPS method overestimates during light rainfall (R<2.5 mm/h) and underestimates during heavy rainfall and rainstorm (R>8 mm/h). By contrast, the CSU-HIDRO_I method can effectively reduce the underestimation of heavy rainfall and improve the estimation accuracy during light rainfall. Compared with the PPS method, the estimation deviation of the CSU-HIDRO_I method for light rainfall, moderate rainfall, heavy rainfall, and rainstorm is reduced by 38%, 24%, 17%, and 15%, respectively. (3) The PPS method is more sensitive to the distance from the radar during precipitation estimation than the other methods. Under the same rainfall intensity, the relative error at different distances fluctuates considerably. By contrast, the CSU-HIDRO_I method is less sensitive to the range from the radar than the other methods. Moreover, the variation of its relative error at different distances is smaller than that of the other methods.
郭佳,吴艳锋,罗丽,肖辉.2020. CINRAD-SA偏振雷达定量降水估测算法改进及应用评估[J].气候与环境研究,25(3):305-319. GUO Jia, WU Yanfeng, LUO Li, XIAO Hui.2020. Improvement of the Quantitative Precipitation Estimation Algorithm Based on the CINRAD-SA Polarization Radar and Its Application Evaluation[J]. Climatic and Environmental Research (in Chinese],25(3):305-319.复制