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Error Evaluation and Hydrometeor Classification Method of Dual Polarization Phased Array Radar
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摘要: 选取2020年3—9月深圳求雨坛的X波段双偏振相控阵雷达探测数据, 与同位置的S波段双偏振雷达进行对比。通过一定限制条件定量分析引入误差的原因, 发现反射率因子ZH和差分反射率ZDR的标定误差和随机误差较大, 其中ZH误差变化范围为-0.5~4.5 dB, ZDR误差变化范围为-0.7~0.2 dB。在上述较大误差影响下, 传统模糊逻辑相态识别方法的水凝物相态识别结果不可靠, 因此根据不同相态的雷达参量特征范围以及融化层高度建立基本结构为二叉树的决策树相态识别方法。针对上述方法的实际应用效果, 分别从水凝物相态识别结果对误差的敏感性和空间分布的合理性进行评估, 结果表明: 决策树相态识别方法的水凝物相态识别结果稳定性高于模糊逻辑相态识别方法, 且在对流云中的水凝物相态分布更加合理, 能够发挥X波段双偏振相控阵雷达在研究云内水凝物相态演变的优势。Abstract: Phased array radar is faster in scanning speed than mechanical scanning radar, but due to the influence of antenna structure and attenuation, phased array radar will produce larger system error and random error. The mainstream fuzzy logic hydrometeor classification method has little difference in the weight coefficients of each parameter, and the calculated composite values are often close, so the hydrometeor classification results are easily affected by data errors. The detection data of the X-band dual polarization phased array radar at Qiuyutan, Shenzhen, from March to September in 2020 are compared with the S-band dual polarization radar at the same location. The points close to the elevation, azimuth and radial distance of the two radars are obtained to establish matching datasets to calculate the errors of X-band dual-polarization phased array radar. Quantitative analysis of the causes for the introduction of errors through certain restriction conditions reveals that the calibration error and random error of the reflectance factor ZH and the differential reflectance ZDR are relatively large. The error range of ZH is -0.5-4.5 dB, and the error of ZDR is -0.7-0.2 dB. After the preliminary correction of calibration error and random error, it is found that there are still some errors in data, which make the hydrometeor classification result of fuzzy logic method unreliable, so the decision tree hydrometeor classification method with the basic structure of binary tree is established according to the characteristic range of radar parameters of different hydrometeors and the height of the melting layer. In order to verify the practical application effects of the above methods, the error sensitivity of hydrometeor classification results and the rationality of hydrometeor spatial distribution are evaluated respectively. Typical examples are selected to further evaluate the rationality of the decision tree hydrometeor classification method by comparing the parameters and hydrometeor classification results of X-band dual polarization phased array radar and S-band dual polarization radar. The evaluation results show that the stability of the decision tree hydrometeor classification method is higher than that of the fuzzy logic method, and the hydrometeor distribution in the convective cloud is more reasonable, which can give full play to the advantage of X-band dual polarization phased array radar in studying the phase evolution of particles in the cloud.
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图 2 2020年3月18日10:30 X-PAR与S-POL的ZH和ZDR水平结构
(相邻距离圈间距为15 km，+为雷达位置)
Fig. 2 The horizontal structure of ZH and ZDR of X-PAR and S-POL at 1030 BT 18 Mar 2020
(the distance between adjacent circles is 15 km, + denotes the location of radar)
图 3 X-PAR的ZH和ZDR随机误差对比频次及误差量级占比
Fig. 3 ZH and ZDR random error comparison frequency and error magnitude ratio of X-PAR
图 4 X-PAR的ZH和ZDR标定误差对比频次及误差量级占比
Fig. 4 ZH and ZDR calibration error comparison frequency and error magnitude ratio of X-PAR
图 5 X-PAR的ZH和ZDR衰减订正误差对比频次及误差量级占比
Fig. 5 ZH and ZDR attenuation correction error frequency and error magnitude ratio of X-PAR
图 6 X-PAR的ZH和ZDR波束展宽误差对比频次及误差量级占比
Fig. 6 ZH and ZDR beam broadening error frequency and error magnitude ratio of X-PAR
图 7 向ZH分别引入系统误差和随机误差后相态变化率
Fig. 7 Phase change rate after introducing system error and random error to ZH
图 8 2020年3月18日09:37 X-PAR和S-POL的ZH，ρhv和ZDR水平结构(6.3°仰角)
Fig. 8 The horizontal structure of ZH, ρhv and ZDR of X-PAR and S-POL at 0937 BT 18 Mar 2020 (the elevation: 6.3°)
图 9 DHC方法和FHC方法对于2020年3月18日09:37 X-PAR和S-POL探测水凝物相态识别结果
(6.3°仰角)
Fig. 9 Hydrometeor classification results of DHC method and FHC method in X-PAR and S-POL at 0937 BT 18 Mar 2020
(the elevation: 6.3°)
图 10 2020年5月11日23:00 X-PAR和S-POL的ZH, ρhv和ZDR水平结构
(6.3°仰角, 相邻距离圈间距为15 km，+为雷达位置)
Fig. 10 The horizontal structure of ZH, ρhv and ZDR of X-PAR and S-POL at 2300 BT 11 May 2020
(the elevation: 6.3°, the distance between adjacent circles is 15 km, + deontes the location of radar)
图 11 DHC方法和FHC方法对于2020年5月11日23：00 X-PAR和S-POL探测水凝物相态识别结果
(6.3°仰角, 相邻距离圈间距为15 km，+为雷达位置)
Fig. 11 Hydrometeor classification results of DHC method and FHC method in X-PAR and S-POL at 2300 BT 11 May 2020
(the elevation: 6.3°, the distance between adjacent circles is 15 km, + deontes the location of radar)
表 1 X-PAR不同误差的限制条件
Table 1 Limiting conditions for different errors of X-PAR
误差种类 表征误差的变量 限制条件 数据处理 距离 ΦDP 其他限制 标定误差 时间 滤波 20~30 km ≤5° 仰角(X-PAR: 4.5°，S-POL: 4.3°) 仰角 滤波 20~30 km ≤5° 去除标定随时间的误差 衰减订正误差 ΦDP 滤波 25~30 km ≥5° 仰角(X-PAR: 4.5°, S-POL: 4.3°), 去除标定随时间的误差 波束展宽误差 距离 滤波 所有距离 ≤5° 仰角(X-PAR: 4.5°, S-POL: 4.3°), 去除标定随时间的误差 随机误差 ZH标准差、ZDR标准差 未滤波 20~30 km ≤5° 仰角(X-PAR: 4.5°, S-POL: 4.3°), 去除标定随时间的误差 
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