CINRAD/SA-D双偏振天气雷达数据质量评估研究

doi:10.11755/j.issn.1006-7639-2026-04-0670

干旱气象 ›› 2026, Vol. 44 ›› Issue (4): 670-680.DOI: 10.11755/j.issn.1006-7639-2026-04-0670

CINRAD/SA-D双偏振天气雷达数据质量评估研究

张玉洁¹^,²(), 张武³

¹ 山东省气象工程技术中心，山东济南 250031
² 山东省气象防灾减灾重点实验室，山东济南 250031
³ 兰州大学大气科学学院半干旱气候变化教育部重点实验室，甘肃兰州 730000

收稿日期:2026-01-30 修回日期:2026-05-09 出版日期:2026-08-30 发布日期:2026-09-16
作者简介:张玉洁（1972—），女，山东烟台人，硕士，正高级工程师，主要从事探测资料应用研究。E-mail： yjzhyw@163.com。
基金资助:
山东省气象局科学技术研究项目榜单类专项“新一代天气雷达数据质量评估与订正方法研究”(2023SDBD08);中国气象局气象政策研究重点课题(2026ZCYJZDIAN08);山东省自然科学基金创新发展联合基金项目(ZR2025LQX009);中国气象局大气探测重点开放实验室开放课题“基于目标特征的天气雷达电磁干扰抑制技术研究”(2024KLAS05M);山东省气象局科学技术研究项目专题领域专项“基于IQ数据的双偏振天气雷达电磁干扰识别及抑制技术研究”(2024sdztly09)

Research on data quality evaluation of CINRAD/SA-D dual-polarization weather radar

ZHANG Yujie¹^,²(), ZHANG Wu³

¹ Shandong Meteorological Engineering Technology Center， Jinan 250031， China
² Key Laboratory for Meteorological Disaster Prevention and Mitigation of Shandong， Jinan 250031， China
³ Key Laboratory for Semi-Arid Climate Change of the Ministry of Education， College of Atmospheric Sciences of Lanzhou University， Lanzhou 730000， China

Received:2026-01-30 Revised:2026-05-09 Online:2026-08-30 Published:2026-09-16

摘要/Abstract

摘要：

为提高CINRAD/SA-D双偏振天气雷达数据应用质量，基于山东省济南、青岛、烟台、济宁4部CINRAD/SA-D型雷达2023年5—9月观测数据，采用定性定量识别方法，分析4部雷达电磁与地物干扰的空间分布特征，并基于邻域空间相关性的标准差改进算法，对水平反射率因子（Z_H）、差分反射率因子（Z_DR）、协相关系数（ρ_hv）及差分传播相移（Φ_DP）等核心偏振参量的数据稳定性进行定量评估。结果表明：1）电磁干扰呈现“沿海重、内陆轻”的空间分布特性，主要与雷达频点重复配置、沿海站网密度及复杂电磁环境有关；地物干扰表现为点状集中，主要由风电机组运动杂波超出滤波器抑制阈值所致。2）各偏振参量稳定性差异明显：Z_DR稳定性最优，4部雷达超过96%的样本实际标准差低于理论标准差阈值；ρ_hv整体表现较好，达标率超过93%，且新建及技术升级较晚的雷达表现出更优的数据一致性；Z_H稳定性沿海优于内陆，反映了探测环境与下垫面对观测均匀性的综合影响；Φ_DP波动性显著，其数据质量同时受地形复杂度对降水粒子相态分布的调制作用以及雷达系统自身相位测量性能的共同制约。

关键词: CINRAD/SA-D双偏振天气雷达, 数据质量评估, 干扰识别, 标准差分析, 差分反射率因子

Abstract:

To improve the quality of data application for CINRAD/SA-D dual-polarization weather radars, this study utilized observational data from four CINRAD/SA-D radars located in Jinan, Qingdao, Yantai, and Jining, Shandong Province, from May to September 2023, to systematically analyze the spatial distribution characteristics of electromagnetic and ground clutter interference for the four radars using qualitative and quantitative identification methods. Furthermore, based on an improved standard deviation algorithm leveraging neighborhood spatial correlation, the data stability of core polarimetric parameters including horizontal reflectivity factor (Z_H), differential reflectivity (Z_DR), co-correlation coefficient (ρ_hv), and differential propagation phase shift (Φ_DP)_,was quantitatively evaluated. The results show that: 1) Electromagnetic interference exhibits a spatial distribution pattern of “severe in coastal areas, mild in inland areas”, which is primarily attributable to duplication of radar frequency points, coastal network density, and complex electromagnetic environments. Ground object interference presents as point-like concentration, mainly caused by the motion clutter of wind turbines exceeding the filter suppression threshold. 2) The stability of the polarimetric parameters differs significantly. Differential reflectivity (Z_DR) has the best stability, with over 96% of samples from the four radars having standard deviations below the theoretical standard deviation threshold. The co-correlation coefficient (ρ_hv) generally performs well, with a compliance rate exceeding 93%, moreover, the radars with newer construction and later technological upgrades exhibit better data consistency. The stability of Z_His better in coastal areas than in inland areas, reflecting the combined influence of the detection environment and the underlying surface on the uniformity of observations. The fluctuation of Φ_DP is significant. Its data quality is simultaneously constrained by the modulation effect of the complexity of the terrain on the distribution of precipitation particle phases and the phase measurement performance of the radar system itself.

