An integrated model for tropical cyclone track forecasting based on deep learning techniques

Abstract An accurate track forecast for tropical cyclones (TC) is critical for disaster prevention and mitigation. Although various models, including statistical, numerical and AI-based have demonstrated good skill in TC track forecasting, they haven’t ever been (and won’t ever be) perfect, resulting in large uncertainties among them. To reduce the uncertainties, the Spatio-Temporal Integrated Forecast Network (ST-IFNet), an integrated model, is developed using deep learning techniques and the track forecasts from seven operational agencies. ST-IFNet utilizes Agency-Time Dual-Branch Attentions to dynamically adjust the weights of each agency at each time step, and concurrently mines the physical trends of the changes in TC movement by the incorporation of environmental data. Experimental results show that ST-IFNet outperforms the operational forecast from any of the agencies as well as a simple average of them, especially in the medium- (36h–48h) and long-term (60h–120h) TC track forecast, achieving a track position error (TPE) as low as 62.89km, 78.93km, 123.96km, 139.91km and 177.69km for 24h, 48h, 72h, 96h and 120h, respectively. Further analysis reveals that large-scale potential height fields (≥ 1000 km) and mesoscale flow fields (200–1000 km) obtained from scale separation provide the most valuable guidance for ST-IFNet. This work builds a bridge between numerical forecasts and artificial intelligence techniques and provides a useful and efficient tool for operational TC track forecast in the future.