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Physical Attention-Gated Spatial-Temporal Predictive Network for Weather Forecasting

Author

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  • Xueliang Zhao

    (Chengdu Institute of Computer Applications, Chinese Academy of Sciences, Chengdu 610041, China
    Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China
    School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China)

  • Qilong Sun

    (Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China
    Binzhou Institute of Technology, Binzhou 256606, China)

  • Xiaoguang Lin

    (Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China)

Abstract

Spatial-temporal sequence prediction is one of the hottest topics in the field of deep learning due to its wide range of potential applications in video-like data processing, specifically weather forecasting. Since most spatial-temporal observations evolve under physical laws, we adopt an attentional gating scheme to leverage the dynamic patterns captured by tailored convolution structures and propose a novel neural network, PastNet, to achieve accurate predictions. By highlighting useful parts of the whole feature map, the gating units help increase the efficiency of the architecture. Extensive experiments conducted on synthetic and real-world datasets reveal that PastNet bears the ability to accomplish this task with better performance than baseline methods.

Suggested Citation

  • Xueliang Zhao & Qilong Sun & Xiaoguang Lin, 2023. "Physical Attention-Gated Spatial-Temporal Predictive Network for Weather Forecasting," Mathematics, MDPI, vol. 11(6), pages 1-10, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:6:p:1330-:d:1092460
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    References listed on IDEAS

    as
    1. Peter Bauer & Alan Thorpe & Gilbert Brunet, 2015. "The quiet revolution of numerical weather prediction," Nature, Nature, vol. 525(7567), pages 47-55, September.
    2. Suman Ravuri & Karel Lenc & Matthew Willson & Dmitry Kangin & Remi Lam & Piotr Mirowski & Megan Fitzsimons & Maria Athanassiadou & Sheleem Kashem & Sam Madge & Rachel Prudden & Amol Mandhane & Aidan C, 2021. "Skilful precipitation nowcasting using deep generative models of radar," Nature, Nature, vol. 597(7878), pages 672-677, September.
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