Key words: CINRAD/SA-D dual-polarization weather radar, data quality assessment, interference identification, standard deviation analysis, differential reflectivity

中图分类号:

P412.25

张玉洁, 张武. CINRAD/SA-D双偏振天气雷达数据质量评估研究[J]. 干旱气象, 2026, 44(4): 670-680.

ZHANG Yujie, ZHANG Wu. Research on data quality evaluation of CINRAD/SA-D dual-polarization weather radar[J]. Journal of Arid Meteorology, 2026, 44(4): 670-680.

图/表 10

表1 CINRAD/SA-D型双偏振天气雷达主要性能参数

Tab.1 Performance parameters of CINRAD/SA-D dual-polarization weather radar

项目	性能指标
脉冲宽度	（1.57±0.10）μs（窄），（4.70±0.25）μs（宽）
发射机输出功率	≥650 kW
脉冲重复频率	300~1 300 Hz（窄），300~450 Hz（宽）
天线反射体直径	≥8.5 m
功率增益	≥44.0 dB
波束宽度	≤1 °
第一旁瓣电平	≤-29 dB
最小可测功率	≤-110.0 dBm（窄），≤-114.0 dBm（宽）
噪声系数	≤3.0 dB
动态范围	≥95 dB
距离库分辨率	250 m
输出参数	Z（反射率因子），V（径向速度），W（谱宽），Z_DR，Φ_DP，K_DP，ρ_hv
分辨率	强度≤0.5 dBZ，速度≤0.5 m·s^-1，差分反射率因子≤0.1 dB，差分传播相移≤0.1 °，差分相移率≤0.1（°）·km^-1，协相关系数≤0.005

图1 山东省济南、青岛、烟台、济宁4部CINRAD/SA-D双偏振天气雷达分布

Fig.1 Distribution of four CINRAD/SA-D dual-polarization weather radars in Jinan， Qingdao， Yantai， and Jining， Shandong Province

图2 CINRAD/SA-D型双偏振天气雷达数据质量一体化评估框架

Fig.2 An integrated data quality evaluation framework for CINRAD/SA-D dual-polarization weather radars

图3 S波段雷达邻域空间标准差计算示意图（中心点为待评估距离库，周围8个点为其空间最邻近点，共同构成用于计算的3×3分析单元）

Fig.3 Schematic diagram of the neighborhood spatial standard deviation calculation for S-band radar （The central point is the range bin to be evaluated， the 8 surrounding points are its spatially closest points， and together forming a 3×3 analysis unit for calculation）

图4 2023年5—9月济南、青岛、烟台、济宁4部新一代天气雷达电磁（a）和地物（b）干扰次数分布

Fig. 4 Distribution of frequencies of electromagnetic interference (a) and ground clutter (b) observed by the four next-generation weather radars at Jinan, Qingdao, Yantai, and Jining from May to September 2023

表2 济南、青岛、烟台、济宁双偏振天气雷达水平反射率因子、差分反射率因子、协相关系数、差分传播相移的理论标准差估计值最大值

Tab.2 Estimated maximum theoretical standard deviations of horizontal reflectivity factor， differential reflectivity， co-correlation coefficient， and differential propagation phase shift for the dual-polarization weather radars in Jinan， Qingdao， Yantai， and Jining

雷达站	型号	脉冲重复频率/Hz	雷达波长/cm	SD（${\widehat{\mathit{Z}}}_{\mathrm{H}}$）最大值/dB	SD（${\widehat{\mathit{Z}}}_{\mathrm{D}\mathrm{R}}$）最大值/dB	SD（${\widehat{\mathit{\rho }}}_{\mathrm{h}\mathrm{v}}$）最大值	SD（${\widehat{\mathit{\Phi }}}_{\mathrm{D}\mathrm{P}}$）最大值/（º）
济南	SA-D	322	10.60	2.00	1.57	0.08	3.39
青岛	SA-D	322	10.42	1.99	1.56	0.08	3.36
烟台	SA-D	322	10.71	2.01	1.58	0.08	3.40
济宁	SA-D	322	10.27	1.98	1.55	0.08	3.33

图5 2023年5—9月济南、青岛、烟台、济宁新一代双偏振天气雷达1.5 °仰角反射率因子标准差分布

Fig.5 Distribution of the standard deviation of reflectivity factor at the 1.5° elevation angle for the new generation dual-polarization weather radars in Jinan， Qingdao，Yantai， and Jining from May to September 2023

图6 2023年5—9月济南、青岛、烟台、济宁4部新一代双偏振天气雷达1.5 °仰角差分反射率因子标准差分布

Fig.6 Distribution of the standard deviation of differential reflectivity at the 1.5 ° elevation angle for the four new generation dual-polarization weather radars in Jinan， Qingdao， Yantai， and Jining from May to September 2023

图7 2023年5—9月济南、青岛、烟台、济宁4部新一代双偏振天气雷达1.5 °仰角协相关系数标准差分布

Fig.7 Distribution of the standard deviation of the co-correlation coefficient at the 1.5 ° elevation angle for the four new generation dual-polarization weather radars in Jinan， Qingdao， Yantai， and Jining from May to September 2023

图8 2023年5—9月济南、青岛、烟台、济宁4部新一代双偏振天气雷达1.5 °仰角差分传播相移标准差分布

Fig.8 Distribution of the standard deviation of the differential phase at the 1.5 ° elevation angle for the four new generation dual-polarization weather radars in Jinan， Qingdao， Yantai， and Jining from May to September 2023

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CINRAD/SA-D双偏振天气雷达数据质量评估研究

Research on data quality evaluation of CINRAD/SA-D dual-polarization weather radar

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