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; Cong Bai (Nature Communications)
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 TCN D - a open multimodal TC dataset spanning six major ocean basins with 70 years of multi-source data, and TCN M - an AI-meteorology integrated prediction model including multiple modules such as Generator Chooser Network and Environment-Time Net. Comprehensive evaluations demonstrate that TCN M 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.