Open AccessAn Automatic Rainfall‐Type Classification Algorithm Combining Diverse 3‐D Features of Radar Echoes
Author(s) -
Lei Bo,
Xu ZiXin,
Yang Ling,
Li Xuehua,
Zhen Xiaoqiong
Publication year - 2019
Publication title -
earth and space science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.843
H-Index - 23
ISSN - 2333-5084
DOI - 10.1029/2019ea000796
Subject(s) - algorithm , radar , computer science , artificial neural network , fuzzy logic , artificial intelligence , classifier (uml) , doppler radar , data mining , pattern recognition (psychology) , telecommunications
Abstract 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.