Error Evaluation and Hydrometeor Classification Method of Dual Polarization Phased Array Radar

Li Zhe; Wu Chong; Liu Liping; Zong Rong; Luo Ming

doi:10.11898/1001-7313.20220102

Vol.33, NO.1, 2022

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Article Navigation > Journal of Applied Meteorological Science > 2022 > 33(1): 16-28

Li Zhe, Wu Chong, Liu Liping, et al. Error evaluation and hydrometeor classification method of dual polarization phased array radar. J Appl Meteor Sci, 2022, 33(1): 16-28. DOI: 10.11898/1001-7313.20220102.

Citation:

Li Zhe, Wu Chong, Liu Liping, et al. Error evaluation and hydrometeor classification method of dual polarization phased array radar. J Appl Meteor Sci, 2022, 33(1): 16-28. DOI: 10.11898/1001-7313.20220102.

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Error Evaluation and Hydrometeor Classification Method of Dual Polarization Phased Array Radar

DOI: 10.11898/1001-7313.20220102

Li Zhe^{1, 2},
Wu Chong^{2
,
,},
Liu Liping²,
Zong Rong³,
Luo Ming³

1.
Department of Atmospheric and Oceanic Sciences & Institute of Atmospheric Sciences, Fudan University, Shanghai 200438
2.
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081
3.
Shenzhen National Climate Observatory, Shenzhen 518000

Received Date: 2021-09-15
Rev Recd Date: 2021-11-16
Publish Date: 2022-01-19

Abstract

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 Z_H and the differential reflectance Z_DR are relatively large. The error range of Z_H is -0.5-4.5 dB, and the error of Z_DR 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.
- dual polarization phased array radar;
- error analysis;
- hydrometeor classification

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References(25)

Figures and Tables

Fig. 1 Flow chart of DHC method

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图 2 2020年3月18日10:30 X-PAR与S-POL的Z_H和Z_DR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 Z_H and Z_DR 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)

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Fig. 3 Z_H and Z_DR random error comparison frequency and error magnitude ratio of X-PAR

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Fig. 4 Z_H and Z_DR calibration error comparison frequency and error magnitude ratio of X-PAR

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Fig. 5 Z_H and Z_DR attenuation correction error frequency and error magnitude ratio of X-PAR

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Fig. 6 Z_H and Z_DR beam broadening error frequency and error magnitude ratio of X-PAR

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Fig. 7 Phase change rate after introducing system error and random error to Z_H

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Fig. 8 The horizontal structure of Z_H, ρ_hv and Z_DR of X-PAR and S-POL at 0937 BT 18 Mar 2020 (the elevation: 6.3°)

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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°)

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图 10 2020年5月11日23:00 X-PAR和S-POL的Z_H, ρ_hv和Z_DR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 Z_H, ρ_hv and Z_DR 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)

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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)

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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
误差种类	表征误差的变量	数据处理	距离	Φ_DP	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°)
标定误差	仰角	滤波	20~30 km	≤5°	去除标定随时间的误差
衰减订正误差	Φ_DP	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	Z_H标准差、Z_DR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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