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

  • Type:

    Article

  • Date:

    13 October 2023

  • Publication Status:

    Published

  • DOI:

    10.1109/msmc.2022.3216943

  • ISSN:

    2380-1298

  • Funders:

    National Natural Science Foundation of China; Royal Society of Edinburgh; New Funder

Citation

Liu, Q., Yang, Z., Ji, R., Zhang, Y., Bilal, M., Liu, X., …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

Authors

Keywords

Precipitation nowcasting, Deep Learning, Beam Blockage Correction, Radar Echo Extrapolation, short-term precipitation nowcasting

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