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Key Technologies of Hydrometeor Classification and Mosaic Algorithm for X-band Polarimetric Radar
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摘要: X波段双偏振雷达具有时空分辨率高、易于布网的特点，但散射特性差异和衰减影响使现有S波段雷达的相态识别和拼图算法不适用于X波段双偏振雷达。该文针对X波段相态识别及拼图产品的关键技术开展研究，提出基于准垂直剖面的融化层识别方法、基于数据质量的置信度阈值调整方法、基于统计的隶属函数参数改进方法和基于衰减程度的拼图融合方法。通过对比改进后可有效提升水凝物相态识别结果的可靠性和多雷达拼图结果的合理性。在2016年汛期北京典型个例中，融合后的X波段雷达网与当地S波段业务雷达相比能够提供更精细的回波结构和水凝物相态分布，有效缓解S波段雷达在近处探测能力降低的问题，识别的降雹区与地面观测相符。Abstract: The advantages of X-band polarimetric weather radar focus on its high spatio-temporal resolution and capability of multi-radar networking. However, the previously designed hydrometeor classification algorithm (HCA) for S-band weather radar is unsuitable for X-band weather radar due to the difference of backscattering characteristics and heavy precipitation attenuation. Therefore, the key technologies of hydrometeor classification algorithm and multi-radar mosaic algorithm for X-band polarimetric weather radar are proposed. First, it is found that the melting layer detection algorithm designed for S-band polarimetric weather radar is not suitable for X-band weather radar through analysis on the data collected by Beijing X-band radar network. A melting layer detection method based on quasi-vertical profile is proposed, which greatly improves the accuracy of obtaining the melting information. Second, a confidence threshold adjustment method is proposed to accurately estimate the data quality in the case of precipitation and clutter superposition. Third, an optimization method of membership functions based on data statistics is proposed to reconstruct the classification parameters suitable for Beijing X-band radar network. Finally, a multi-radar mosaic method based on rainfall attenuation is proposed, in which the reflectivity factors of networking radars are weighted and averaged by the data quality factor. Compared with the traditional method, it is found that the structural inhomogeneity of X-band radar mosaic result is effectively reduced. Those modifications effectively enhance the reliability of classification mosaic results of X-band weather radar network and provide technical support for the rapid deployment of X-band radar in China. Three typical precipitation cases in Beijing during the flood season in 2016 are used to compare the observational efficiency between X-band weather radar network and S-band operational radar. For the cases of convective rainfall, fine echo structures and reasonable hydrometeor distributions are found in X-band radar mosaic results. Especially for convective rainfall with short duration and small spatial scale, the advantage of X-band radar is more obvious, which alleviates the limited detection ability of S-band operational radar in urban areas. In addition, the hail falling area identified by X-band radar can be verified by manual observation in national weather stations. The performance of X-band weather radar network in large-scale stratiform precipitation, however, is not as good as S-band weather radar.
