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Skillful seasonal prediction of typhoon track density using deep learning

Feng, Z., Lv, S., Sun, Y., Feng, X. ORCID:, Zhai, P., Lin, Y., Shen, Y. and Zhong, W. (2023) Skillful seasonal prediction of typhoon track density using deep learning. Remote Sensing, 15 (7). 1797. ISSN 2072-4292

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To link to this item DOI: 10.3390/rs15071797


Tropical cyclones (TCs) seriously threaten the safety of human life and property especially when approaching a coast or making landfall. Robust, long-lead predictions are valuable for managing policy responses. However, despite decades of efforts, seasonal prediction of TCs remains a challenge. Here, we introduce a deep-learning prediction model to make skillful seasonal prediction of TC track density in the Western North Pacific (WNP) during the typhoon season, with a lead time of up to four months. To overcome the limited availability of observational data, we use TC tracks from CMIP5 and CMIP6 climate models as the training data, followed by a transfer-learning method to train a fully convolutional neural network named SeaUnet. Through the deep-learning process (i.e., heat map analysis), SeaUnet identifies physically based precursors. We show that SeaUnet has a good performance for typhoon distribution, outperforming state-of-the-art dynamic systems. The success of SeaUnet indicates its potential for operational use.

Item Type:Article
Divisions:Science > School of Mathematical, Physical and Computational Sciences > NCAS
Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:111574
Publisher:MDPI AG


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