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Deep Vision in Analysis and Recognition of Radar Data: Achievements, Advancements, and Challenges

Liu, Qi; Yang, Zhiyun; Ji, Ru; Zhang, Yonghong; Bilal, Muhammad; Liu, Xiaodong; Vimal, S; Xu, Xiaolong


Qi Liu

Zhiyun Yang

Ru Ji

Yonghong Zhang

Muhammad Bilal

S Vimal

Xiaolong Xu


Radars are widely used to obtain echo information for effective prediction, such as precipitation nowcasting. In this article, recent relevant scientific investigation and practical efforts using deep learning (DL) models for weather radar data analysis and pattern recognition have been reviewed. In addition, this work presents and discusses recent achievements, as well as recent developments and existing problems, in an attempt to establish plausible potentials and trends in this highly concerned field, particularly, in the fields of beam blockage correction, radar echo extrapolation, and precipitation nowcast. Compared to traditional approaches, present DL methods depict better performance and convenience but suffer from stability and generalization.

Journal Article Type Article
Acceptance Date Oct 12, 2022
Online Publication Date Oct 13, 2023
Publication Date 2023-10
Deposit Date Nov 15, 2022
Publicly Available Date Oct 13, 2023
Journal IEEE Systems, Man, and Cybernetics Magazine
Print ISSN 2380-1298
Electronic ISSN 2333-942X
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 9
Issue 4
Pages 4-12
Keywords Precipitation nowcasting, Deep Learning, Beam Blockage Correction, Radar Echo Extrapolation, short-term precipitation nowcasting
Public URL


Deep Vision In Analysis And Recognition Of Radar Data: Achievements, Advancements And Challenges (accepted version) (589 Kb)

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