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

Abstract

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.

Citation

Liu, Q., Yang, Z., Ji, R., Zhang, Y., Bilal, M., Liu, X., Vimal, S., & Xu, X. (2023). Deep Vision in Analysis and Recognition of Radar Data: Achievements, Advancements, and Challenges. IEEE Systems, Man, and Cybernetics Magazine, 9(4), 4-12. https://doi.org/10.1109/msmc.2022.3216943

• 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
• DOI: https://doi.org/10.1109/msmc.2022.3216943
• Keywords: Precipitation nowcasting, Deep Learning, Beam Blockage Correction, Radar Echo Extrapolation, short-term precipitation nowcasting
• Public URL: http://researchrepository.napier.ac.uk/Output/2957882