Vol.31,  No.3, 
May  2020
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Xu Shuyang, Wu Chong, Liu Liping. Parameter improvements of hydrometeor classification algorithm for the dual-polarimetric radar. J Appl Meteor Sci, 2020, 31(3): 350-360. DOI:  10.11898/1001-7313.20200309.
Citation: Xu Shuyang, Wu Chong, Liu Liping. Parameter improvements of hydrometeor classification algorithm for the dual-polarimetric radar. J Appl Meteor Sci, 2020, 31(3): 350-360. DOI:  10.11898/1001-7313.20200309.
shu

Parameter Improvements of Hydrometeor Classification Algorithm for the Dual-polarimetric Radar

  • 1.

    College of Electronic Engineering, Chengdu University of Information Technology, Chengdu 610225

  • 2.

    Chinese Academy of Meteorological Sciences, Beijing 100081

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    Abstract
  • Most of recent hydrometeor classification schemes for dual-polarimtric radar are based on fuzzy logic. Due to the lack of true value of hydrometeors, it is difficult to verify whether classifications are right or not. Therefore, three methods are proposed, cumulative value test, algorithm sensitivity test and hydrometeors' distribution test. Cumulative value test is used to inspect the ability of classifying and distinguishing. When input data contains system deviation and noise, the output classification would also contain system deviation and noise. Hence, a method is proposed to test the sensitivity of system deviation and noise. Hydrometeor distribution test is to analyze whether the hydrometeor distribution is temporal and spatial continuous. Through these tests the reliability and stability of the algorithm are analyzed, and key factors which affect the classification can be found out. Using observations of precipitation processes in Guangzhou in spring and summer from 2016 to 2017, the efficiency of S-band dual-polarization doppler radar is examined. Main results show that the classifying hydrometeor relies on the membership function. Using cumulative value test, some hydrometeors are found out with inappropriate membership function. These membership functions are not consistent with real characteristics of hydrometeors, which are ground clutter (including that due to anomalous propagation), biological scatters, dry aggregated snow, crystals of carious orientations, light and moderate rain, and a mixture of rain and hail. The way to modify these membership functions is based on hydrometeor statistic characteristics. Another insufficiency of membership function is to distinguish similar hydrometeors, such as crystals of various orientations with dry aggregated snow and heavy rain with mixture of rain and hail. The method to modify membership function's distinguishing ability is increasing parametric weights which has stronger discriminating ability. To ensure every hydrometeor has more than 90% stable results, the error of Zh is between -0.5 dB and +0.5 dB, that of ZDR is between -0.1 dB and +0.1 dB, that of ρhv is less than 0.02, and that of KDP is between -0.3 dB and +0.9 dB. Moreover, data quality of Zh and ZDR is more important than other parameters, system deviation is more influencing than noise. After hydrometeor distribution test, it is found that heavy rain may be misclassified as mixture of rain and hail. The spatial consistency correction method is to check whether the distribution of rain and hail mixture in a certain space is continuous.
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  • Fig  1.   Algorithm sensitivity to data error

    (a)sensitivity to Zh system deviation, (b)sensitivity to ZDR system deviation, (c)sensitivity to Zh artificial noise, (d)sensitivity to ZDR artificial noise

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    Fig  2.   Distribution of hydrometeor heights from 0800 BT to 1700 BT on 21 Apr 2017 (the shaded denotes the frequency)

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    Fig  3.   Zh and hdyrometeors' horizontal and vertical structure at 1354 BT 21 Apr 2017

    (a)Zh horizontal structure, (b)hdyrometeor horizontal structure, (c)Zh vertical structure, (d)hdyrometeor vertical structure

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    Fig  4.   Hydrometeor variable feature and improvement of membership function(the shaded denotes the frequency)

    (a)Zh-ρhv feature for biological scatterers, (b)σZh-σФDP feature for biological scatterers, (c)Zh-ρhv feature for ground clutter, including that due to anomalous propagation, (d)σZh-σФDP feature for ground clutter, including that due to anomalous propagation, (e)Zh-ZDR feature for crystals of various orientations(the orange line) and dry aggregated snow(the black line), (f)Zh-ρhv feature for crystals of various orientations and dry aggregated snow, (g)Zh-ZDR feature for a mixture of rain and hail, (h)Zh-KDR feature for a mixture of rain and hail

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    Fig  5.   Variability of A and ΔA for crystals, heavy rain and a mixture of rain and hail after changing ZDR weight

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    Table  1   Periods and characteristics of precipitation samples

