Identification and application of ZDR column from dual polarization radar based on convective storm structure
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摘要: 双偏振雷达观测到的垂直伸展至环境0℃层之上的柱状差分反射率因子增强区（即ZDR≥1 dB），被称为ZDR柱。ZDR柱可以提供对流风暴上升气流的位置和强度信息，是分析对流风暴演变的有力工具。为了实现对ZDR柱的自动识别并提供用于对流风暴预警的诊断信息，基于对流风暴的三维形态特征，使用厦门双偏振雷达观测数据设计了ZDR柱识别算法，并提取ZDR柱形态参数。结合地面观测资料，探索ZDR柱形态参数在对流风暴定量化分析领域的应用。研究表明：（1）强风暴和非强风暴在ZDR柱形态参数上存在统计学上的明显差异，这为预报员据此判别两类对流风暴提供了参考依据。当ZDR柱深度达到1500 m后，至少有60%的雷达体扫个数与强风暴相关。ZDR柱体积、质心高度和最大ZDR值的阈值达到20 m3、500 m和3 dB时，这一比例分别达到70%、70%和50%。（2）ZDR柱的演变可较好地指示对流风暴的发展过程，其形态参数的极值早于强对流天气现象出现。在连续性强对流天气过程中，ZDR柱的再度发展预示着对流风暴的再次增强。（3） ZDR柱对于风暴的合并与分裂过程具有预示性。在风暴合并（分裂）过程中伴有ZDR柱合并（分裂）的现象，其中有57%（69%）的过程ZDR柱提前于对流风暴发生合并（分裂）。（4）ZDR柱的位置与对流风暴的后续传播方向存在相关，可为改善对流风暴移动路径的预测提供参考依据。Abstract: ZDR column, the quasi-vertical continuous column of enhanced differential reflectivity (i.e., ZDR≥1 dB) observed by dual-polarization radar, can extend well above the environmental 0℃ level. ZDR column can provide information about the location and intensity of convective storm updraft, which makes it a useful tool for analyzing the evolution of convective storms. This paper introduces an automatic ZDR column identification algorithm, which is designed to provide diagnostic information pertinent to convective storm warning. Based on the 3D structure characteristics of convective storms, the algorithm for ZDR column identification is designed and its morphological parameters are calculated. The application of ZDR column morphological parameters in quantitative analysis of convective storms is explored by using Xiamen dual polarization radar and automatic weather station data. The study yields the following results. (1) Statistically significant differences exist between severe and non-severe storms in terms of the ZDR column morphological parameters, indicating that these products can provide references for forecasters to distinguish the two types of convective storms. Once the ZDR column depth reaches 1500 m, at least 60% of the volume scans are associated with severe storms. Similarly, once the thresholds for ZDR column volume, centroid height and maximum ZDR value reach 20 m3, 500 m and 3 dB, respectively, at least 70%, 70% and 50% of the volume scans are associated with severe storms. (2) The evolution of ZDR columns is an appropriate index that can reflect the development of convective storms, and the peak values of its morphological parameters precede the occurrence of severe convective weather. During the continuous severe convective weather process, the re-development of the ZDR column is earlier than that of the convective storm. (3) ZDR columns can predict the merging and splitting process of convective storms. The process of storm merging (splitting) is accompanied by the ZDR column merging (splitting). ZDR column merging (splitting) occurs earlier than that of the convective storm in 57% (69%) of the processes. (4) There is a correlation betweenthe position of the ZDR column and subsequent propagation direction of the convective storm, which can provide a reference for improving the prediction of the movement path of convective storm.
