基于相对风暴径向速度场的辐合区自动识别算法

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。

Automatic Recognition Algorithm of Convergence Region Based on Relative Storm Radial Velocity Field

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.