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Parameter Improvements of Hydrometeor Classification Algorithm for the Dual-polarimetric Radar
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摘要: 双偏振雷达的水凝物相态识别算法基于模糊逻辑方法建立，针对方法的可靠性和稳定性问题，利用2016—2017年暖季广州S波段双偏振雷达数据，从3个方面找出影响识别效果的关键因素并改进。使用模糊逻辑的累加值为检验依据，找出不合理的模糊规则，通过相态特征统计和权重矩阵修改加以改进。使用误差敏感性检验法系统，评估误差对识别效果的影响，发现反射率因子的误差在-0.5~+0.5 dBZ、差分反射率因子的误差在-0.1~+0.1 dB、雷达相关系数的误差在0~0.02、差分相移率的误差在-0.3~+0.9 dB的范围内，识别结果稳定性较好。此外，相态时空分布统计中发现底层冰雹面积异常增加，通过空间一致性检验可订正异常结果。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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图 1 相态识别对误差敏感性的检验
(a)对Zh系统偏差的敏感性, (b)对ZDR系统偏差的敏感性, (c)对Zh噪声的敏感性, (d)对ZDR噪声的敏感性
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
图 2 2017年4月21日08:00—17:00部分相态高度的时空分布(填色表示频率)
Fig. 2 Distribution of hydrometeor heights from 0800 BT to 1700 BT on 21 Apr 2017 (the shaded denotes the frequency)
图 3 2017年4月21日13:54水平和垂直方向的Zh和相态结构
(a)Zh水平分布, (b)相态水平分布, (c)Zh垂直结构, (d)相态垂直结构
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
图 4 相态偏振参量特征和隶属函数改进(填色表示频率)
(a)晴空回波的Zh-ρhv, (b)晴空回波的σZh-σФDP, (c)地物回波的Zh-ρhv, (d)地物回波的σZh-σФDP, (e)冰晶和干雪的Zh-ZDR(新隶属函数干雪为橙线，冰晶为黑线, 下同), (f)冰晶和干雪的Zh-ρhv, (g)冰雹的Zh-ZDR, (h)冰雹的Zh-KDR
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
图 5 冰晶、大雨和冰雹改变ZDR权重后A和ΔA的变化趋势
Fig. 5 Variability of A and ΔA for crystals, heavy rain and a mixture of rain and hail after changing ZDR weight
表 1 样本时段和降水云特征
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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表 2 A的分布和ΔA≤0.1的比例
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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表 3 识别效果分析存在不足的相态
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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表 4 改进前后隶属函数
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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表 5 误判冰雹的比例和高度
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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