A Super‐Resolution Model for Enhancing the Intensity and Structure of Tropical Cyclone Predictions

Abstract

Accurate predictions of intensity and structure of tropical cyclones (TCs) are crucial for disaster warning, prevention and mitigation. Traditional dynamical models with coarse horizontal resolutions suffer from systematic underprediction of TC intensity, whereas those with fine horizontal resolutions require intensive computations. Deep learning (DL)‐based models are computationally efficient yet substantially underpredict the TC intensity. To enhance the prediction of DL‐based weather models for TC intensities and structures, a hybrid framework (HybSR‐TC) is proposed here. By integrating a Swin Transformer backbone with multi‐variable processing and meta‐upsampling techniques, HybSR‐TC is trained based on low‐resolution (0.25°) forecasts of Pangu‐Weather and high‐resolution (2 km) weather research and forecasting forecasts with accurate TC tracks and well simulated intensities and structures. For TC predictions, HybSR‐TC maps the 6‐hr Pangu‐Weather forecasts of surface pressure and wind fields at 0.25° resolution to 2‐km resolution fields without the requirement for massive computational costs. HybSR‐TC can better capture the rapid intensification and temporal evolution of TCs than Pangu‐Weather and bilinear interpolation. HybSR‐TC produces significantly smaller errors of TC intensity and structure than Pangu‐Weather and bilinear interpolation, until approximately 10‐day lead times. Thus, the ability of HybSR‐TC to enhance the forecasts of TC intensity and structure based on the low‐resolution forecasts from AI models has been demonstrated.