Search

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. Key Points: A framework of region‐of‐interest detection and classification is proposed for networked X‐band weather radar systemReal‐time machine learning‐based algorithms are introduced to achieve accurate identification with simple inputThe framework is subjectively and objectively evaluated in a networked X‐band radar system located in Chengdu, China [ABSTRACT FROM AUTHOR]

This paper proposes an improved algorithm which combines neural networks with diverse 3‐D structural features to partition radar reflectivity into convective and stratiform precipitation types. Radar data used in this work were obtained from three networked X‐band Doppler radars located in Chengdu, China. The proposed algorithm consists of the two sections: six high‐resolution features, which could be extracted from radar volume scanning and expressed the characteristics of the target in many ways, are selected as input to the neural network; systematic self‐diagnosis is implemented; and the optimized model is determined according to analysis of bias and variance of classifier. Three state‐of‐art classification algorithms were implemented as references for algorithm evaluation. Both subjective comparison and statistical results convince that performance of the proposed algorithm is better than performance of traditional classification algorithms in various weather systems. The statistical results show that the proposed algorithm F‐score values are 3%, 24%, and 30% higher than the fuzzy logic, SHY95, and BL algorithms and the recognition speed of the proposed algorithm is 54 times, two times, and four times that of fuzzy logic, SHY95, and BL, respectively. Considering network training is an offline procedure, classification by proposed neural network algorithm has great potential in real‐time weather analysis for the precipitation classification. Plain Language Summary: In recent years, networked radar become the popular way to overcome some of the inherent shortcomings of single radar. There are different scanning strategies for convective and stratiform clouds, and it is difficult to accurately distinguish between convective clouds and stratiform clouds, and when the accuracy is high, there will be a problem of low‐recognition rate. Our algorithm utilizes a variety of features derived from radar products that have a good distinction between the two categories. At the same time, combined with neural network, its powerful nonlinear fitting and high speed make the recognition speed fast and the recognition result is high. The recognition result of our algorithm is higher than the recognition result of the popular fuzzy logic algorithm, and its recognition speed is even several times that of the fuzzy logic algorithm. Key Points: Six distinguishable features are extracted from radar echoes in order to well describe the horizontal and vertical structure of the cloudThe neural network is carefully diagnosed according to analysis of bias and variance of classifier to avoid overfitting and underfittingThe algorithm is evaluated by comparing the classification performance with those of three state‐of‐art algorithms subjectively and statistically [ABSTRACT FROM AUTHOR]