    云的特征 时段
    层状云 2016-06-04—05
    层状云加对流云 2017-03-18—20
    2017-04-12—13
    线状对流单体 2017-04-26—27
    零散对流单体 2017-03-07—09
    2017-03-25—26
    2017-03-29—30
    2017-05-07—08
    对流单体合并 2017-04-11—12
    2017-04-19—20
    飑线 2017-05-04—05
    2017-03-31—04-01
    2017-05-08—09
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    Table  2   Distribution of A and proportion of ΔA ≤ 0.1

    相态 0≤A < 0.3 0.3≤A < 0.7 0.7≤A≤1 ΔA≤0.1 A2主要相态
    地物回波 10.40% 65.95% 23.65% 44.61% 晴空回波
    晴空回波 12.75% 76.80% 10.45% 19.29% 地物回波
    干雪 18.29% 26.09% 55.62% 21.37% 冰晶
    湿雪 0.02% 38.77% 61.21% 11.78% 干雪
    冰晶 15.64% 62.48% 21.88% 35.49% 干雪
    霰 0.00% 20.93% 79.07% 22.66% 冰晶
    大雨滴 1.86% 45.66% 52.48% 33.05% 小到中雨
    小到中雨 17.33% 39.57% 43.10% 20.70% 大雨
    大雨 0.04% 31.99% 67.96% 37.25% 冰雹
    冰雹 0.11% 76.86% 23.03% 48.08% 大雨
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    Table  3   Insufficiency of hydrometeor classification results

    识别效果的不足 冰雹 大雨 霰 冰晶 干雪 地物 晴空
    隶属函数不合理 是 否 否 是 是 是 是
    相态区分度不足 是 是 否 是 否 是 否
    对Zh误差敏感 是 是 是 是 否 否 否
    对ZDR误差敏感 是 否 是 否 是 否 否
    空间分布异常 是 否 否 否 否 否 否
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    Table  4   Membership functions before and after modification

    相态 偏振参量 原隶属函数 新隶属函数
    地物 Zh/dBZ (15, 20, 70, 80) (-19, -16, -2.5, 1.5)
    ρhv (0.4, 0.6, 0.9, 0.95) (0.43, 0.48, 0.85, 0.95)
    σZh/dBZ (2, 4, 10, 15) (0.5, 1.0, 4.5, 6.5)
    σФDP/(°) (30, 40, 100, 120) (15, 35, 125, 136)
    晴空回波 Zh/dBZ (5, 10, 20, 30) (-5, -3, 4.5, 6.5)
    ρhv (0, 2, 10, 12) (0.5, 0.6, 0.83, 0.9)
    σФDP/(°) (8, 10, 100, 120) (15, 23, 103, 120)
    干雪 ZDR/dB (-0.3, 0.0, 0.3, 0.6) (-0.5, -0.3, 0.3, 0.6)
    ρhv (0.95, 0.98, 1.00, 1.01) (0.94, 0.97, 1.00, 1.01)
    冰晶 ρhv (0.95, 0.98, 1.00, 1.01) (0.94, 0.97, 1.00, 1.01)
    大雨 KDP/dB (g1-1, g1, g2, g2+1) (g1+6, g1+7, g2+1, g2+2)
    冰雹 ZDR/dB (-0.3, 0.0, f1, f1+0.5) (-1.3, -1, f1, f1+0.5)
    KDP/dB (-10, -4, g1, g1+1) (-10, -4, g1+6, g1+7)
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    Table  5   Proportion and height of misclassified a mixture of rain and hail

    云类型 时段 订正冰雹占比/% 被订正冰雹的平均高度/m 冰雹的平均高度/m
    层状云加对流云 2017-03-18—20 10.73 763.52 1886.25
    2017-04-12—13 23.92 889.94 4084.63
    线状对流单体 2017-04-26—27 8.45 17916.59 2877.42
    零散对流单体 2017-03-07—09 24.71 647.47 1464.55
    2017-03-25—26 17.21 1419.22 3115.25
    2017-03-29—30 18.82 1965.07 2872.65
    2017-05-07—08 19.97 1593.02 2155.58
    对流单体合并 2017-04-11—12 8.81 840.62 2441.58
    2017-04-19—20 8.98 1520.22 4328.00
    飑线 2017-05-04—05 16.71 1456.96 3321.23
    2017-03-31—04-01 0.36 1978.03 2697.98
    2017-05-08—09 8.24 1112.96 3506.09
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    • Received : 2019-12-09
    • Accepted : 2020-02-19
    • Published : 2020-05-30
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