Downscaling of Tropical Cyclone Surface Wind Fields With a Hybrid Attention Transformer

Abstract This study employs a Hybrid Attention Transformer‐based super‐resolution model to reconstruct tropical cyclone (TC) surface wind fields. The model downscales coarse‐resolution ERA5 reanalysis data to high‐resolution HWind analyses (1998–2013), increasing the horizontal resolution by a factor of five. First, this study assesses the impact of data set partitioning strategies and different meteorological input variables on model performance. The reconstructed TC surface wind fields and the radial profiles of azimuthally averaged surface winds agree well with the HWind analysis. Key TC metrics, including the maximum sustained wind speed (MSW) and the radius of 17 m s −1 winds (R17), show high correlations with HWind (0.81 and 0.92, respectively). When independently validated against IBTrACS records over the North Atlantic (1990–2020), the model maintains robust correlations, confirming its out‐of‐sample reliability across different periods and cases. The reconstructed past TC intensity and size metrics achieved by the developed model provide new opportunities for research, demonstrating the model's future potential in TC climatology and hazard assessment studies.