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.