Editorial Type:
Article
Article Type:
Research Article

Predicting the Intensity of Tropical Cyclones over the Western North Pacific Using a Dual-Branch Spatiotemporal Attention Convolutional Network

Wei Tian aSchool of Software, Nanjing University of Information Science and Technology, Nanjing, China

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Yuanyuan Chen aSchool of Software, Nanjing University of Information Science and Technology, Nanjing, China

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Ping Song bSchool of Computer, Nanjing University of Information Science and Technology, Nanjing, China

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Haifeng Xu aSchool of Software, Nanjing University of Information Science and Technology, Nanjing, China

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Liguang Wu cFuDan University, ShangHai, China

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Kenny Thiam Choy Lim Kam Sian dWuXi University, WuXi, China

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Yonghong Zhang eSchool of Automation, Nanjing University of Information Science and Technology, Nanjing, China

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Chunyi Xiang fNational Meteorological Center, Beijing, China

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Online Publication:
21 May 2024
Print Publication:
01 May 2024
DOI:
https://doi.org/10.1175/WAF-D-23-0191.1
Page(s):
807–819
Restricted access

Abstract

This paper proposes a spatiotemporal attention convolutional network (STAC-Pred) that leverages deep learning techniques to model the spatiotemporal features of tropical cyclones (TCs) and enable real-time prediction of their intensity. The proposed model employs dual branches to concurrently extract and integrate features from intensity heatmaps and satellite cloud imagery. Additionally, a residual attention (RA) module is integrated into the three-channel cloud imagery convolution process to automatically respond to high wind speed regions. TC’s longitude, latitude, and radius of winds are injected into the multi-timepoint prediction model to assist in the prediction task. Furthermore, a rolling mechanism (RM) is employed to smooth the fluctuation of losses, achieving accurate prediction of TC intensity. We use several TC records to evaluate and validate the universality and effectiveness of the model. The results indicate that STAC-Pred achieves satisfactory performance. Specifically, the STAC-Pred model improves prediction performance by 47.69% and 28.26% compared to the baseline (official institutions) at 3- and 6-h intervals, respectively.

Significance Statement

Tropical cyclones are one of the most deadly and damaging natural disasters in coastal areas worldwide. Early prediction can significantly reduce casualties and property losses. This study innovatively conducts dimensionality augmentation on one-dimensional intensity numerical sequences and proposes a new network model for rolling forecast of their future intensity. The proposed prototype model (not yet incorporating any atmospheric conditions) shows promising results for 3- and 6-h advance forecasts, providing valuable guidance for forecasters regarding real-time operational predictions of short-term tropical cyclone intensity.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Wei Tian, tw@nuist.edu.cn

Abstract

This paper proposes a spatiotemporal attention convolutional network (STAC-Pred) that leverages deep learning techniques to model the spatiotemporal features of tropical cyclones (TCs) and enable real-time prediction of their intensity. The proposed model employs dual branches to concurrently extract and integrate features from intensity heatmaps and satellite cloud imagery. Additionally, a residual attention (RA) module is integrated into the three-channel cloud imagery convolution process to automatically respond to high wind speed regions. TC’s longitude, latitude, and radius of winds are injected into the multi-timepoint prediction model to assist in the prediction task. Furthermore, a rolling mechanism (RM) is employed to smooth the fluctuation of losses, achieving accurate prediction of TC intensity. We use several TC records to evaluate and validate the universality and effectiveness of the model. The results indicate that STAC-Pred achieves satisfactory performance. Specifically, the STAC-Pred model improves prediction performance by 47.69% and 28.26% compared to the baseline (official institutions) at 3- and 6-h intervals, respectively.

Significance Statement

Tropical cyclones are one of the most deadly and damaging natural disasters in coastal areas worldwide. Early prediction can significantly reduce casualties and property losses. This study innovatively conducts dimensionality augmentation on one-dimensional intensity numerical sequences and proposes a new network model for rolling forecast of their future intensity. The proposed prototype model (not yet incorporating any atmospheric conditions) shows promising results for 3- and 6-h advance forecasts, providing valuable guidance for forecasters regarding real-time operational predictions of short-term tropical cyclone intensity.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Wei Tian, tw@nuist.edu.cn
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