多普勒天气雷达地物回波特征及其识别方法改进

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CINRAD/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。

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

Radar echoes caused by non-meteorological targets significantly affect radar data quality, and contaminated 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 velocities. 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 acertain 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 (M_OP) is compared against that of the original one such as China currently available membership function (M_CH) and American membership function (M_AM) 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 T_DBZ, G_DBZ, S_PIN, M_DVE, M_DSW, S_DVE can retrieve precipitation 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 functions got from CSI show better result than the original one, especially in distinguishing the precipitation echo from clutter. The algorithm performs well, but the result isn't hundred-percent correct yet. Through individual case analysis, it's found out the cause for the wrong classifying is echo intensity's horizontal texture and velocity's range unfold which is unavoidable, but it proves velocity data can improve the echo classifying result too. Radar data quality control is a complicated question, just using radar data is not enough to reach a perfect outcome. Satellite or automatic weather station data can be imported to make the result more authentic. And the most effective work on radar data quality control is to combine the manual work to the algorithm, through which all kinds of data problems recognized by auto algorithm can be solved. Radar echo classifying is still a key point in radar data quality control, radar data quality will not be totally exact until the radar echo characteristic is acknowledged and the right way to work it out is chosen, and that will have great effect on the application of radar data.