An Automatic Framework of Region‐of‐Interest Detection and Classification for Networked X‐Band Weather Radar System

摘要

Nowadays, S‐band weather radars are designed to observe weather conditions within large area. However, S‐band radars suffer from large blind zone at low elevation and fixed scan strategy so that they are not efficient for surveillance of convective weathers such as tornado. Networked X‐band weather radars are thereby proposed to overcome this issue. One key problem of networked radar is to automatically determine the type and the precise position of convective cells that are worthwhile to detect. In this paper, a tailor‐made framework is proposed to automatically find the convective cells and retrieve information of them from reflectivity product of networked X‐band weather radars. The framework consists of three substeps: convection pixel retrieval by Back Propagation Neural Network (BPNN), convection cell construction by Density‐Based Spatial Clustering of Applications with Noise (DBSCAN), and convection cell classification by Convolutional Neural Network (CNN). Evaluation results show that the proposed algorithm is capable to identify isolated single‐cell and multicell convective storms from the reflectivity image accurately. Therefore, the proposed framework is capable to provide information for networked X‐band weather radars so that they can track harmful convective weathers. The proposed framework has been embedded in the meteorological command and control (MCC) center of networked X‐band radar system in Chengdu. Plain Language Summary Weather radar is significant in modern meteorological observation system. Recently, networked X‐band weather radars are attractive because it provides an efficient way to detect and track harmful convective weather such as tornadoes. One of the most difficult tasks of networked X‐band weather radars is to automatically determine clouds that are possible to endanger our regular live. It is not an easy mission because clouds are notoriously changeable, and the time resolution of our weather radars is relative low. In this paper, we propose an algorithm to challenge this problem by utilizing machine learning methods. According to the evaluation results, our algorithm is accurate and computational efficient, and it has been embedded into the networked X‐band radar system in Chengdu, China.