Vol.32,  No.2, 
March  2021
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Wu Chong, Liu Liping, Yang Meilin, et al. Key technologies of hydrometeor classification and mosaic algorithm for X-band polarimetric radar. J Appl Meteor Sci, 2021, 32(2): 200-216. DOI: 10.11898/1001-7313.20210206.
Citation: Wu Chong, Liu Liping, Yang Meilin, et al. Key technologies of hydrometeor classification and mosaic algorithm for X-band polarimetric radar. J Appl Meteor Sci, 2021, 32(2): 200-216. DOI: 10.11898/1001-7313.20210206.
shu

Key Technologies of Hydrometeor Classification and Mosaic Algorithm for X-band Polarimetric Radar

  • 1).

    Chinese Academy of Meteorological Sciences, Beijing 100081

  • 2).

    Institute of Urban Meteorology, CMA, Beijing 100089

  • 3).

    Nanjing University of Information Science & Technology, Nanjing 210044

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    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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    • References (34)

  • 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)

    DownLoad: Full-Size Img PowerPoint

    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

    DownLoad: Full-Size Img PowerPoint

    Fig  3.   The profiles of Gauss functions and the minimum reflectivity of BJXSY radar

    (a)Fatt, (b)minimum reflectivity, (c)Fsnr, (d)Frhv

    DownLoad: Full-Size Img PowerPoint

    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)

    DownLoad: Full-Size Img PowerPoint

    Fig  5.   The CAPPI of mosaic test at 2.5 km height observed by BJ-Xnet at 0620 BT 31 Jul 2016

    DownLoad: Full-Size Img PowerPoint

    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

    DownLoad: Full-Size Img PowerPoint

    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)

    DownLoad: Full-Size Img PowerPoint

    Fig  8.   The same as in Fig. 7, but for comparison of vertical structures between BJSDX and BJ-Xnet

    (positions of vertical section are marked by black solid lines in the CAPPIs)

    DownLoad: Full-Size Img PowerPoint

    Fig  9.   The same as in Fig. 7, but for comparison between BJSDX and BJ-Xnet on 30 Jul 2016

    DownLoad: Full-Size Img PowerPoint

    Fig  10.   The same as Fig. 8, but for comparison between BJSDX and BJ-Xnet on 30 Jul 2016

    DownLoad: Full-Size Img PowerPoint

    Fig  11.   The same as Fig. 7, but for comparison between BJSDX and BJ-Xnet on 20 Jul 2016

    DownLoad: Full-Size Img PowerPoint

    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
    DownLoad: CSV
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    Article views: 2928 PDF downloads: 398 Cited by: 21
    • Received : 2020-06-30
    • Accepted : 2020-10-18
    • Published : 2021-03-30
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