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Automatic Recognition Algorithm of Convergence Region Based on Relative Storm Radial Velocity Field
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摘要: 该文提出一种从相对风暴的径向速度图中自动识别中层径向辐合特征的算法，即相对风暴中层径向辐合特征自动识别算法。算法首先识别出单仰角径向速度图上每个径向的正-负速度段，并按照一定规则对其进行配对，形成径向辐合段；然后在二维锥面上做水平相关分析得到二维径向辐合块，再对二维辐合块进行垂直相关分析，形成风暴的三维径向辐合体，计算其强度、厚度、中心高度等重要特征参数。利用2013年8月18日和2018年8月7日两次非典型“正-负速度区域对”径向辐合特征的飑线雷达资料对该算法进行测试，结果表明：径向辐合特征在相对风暴的径向速度图上的识别效果较原始径向速度场更优。统计分析特征参数与飑线大风的相关性表明：平均径向辐合强度、最大径向辐合强度、厚度与风速之间有较好的线性相关性，且均为正相关，其中平均径向辐合强度与风速之间的相关系数最大，达到0.79。通过算法识别的径向辐合特征可以提前约30 min预警飑线大风。
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关键词:
- 相对风暴径向速度;
- 中层径向辐合;
- 相对风暴中层径向辐合;
- 雷暴大风
Abstract: An algorithm for automatically identifying the mid-altitude radial convergence from the storm-relative radial velocity field is proposed. The algorithm first identifies the positive-negative velocity segments in each radial direction on the single-elevation radial velocity field, before pairing them to form a radial convergence segment. A two-dimensional radial convergence block is obtained through horizontal correlation analysis, and then three-dimensional radial convergence body of the storm is obtained through vertical correlation analysis. Thus, the parameters such as strength, thickness and center height are calculated.The algorithm is verified using two squall line radar data with a typical "positive-negative velocity zone pairs" radial convergence characteristics, and the results show that the radial convergence feature identified in the relative storm radial velocity field is more complete than the original radial velocity field. The flow field of the meso-small-scale weather system is mainly composed of rotation and translation combined with ascending motion. When the translational motion speed is greater than the rotational speed, the shear (rotation, convergence, or divergence) of the system in the basic radial velocity field may be affected, while using the relative storm radial velocity can overcome this to identify the mid-level convergence better. A batch experiment of 10 thunderstorms and strong convective weather indicates the recognition accuracy of this algorithm is 82.4%, including a typical MARC features.Statistical analysis of the correlation between characteristic parameters and strength of squall line winds shows that the average radial convergence strength, maximum radial convergence strength, thickness have good positive linear correlations with wind speed. The correlation coefficient between convergence intensity and wind speed is the largest, reaching 0.79. According to the radial convergence characteristic parameter value, the intensity of the ground gale can be roughly judged, which provides a certain reference for the monitoring and early warning of convective gale and disaster assessment. The radial convergence feature identified by the algorithm can alert squall line gale about 30 minutes in advance. Therefore, the application of this algorithm will effectively improve the advancement of the warning signal release time. -
图 1 2016年8月6日23:34乐山雷达3.4°仰角反射率因子(a)、原始径向速度(b)以及处理后的相对风暴径向速度图(c)及对应沿黑色直线的反射率因子剖面图(d)、径向速度剖面图(e)和相对风暴径向速度剖面图(f)
(剖面由雷达站沿径向指向远离雷达侧)
Fig. 1 The reflectivity factor(a), raw radial velocity(b), processed relative storm radial velocity graph(c), corresponding reflectivity factor profile(d), radial velocity profile(e), and relative storm radial velocity profile(f) along the black lines based on Leshan Radar at 3.4° elevation angle at 2334 BT 6 Aug 2016
(the profile is directed away from the radar side by the radar station in the radial direction)
图 2 模拟的2016年8月7日01:00四川盆地飑线最大反射率因子(填色)以及中层环境风场(风羽，单位:m·s-1) (a), 沿图 2a红色直线的垂直剖面图(其中阴影为反射率因子，箭头为风矢量(风速矢量的垂直分量表示垂直速度的2.0倍)，等值线为相对湿度(单位: %)) (b)
Fig. 2 Simulated maximum reflectance factor (the shaded) and mid-level environmental wind field (the barb, unit:m·s-1) of the squall line at 0100 BT 7 Aug 2016(a), vertical section along the red line in Fig. 2a (where the shaded is the reflectivity factor, the arrow is the wind vector(the vertical component of the wind speed vector represents 2.0 times of vertical velocity), the contour is relative humidity(unit:%)) (b)
图 3 径向辐合对示意图
(方格内数值为等距离抽取图 1c中剖面线所在径向上有效范围内的径向速度整数值，单位：m·s-1，开始和结束的径向距离分别为51 km和61 km)
Fig. 3 Schematic diagram of radial convergence pair
(the values are the integral values of radial velocity(unit:m·s-1) in the radial direction of the section line in Fig. 1c, and the radial distances at the beginning and the end are 51 km and 61 km, respectively)
图 4 正-负速度辐合对识别算法流程图
Fig. 4 Identification algorithm of positive-negative velocity convergence pair flowchart
图 5 2013年8月18日00:49宜宾雷达2.4°仰角反射率因子(a)、原始径向速度(b)以及2018年8月7日20:15南充雷达4.3°仰角反射率因子(c)、原始径向速度(d)
(实线和虚线分别表示原始和相对风暴径向速度场上识别出的径向辐合块，标注A，B，C，E，F，G分别代表算法识别出的径向辐合块)
Fig. 5 The reflectivity factor(a) and raw radial velocity(b) based on Yibin radar station at 2.4° elevation angle at 0049 BT 18 Aug 2013 and the reflectivity factor(c) and original radial velocity(d) based on Nanchong radar station at 4.3° elevation angle at 2015 BT 7 Aug 7 2018
(the solid line and the dashed line respectively represent the radial convergent blocks identified in the RRV and RSRV, and the labels A, B, C, E, F and G represent the radial convergent blocks identified by the algorithm)
图 6 地面极大风速与相对风暴径向辐合特征参数散点分布及拟合曲线
Fig. 6 Scattered point distribution and fitting curve of the ground maximum wind speed and relative storm radial convergence characteristic parameters
表 1 检验识别算法的飑线个例及识别结果
Table 1 Examples of squall line to test the recognition algorithm and recognition result
时间 是否典型MARC特征 测试样本量 算法成功识别样本量 雷达站名 2009-07-26T01:00—02:40 是 5 4 广元 2010-06-20T19:30—21:30 是 4 2 南充 2012-08-18T19:00—20:50 是 8 7 宜宾 2013-08-18T00:00—02:00 是 7 7 宜宾 2015-07-27T20:00—21:00 是 4 2 宜宾 2016-06-04T13:00—15:00 非 6 5 广元 2016-08-06T22:00—23:50 非 5 5 乐山 2016-08-14T22:40—23:50 非 8 6 宜宾 2017-07-16T00:00—01:30 是 4 4 成都 2018-08-07T19:00—20:40 非 6 5 南充 
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