Zhongnan University of Economics and Law

Tropical cyclone intensity forecasting has relied on best-track data or geostationary satellite imagery, yet similar cloud patterns can accompany intensification, steady state or weakening, and generic fusion strategies cannot tell them apart. TCFNet defines intensity trend from pressure gradients, uses it to select positive pairs for contrastive learning so the visual encoder learns cloud representations aligned with intensity change, then propagates states continuously to arbitrary lead times with neural ordinary differential equations. For 24-hour forecasts in the Western North Pacific and South Pacific, pressure RMSE is 9.52 hPa and 7.00 hPa respectively, and the critical success index for rapid intensification is 45.26%.

Tropical cyclone intensity forecasting has relied on best-track data or geostationary satellite imagery, yet similar cloud patterns can accompany intensification, steady state or weakening, and generic fusion strategies cannot tell them apart. TCFNet defines intensity trend from pressure gradients, uses it to select positive pairs for contrastive learning so the visual encoder learns cloud representations aligned with intensity change, then propagates states continuously to arbitrary lead times with neural ordinary differential equations. For 24-hour forecasts in the Western North Pacific and South Pacific, pressure RMSE is 9.52 hPa and 7.00 hPa respectively, and the critical success index for rapid intensification is 45.26%.