基于OPTICS聚类算法的雷达数据雷暴单体识别方法

雷达数据的雷暴单体识别算法是雷暴追踪算法的重要组成部分,传统的连续区域法只能通过改变回波强度阈值来调整雷暴单体识别结果,不能满足当前应用需求。本文提出了一种基于OPTICS(Ordering Points to Identify the Clustering Structure)算法的雷暴识别方法,该方法能基于高回波点的密度信息进行雷暴单体识别。利用高分辨率X波段天气雷达在两次雷暴过程中的体扫数据检验了算法的效果,并与传统方法进行了比较。结果表明:该方法能克服连续区域法在高分辨率雷达数据中可能出现的无法区分不同单体、识别结果过于零散等问题。并且能在不修改回波强度阈值的情况下灵活调整输出结果,以适应不同应用需求。

A Storm Cell Identification Method for Radar Data Based on OPTICS Algorithm

The thunderstorm identification algorithm of radar data is an important part of the thunderstorm tracking technique. The traditional continuous region method can only adjust thunderstorm identification results by changing the reflectivity threshold, which cannot meet the current application requirements. This paper proposes a storm identification method based on the OPTICS (Ordering Points to Identify the Clustering Structure) algorithm. This method can identify storm cells based on the density information of points with high reflectivity. Using volume scan data of high resolution X band weather radar in two thunderstorms, the performance of the proposed algorithm is tested and compared with the traditional method. The results show that this method can overcome the problems that may occur in traditional methods in high resolution radar data, such as being unable to distinguish adjacent cells or getting too scattered identification results. Besides, it can flexibly adjust the output results without changing the reflectivity threshold to meet the needs of different applications.