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图 1 BJ-Xnet与BJSDX分布及波束最低覆盖高度对比
(黑色虚线框表示北京市主城区)
Fig. 1 Site distribution and the minimum height of radar coverage of BJ-Xnet and BJSDX
(the black dotted line indicates the scope of Beijing downtown)
图 2 BJXSY雷达分别使用MLDA算法和QVP算法得到的融化层识别结果
(a)2016年7月20日07:32 MLDA算法识别的融化物空间分布(散点)，(黑色实线表示算法识别的融化层顶和底层随方位的变化，黑色虚线表示探空的融化层顶高度)
(b)2016年7月20日01:00—17:00 BJXSY雷达使用QVP算法得到的融化层识别结果Fig. 2 Melting Layers identified by MLDA and QVP for BJXSY radar
(a)distribution of melting particles identified by the MLDA at 0732 BT 20 Jul 2016(the scattered)
(black solid lines represent the top and bottom of melting layers varying with azimuth, and black dotted line represents the height of melting layer obtained by upair sounding),(b)melting layers identified by QVP from 0100 BT to 1700 BT on 20 Jul 2016
图 3 BJXSY雷达置信度和灵敏度廓线
(a)Fatt, (b)灵敏度, (c)Fsnr, (d)Frhv
Fig. 3 The profiles of Gauss functions and the minimum reflectivity of BJXSY radar
(a)Fatt, (b)minimum reflectivity, (c)Fsnr, (d)Frhv
图 4 BJXSY雷达2016年汛期观测数据统计得到的与不同降水相态对应的ZH-ZDR, ZH-ρhv, ZH-KDP(l)频次图
(黑框和红框分别表示默认和改进后的隶属函数参数, 实线和虚线分布表示值为1和0的隶属函数区间分界线)
Fig. 4 The frequency diagrams of ZH-ZDR, ZH-ρhv and ZH-KDP(l) of different hydrometeors from BJXSY radar in flood season of 2016
(the black and red boxes represent the default and modified membership functions respectively, and the solid line and dotted lines represent the boundary of membership function with values of 1 and 0)
图 5 2016年7月31日06:20 BJ-Xnet在2.5 km高度拼图试验的CAPPI
Fig. 5 The CAPPI of mosaic test at 2.5 km height observed by BJ-Xnet at 0620 BT 31 Jul 2016
图 6 2016年7月31日06：20 BJ-Xnet在2.5 km高度分别使用DW方法, PW方法和QW方法得到的ZH拼图结果
Fig. 6 Mosaic results of ZH by DW method, PW method and QW method for BJ-Xnet at 2.5 km height at 0620 BT 31 Jul 2016
图 7 2016年7月27日BJSDX与BJ-Xnet 2.5 km高度CAPPI对比
(黑色虚线框表示北京市主城区, 黑色实线表示垂直剖面的选取位置)
Fig. 7 The comparison of CAPPI between BJSDX and BJ-Xnet at 2.5 km height on 27 Jul 2016
(the black dotted line indicates the scope of Beijing downtown, the black solid line indicates position of vertical sections)
表 1 BJ-Xnet改进前后隶属函数的参数对比
Table 1 Parameters of membership functions of BJ-Xnet before and after modification
相态 隶属函数 单位 默认隶属函数参数 改进后隶属函数参数 x1 x2 x3 x4 x1 x2 x3 x4 干雪 P[ZDR] dB -0.3 0 0.3 0.6 -0.3 -0.1 0.4 0.6 干雪 P[ρhv] 0.95 0.98 1 1.01 0.95 0.97 1 1.01 冰晶 P[ρhv] 0.95 0.98 1 1.01 0.95 0.97 1 1.01 冰晶 P[KDP(l)] dB·km-1 -5 0 10 15 -30 -25 10 20 湿雪 P[ZH] dBZ 25 30 40 50 20 23 37 45 湿雪 P[ZDR] dB 0.5 1 2 3 0 0.5 2 2.5 湿雪 P[ρhv] 0.88 0.92 0.95 0.985 0.86 0.88 0.96 0.985 霰 P[ZDR] dB -0.3 0.0 f1 f1+0.3 -1.5 -0.8 0.0 0.5 雨夹雹 P[ZDR] dB -0.3 0.0 f1 f1+0.5 -1.5 -1.0 f1+0.3 f1+0.8 雨夹雹 P[ρhv] dB 0.85 0.90 1.00 1.01 0.80 0.90 1.00 1.01 雨夹雹 P[KDP(l)] dB·km-1 -10 -4 g1 g1+1 -10 -4 5 7 晴空回波 P[ZDR] dB 0 2 10 12 -5 -2 2 7 晴空回波 P[ρhv] 0.3 0.5 0.8 0.83 0.2 0.3 0.75 0.83 晴空回波 P[σ(ZH)] (°) 1 2 4 7 1 1.5 5 8 晴空回波 P[σ(ΦDP)] (°) 8 10 40 60 8 15 120 150 
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