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
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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。Abstract: 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 (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 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.
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Key words:
- AP clutter;
- echo identification;
- fuzzy logical
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图 1 天津SA雷达2005年6月21日12:13(世界时,下同)观测 PPI图(a)回波强度,(b)径向速度,(c)回波分类
(红色箭头指向为超折射地物回波, 仰角:0.4°,距离圈:50km;CL表示地物, CC为对流云, SC为层状云, CA为晴空回波)
Fig. 1 PPI of Tianjin rader at 12:13 on June 21, 2005 (a) echo intensity, (b) radial velocity, (c) echo classify
(red rows point to AP (anomalous propagation) clutter, elevation:0.4°, interval of range circles:50km;CL shows clutter, CC shows convective cloud, SC shows stratiform cloud, CA shows clear-air echoes)
图 4 2007年7月7日11:18 天津雷达观测图(红色箭头指向超折射回波, 灰色区域表示识别出来的超折射地物回波, 实线圈范围内为误识别回波)
(a)回波识别前0.4°仰角反射率因子, (b)回波识别前0.4°仰角径向速度, (c)回波识别前1.3°仰角反射率因子, (d) 回波识别前1.3°仰角径向速度, (e) MOP回波识别后的 0.4°仰角反射率因子, (f)MCH回波识别后的0.4°仰角反射率因子
Fig. 4 PPI of Tianjin rader at 11:18 on July 7 2007 (red rows point to MOP clutter, grey block shows the AP clutter that identified correctly, real line circle point to the wrong identification after echo classify)
(a) reflectivity at 0.4°elevation before echo classify, (b) radial velocity at 0.4°elevation before echo classify, (c) reflectivity at 1.3°elevation before echo classify, (d) radial velocity at 1.3°elevation before echo classify, (e) reflectivity at 0.4°elevation after echo classify with MOP, (f) reflectivity at 0.4°elevatation after echo classify with MCH
图 5 2007年10月6日17:18 北京雷达观测图( 红色箭头指向超折射地物回波, 灰色表示识别出来的超折射地物回波, 实线圈处表示误识别回波, 虚线圈处表示本方法识别效果改进处)
(a)回波识别前0.6°仰角反射率因子, (b)回波识别前 0.6°仰角径向速度, (c) 回波识别前 1.6°仰角反射率因子, (d)回波识别前的 1.6°仰角径向速度, (e)MOP回波识别后的 0.6°仰角反射率因子, (f) MCH回波识别后的 0.6°仰角反射率因子
Fig. 5 PPI of Beijing radar at 17:18 on October 6, 2007( red rows point to MOP clutter, grey block shows AP clutter that identified correct, real line circle point to the wrong identification after echo classify, and the broken line circle shows improvement of this method comparing to the method before)
(a) reflectivity at 0.6°elevation before echo classify, (b) radial velocity at 0. 6°elevation before echo classify, (c) reflectivity at 1.6°elevation before echo classify, (d) radial velocity at 1. 6°elevation before echo classify, (e) reflectivity at 0.6°elevation after echo classify withMOP, (f) reflectivity at 0.6°elevatation after echo classify with MCH
表 1 不同隶属函数下各参量的识别准确率(单位: % )
Table 1 Identifiable accuracy of each characteristic parameter with different membership function(unit: % )
表 2 对地物 、降水回波识别准确率和对降水回波误判率 (单位: % )
Table 2 Identifiable accuracy for clutter and precipitation echo and erroneous recognition for precipitation echo (unit: % )
表 3 仅使用回波强度时对地物 、降水回波识别的准确率和对降水的误判率(单位: % )
Table 3 Identifiable accuracy for clutter and precipitation echo and erroneous recognition for precipitation echo with only echo intensity(unit:%)
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