Benchmark dataset and deep learning method for global tropical cyclone forecasting

Cheng Huang, Pan Mu, Jinglin Zhang, Sixian Chan, Shiqi Zhang, Hanting Yan, Shengyong Chen and Cong Bai ()
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Cheng Huang: Zhejiang University of Technology
Pan Mu: Zhejiang University of Technology
Jinglin Zhang: Shangdong University
Sixian Chan: Zhejiang University of Technology
Shiqi Zhang: Zhejiang University of Technology
Hanting Yan: Zhejiang University of Technology
Shengyong Chen: Tianjin University of Technology
Cong Bai: Zhejiang University of Technology

Nature Communications, 2025, vol. 16, issue 1, 1-17

Abstract: Abstract Accurate tropical cyclone (TC) forecasting is critical for disaster prevention. While deep learning shows promise in weather prediction, existing approaches demonstrate limited accuracy in TC track and intensity forecasting, hindered by the lack of open multimodal datasets and insufficient integration of meteorological knowledge. Here we propose TropiCycloneNet containing TCND - a open multimodal TC dataset spanning six major ocean basins with 70 years of multi-source data, and TCNM - an AI-meteorology integrated prediction model including multiple modules such as Generator Chooser Network and Environment-Time Net. Comprehensive evaluations demonstrate that TCNM outperforms both existing deep learning methods and official meteorological forecasts across multiple metrics. This advancement stems from synergistic optimization of our meteorologically-informed architecture and the dataset’s comprehensive spatiotemporal coverage. The released resources and method can attract more researchers to the field, thereby accelerating data-driven tropical cyclone prediction research.

Date: 2025
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DOI: 10.1038/s41467-025-61087-4

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