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Non-precipitation Identification Technique for CINRAD/SAD Dual Polarimetric Weather Radar
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摘要: 双偏振雷达观测特征参量（如相关系数、差分反射率等）能有效抑制地物、超折射、电磁干扰、海浪和晴空等非降水回波。在上海南汇WSR-88D双偏振雷达非降水回波识别算法基础上，对我国升级布网且纳入业务运行的CINRAD/SAD双偏振雷达数据进行算法测试、算法模块适应性改进，利用江苏、广东的双偏振雷达观测冰雹、融化层、台风降水及各种杂波个例进行算法检验评估，并在组网拼图中展示质量控制效果。结果表明：总体上算法对非降水回波的识别准确率达到95.2%，降水回波的误判率为2.6%。但对夏秋季节夜晚的大面积晴空回波算法识别准确率低于90%，有待尝试利用深度学习方法改进。Abstract: In China, the operational upgrade of dual polarimetric weather radar is being promoted. CINRAD/SAD dual polarimetric weather radar in some provinces such as Guangdong, Jiangsu, Shandong and Zhejiang has been upgraded in operation. By June of 2021, there are 69 dual polarimetric weather radars in national radar network, and it will increase to more than 100 in the future. The dual polarimetric radar is an important detection equipment for studying the microphysical process of precipitation, which can provide multiple polarizations including raindrop spectrum information, and thus better describe the microphysical characteristics of precipitation. The technical upgrade will bring revolutionary changes for data quality control, hydrogel classification and quantitative precipitation estimation. With the measurement parameters such as correlation coefficient or differential reflectivity, the dual-polarimetric weather radar can effectively remove non-precipitation echoes such as ground clutter, anomalous propagation, electromagnetic interference, sea waves, clear air clutter and so on. Based on the non-precipitation identification technique on S-band WSR-88D dual polarization weather radar, the distribution characteristics of correlation coefficient and differential reflectivity in precipitation echo and clutter are analyzed. The CINRAD/SAD dual-polarimetric weather radar data are used to test and improve the algorithm to adapt domestic weather radar, the differential reflectivity texture feature is added in the improved algorithm and the distribution characteristics of differential reflectivity horizontal texture on precipitation echo and clutter are analyzed, to better remove non-precipitation echo. During the evaluation of algorithm, several cases such as hail, melting layer, typhoon and different types of clutters during May-October in 2019 and 2020 are investigated. The results show that the improved algorithm can identify 95.2% of non-precipitation echoes, and the error rate of precipitation is 2.6%. For the large area clear air clutter, after adding the differential reflectivity texture feature, combining with the correlation coefficient texture feature, the accuracy of the algorithm is improved from 68.6% to 96.8% for one case, but the overall accuracy is less than 90% for many cases, and it needs to be improved by deep learning method in the future. Non-precipitation identification algorithm on CINRAD/SAD is applied in mosaic image, showing great application prospect in the future for precipitation classification and quantitative precipitation estimation. It can provide high quality data and play an important role in real-time operation.
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图 1 降水回波与非降水回波的相关系数和差分反射率
(a)降水回波相关系数，(b)非降水回波相关系数，(c)降水回波差分反射率，(d)非降水回波差分反射率
Fig. 1 Correlation coefficient and differential reflectivity for precipitation echo and clutter
(a)correlation coefficient of precipitation, (b)correlation coefficient of non-precipitation, (c)differential reflectivity of precipitation, (d)differential reflectivity of non-precipitation
图 2 2019年9月3日01:30:19徐州CINRAD/SAD双偏振雷达观测特征参量
(相邻距离圈间隔50 km，下同)
Fig. 2 Parameters observed by CINRAD/SAD dual-polarimetric radar of Xuzhou at 013019 BT 3 Sep 2019
(distance of adjacent circles is 50 km, the same hereinafter)
图 4 降水回波与非降水回波的相关系数和差分反射率的水平纹理特征
(a)降水回波相关系数纹理，(b)非降水回波相关系数纹理，(c)降水回波差分反射率纹理，(d)非降水回波差分反射率纹理
Fig. 4 Texture features of correlation coefficient and differential reflectivity for precipitation and non-precipitation
(a)correlation coefficient texture of precipitation, (b)correlation coefficient texture of non-precipitation, (c)differential reflectivity texture of precipitation, (d)differential reflectivity texture of non-precipitation
图 5 CINRAD/SAD双偏振雷达观测非降水回波识别算法流程
Fig. 5 Flow chart of non-precipitation identification algorithm on CINRAD/SAD dual-polarimetric weather radar
图 6 质量控制前后大面积晴空回波
Fig. 6 Reflectivity factor of large clear air before and after radar quality control
图 7 2019年8月1日13:35徐州CINRAD/SAD双偏振雷达观测冰雹及融化层质量控制前后的反射率因子
Fig. 7 Reflectivity factor of hailstorm and melting layer before and after radar quality control observed by CINRAD/SAD dual-polarimetric weather radar of Xuzhou at 1335 BT 1 Aug 2019
图 8 2019年8月11日00:05盐城CINRAD/SAD双偏振雷达观测台风降水质量控制前后的反射率因子
Fig. 8 Reflectivity factor of typhoon precipitation before and after radar quality control observed by CINRAD/SAD dual-polarimetric weather radar of Yancheng at 0005 BT 11 Aug 2019
图 9 2019年7月30日23:03盐城CINRAD/SAD双偏振雷达观测地物、超折射、干扰杂波质量控制前后的反射率因子
Fig. 9 Reflectivity factor of ground clutter, anomalous propagation and electromagnetic interference process before and after radar quality control observed by CINRAD/SAD dual-polarimetric weather radar of Yancheng at 2303 BT 30 Jul 2019
图 10 2019年9月2日03:36徐州CINRAD/SAD双偏振雷达观测降水与大面积晴空混杂回波质量控制前后的反射率因子
Fig. 10 Reflectivity factor of precipitation and large clear air process before and after radar quality control algorithm observed by CINRAD/SAD dual-polarimetric weather radar of Xuzhou at 0336 BT 2 Sep 2019
图 11 2020年6月12日14:00江苏高邮龙卷过程6部CINRAD/SAD双偏振雷达组网拼图
Fig. 11 Mosaic image of Gaoyou Tornado before and after quality control observed by six CINRAD/SAD dual-polarimetric weather radars at 1400 BT 12 Jun 2020
表 1 多种个例质量控制客观评估表
Table 1 Quality control algorithm evaluation of cases
种类 非降水回波识别准确率/% 降水回波误判率/% 冰雹、融化层 96.5 1.2 台风降水 1.8 电磁干扰、小面积晴空 99.2 地物、超折射、电磁干扰 95.7 2.2 大面积晴空 90.9 2.0 
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