多普勒天气雷达地物回波特征及其识别方法改进  

Statistical Characteristics of Clutter and Improvements of Ground Clutter Identification Technique with Doppler Weather Radar

作　　者：江源[1] 刘黎平[1] 庄薇[1] 

机构地区：[1]中国气象科学研究院灾害天气国家重点试验室,北京100081

出　　处：《应用气象学报》2009年第2期203-213,共11页Journal of Applied Meteorological Science

基　　金：国家自然科学基金项目(40775021);973项目"我国南方致洪暴雨监测与预测的理论和方法研究"(2004CB418305);气象行业专项"热带西太平洋观测试验与我国高影响天气可预报性研究";"相控阵天气雷达关键技术研究"和灾害性天气国家重点实验室自主研究课题共同资助

摘　　要：非气象因子会在雷达探测时对雷达资料造成污染,并导致雷达数据的质量问题,在雷达数据应用之前必须对被污染的距离库进行识别和处理。该文在现有基于模糊逻辑识别地物回波工作的基础上,发展适合于我国CIN-RAD/SA的地物回波识别方法,采用北京和天津雷达2005,2006年夏季部分时段体扫资料,同时对反射率因子和径向速度以及速度谱宽进行处理,得到不同回波的各种特征,并对各种回波特征进行分析;考虑到隶属函数的确定是地物识别准确率的关键,运用CSI(critical success index)评判标准确定了模糊逻辑超折射地物回波识别的最佳线性梯形隶属函数;通过识别效果分析说明该方法在识别超折射地物回波中的作用。结果表明:运用改进后的模糊逻辑法可以更好地识别地物回波,特别是那些超折射地物回波;与原方法相比,改进后的方法有效减少了对降水回波的误判。Radar echoes caused by non-meteorological targets significantly affect radar data quality, and contami- nated bins by ground clutter should be identified and eliminated before precipitation can be quantitatively estimated from radar data. An automatic algorithm for ground clutter detection is developed and examined. The algorithm is based on fuzzy logic, using volume scanning radar raw data. It uses some statistics to highlight clutter characteristics, such as shallow vertical extent, high spatial variability, and low radial ve- locities. A value that quantifies the possibility of each bin being affected by clutter is derived, and then certain impacts can be eliminated when this factor exceeds a certain threshold. The ground clutter points in sample data are distinguished empirically. In order to reduce the identified inaccuracy of the precipitation echoes with least infections on the ground clutter identified veracity, the optimal membership functions are determined by analyzing statistic the precipitation and ground clutter with the critical success index （CSI） based on the standard ground clutter and precipitation data. CSI is obtained based on the identified veracity through all samples includes clutters and precipitation of each function performs. The performance of this algorithm （Mop） is compared against that of the original one such as China currently available membership function （MCH） and American membership function （MAM） by testing with statistical analysis, individual cases analysis, and inaccurate result analysis methods. Satisfactory results are obtained from an exhaustive evaluation of this algorithm, especially in the cases where anomalous propagation plays an important role. It turns out six characteristic parameters including TDBZ, GDBZ, SPIN, MDVE, MDsw, SDVE can retrieve precipi- tation echo and clutters well. Radial velocity used in algorithm shows it is good for echo classifying, it will reduce the possibility of identifying the precipitation echo to clutter. The membership

关 键 词：超折射地物回波 回波识别 模糊逻辑 

分 类 号：P412.25[天文地球—大气科学及气象学]