A global kilometre-scale tropical cyclone inner-core vector wind field dataset from CYGNSS observations

Abstract. Tropical cyclone (TC) inner-core vector wind fields are essential for intensity forecasting, storm surge prediction, and structural climatology. The Cyclone Global Navigation Satellite System (CYGNSS) constellation provides dense temporal sampling and L-band precipitation-penetrating capability over the tropical belt, but its sparse, scalar wind speed retrievals have not been fully assimilated to produce kilometre-scale TC inner-core vector wind fields with global multi-basin coverage. This paper presents the QiFeng-CYGNSS dataset, which combines CYGNSS observations with a physics-guided score-based diffusion assimilation framework to reconstruct spatially complete 10 m vector wind fields from sparse scalar wind speed observations. The dataset covers 249 TCs across six active global basins during January 2020–September 2022, providing 1.5 km resolution vector wind fields at every IBTrACS reporting time with available CYGNSS coverage (4955 snapshots in total), accompanied by observation metadata and pixel-level ensemble uncertainty estimates for 138 major-hurricane snapshots. Independent validation against spaceborne C-band synthetic aperture radar, airborne Tail Doppler Radar, and GPS dropsondes indicates that the reconstructions represent TC inner-core structures at kilometre scales, reducing the absolute V_max bias relative to ERA5 and CCMP by ~79% and ~75%, respectively, on the full sample. The dataset is freely available from Zenodo at https://doi.org/10.5281/zenodo.20046109 (Han et al., 2026b).