Three-dimensional temporal-spatial attention for tropical cyclone forecast

Qinjie Lin; Yingjie Jin; Yifan Lin; Xiaoyong Li

doi:10.1117/12.3009402

11 October 2023 Three-dimensional temporal-spatial attention for tropical cyclone forecast

Qinjie Lin, Yingjie Jin, Yifan Lin, Xiaoyong Li

Proceedings Volume 12918, Fourth International Conference on Computer Science and Communication Technology (ICCSCT 2023); 1291820 (2023) https://doi.org/10.1117/12.3009402
Event: International Conference on Computer Science and Communication Technology (ICCSCT 2023), 2023, Wuhan, China

Abstract

The tropical cyclone (TC) is a strong and highly destructive tropical low-pressure vortex that often brings disasters such as strong wind, heavy rain, and storm surge. The formation and intensity forecast of TC is very important for TC disaster warnings. In this paper, we propose a three-dimensional temporal-spatial (3D-TS) attention TC forecast model based on deep learning, which considers the temporal-spatial relationship between TC variables on the basis of machine learning methods. The model introduces 2D convolutional neural networks (2DCNNs) and 3D convolutional neural networks (3DCNNs) to learn oceanographic variables and atmospheric variables of TC, while utilizing Long Short-Term Memory (LSTM) for capturing the temporal correlation in TC’s evolution process, and introduces 3D-TS to grasp the temporalspatial characteristic of TC and enhance the model’s precision. Through experiments, it was found that the model’s performance in TC formation and intensity forecast is better than many existing numerical forecasting methods, statistical forecasting methods, and machine learning methods. We validated the model on the Western Pacific TC dataset, in the TC formation forecast experiment, the model can achieve a maximum accuracy rate of 86.1% and an AUC value of 92.5%. In the TC intensity forecast experiment, the minimum error is 7.1 kt, showing that we achieved state-of-the-art results.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qinjie Lin, Yingjie Jin, Yifan Lin, and Xiaoyong Li "Three-dimensional temporal-spatial attention for tropical cyclone forecast", Proc. SPIE 12918, Fourth International Conference on Computer Science and Communication Technology (ICCSCT 2023), 1291820 (11 October 2023); https://doi.org/10.1117/12.3009402

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KEYWORDS

3D modeling

Machine learning

Atmospheric modeling

Deep learning

Statistical modeling

Convolutional neural networks

Data modeling

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