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AM-ConvGRU: a spatio-temporal model for typhoon path prediction

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A Correction to this article was published on 11 December 2022

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Abstract

Typhoons are one of the most destructive types of disasters. Several statistical models have been designed to predict their paths to reduce damage, casualties, and economic loss. To further increase prediction accuracy, two key challenges are (1) to extract better nonlinear 3D features of typhoons, which is hard due to their complex high-dimensional properties, and (2) to combine suitable 2D and 3D features in a proper way to improve predictions. To address these challenges, this paper presents a novel spatio-temporal deep learning model named Attention-based Multi ConvGRU (AM-ConvGRU). To automatically select high response isobaric planes of typhoons when considering their whole 3D structures, AM-ConvGRU leverages the Residual Channel Attention Block (RCAB). Furthermore, it integrates a novel model named Multi-ConvGRU to extract large-scale nonlinear spatial features of typhoons. Moreover, the approach relies on a Wide & Deep framework to fuse the traditional Generalized Linear Model (GLM) with the proposed AM-ConvGRU model. To evaluate the designed approach, extensive experiments have been conducted using real-world typhoons data from the Western North Pacific (WNP) basin obtained from both the China Meteorological Administration (CMA) dataset and the EAR-Interim dataset maintained by the European Centre for Medium-Range Weather Forecasts (ECMWF). Results show that the proposed method outperforms state-of-the-art deep learning typhoon prediction methods. The source code is available on GitHub with the following link:https://github.com/xuguangning1218/Typhoon_Path.

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Notes

  1. https://www.aoml.noaa.gov/hrd/Landsea/climvari/table.html (last accessed on 2020/11).

  2. http://tcdata.typhoon.org.cn/en/zjljsjj_zlhq.html (last accessed on 2020/11/17).

  3. https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim (last accessed on 2020/11/17).

  4. https://www.tensorflow.org/api_docs/python/tf/keras/layers/SimpleRNN.

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Acknowledgments

This work was supported in part by the Shenzhen Science and Technology Program under Grant JCYJ20210324120208022, JCYJ20180507183823045, and JCYJ20200 109113014456.

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Authors and Affiliations

  1. Harbin Institute of Technology, Shenzhen, 518055, China

    Guangning Xu, Philippe Fournier-Viger, Xutao Li & Yunming Ye

  2. National Satellite Meteorological Center, China Meteorological Administration, Beijing, 100081, China

    Di Xian & Xiuqing Hu

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  1. Guangning Xu
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  2. Di Xian
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Correspondence to Di Xian or Xutao Li.

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Xu, G., Xian, D., Fournier-Viger, P. et al. AM-ConvGRU: a spatio-temporal model for typhoon path prediction. Neural Comput & Applic 34, 5905–5921 (2022). https://doi.org/10.1007/s00521-021-06724-x

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