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Key words:
- Dual-polarization radar /
- ZDR column /
- Identification algorithm /
- Convective storm
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图 1 厦门S波段双偏振雷达周边强对流天气报告及相关观测仪器分布
Figure 1. Distribution of severe convective weather reports by Xiamen S-band dual polarization radar and observation stations
图 6 ZDR柱深度 （a）、体积 （b）、质心高度 （c） 和最大值 （d） 的直方图 （虚线表示第10和第90百分位，并标注相应数值）
Figure 6. Histogram of ZDR column depth （a），volume （b），centroid height （c） and maximum value （d） （dotted lines indicate the 10th and 90th percentiles，and the corresponding values are also listed）
图 7 ZDR柱深度 （a）、体积 （b）、质心高度 （c） 和最大值 （d） 的小提琴图 （图中的蓝 （红） 色区域代表概率密度，其宽度越大则出现的频率越高；图中的箱体为四分位间距框，白色点为中位数）
Figure 7. Violin plots of ZDR column depth （a），volume （b）， centroid height （c） and maximum value （d） （areas shaded in blue （red） show the probability density， and a greater width indicates a higher frequency of occurrence； boxes in each plot mark the interquartile range， and the white dot denotes the median value）
图 8 ZDR柱深度 （a）、体积 （b）、质心高度 （c） 和最大值 （d） 取不同阈值时强风暴和非强风暴的占比
Figure 8. Percentages of all volume scans that are associated with severe and non-severe storms at various thresholds for ZDR column depth （a），volume （b），centroid height （c） and maximum value （d）
图 9 ZDR柱深度 （a）、体积 （b）、质心高度 （c） 和最大值 （d） 极值相对于强对流天气报告的提前量 （箱体为四分位间距框，横线为中位数，b中圆圈为离散数据）
Figure 9. Lead times of the maximum ZDR column depth （a），volume （b）， centroid height （c） and maximum value （d） that occur prior to severe convective weather reports （the boxes mark the interquartile range，and the horizontal line marks the median value， in Fig. b the circles represent discrete data）
图 10 2019年5月17日 （a） 和6月9日 （b） 厦门双偏振雷达观测到的对流风暴ZDR柱深度时序
Figure 10. Time series of ZDR column depth observed by Xiamen dual polarization radar for the convective storms on 17 May （a） and 9 June （b） 2019
图 11 2018年5月23日15时11分 （a）、15时17分 （b） 和15时23分 （c） 厦门S波段雷达6.0°仰角反射率因子 （a1—c1）、径向速度 （a2—c2） 和差分反射率因子 （a3—c3）
Figure 11. ZH （a1—c1），Vr （a2—c2） and ZDR （a3—c3） from Xiamen S-band radar taken at 6.0° elevation at 15:11 BT （a），15:17 BT （b） and 15:23 BT （c） 23 May 2018
图 12 2018年7月31日13时55分 （a）、14时07分 （b） 和14时18分 （c） 厦门双偏振雷达3.4°仰角反射率因子 （a1−c1）、径向速度 （a2—c2） 和差分反射率因子 （a3—c3）
Figure 12. ZH （a1—c1），Vr （a2—c2） and ZDR （a3—c3） from Xiamen dual-polarization radar taken at 3.4° elevation at 13:55 BT （a），14:07 BT （b） and 14:18 BT （c） 31 July 2018
图 13 2018年7月31日13时55分 （a）、14时07分 （b） 和14时18分 （c） 沿图12中线段ab、cd和ef所做垂直剖面 （a1—c1. 反射率因子，a2—c2. 径向速度，a3—c3. 差分反射率因子）
Figure 13. Vertical cross sections along lines "ab"，"cd" and "ef" shown in Fig. 12 at 13:55 BT （a），14:07 BT （b） and 14:18 BT （c） 31 July 2018 （a1—c1. ZH，a2—c2. Vr，a3—c3. ZDR）
图 14 2019年4月22日 （a） 和2018年5月23日 （b） 厦门双偏振雷达观测的风暴路径和ZDR柱路径 （风暴路径及其预测路径源自SCIT算法）
Figure 14. Storm paths and ZDR column paths observed by Xiamen dual-polarization radar on 22 April 2019 （a） and 23 May 2018 （b） （the storm tracks and forecast paths are derived from the SCIT algorithm）
表 1 各类型风暴数量及其体扫数
Table 1. Number of storms and volume scans analyzed for various storm types
风暴类型 风暴数量（个） 体扫数（个） 强风暴 30 619 非强风暴 30 367 冰雹强风暴 10 231 雷暴大风强风暴 10 216 短时强降水强风暴 10 172 
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