A high-resolution dataset of pre-rainfall low-level vertical wind shear derived from radar wind profilers across China

Abstract. Favorable low-level vertical wind shear (VWS) is an important dynamic feature conducive to rainfall development. However, continuous observations capable of resolving the fine-scale vertical structure and rapid pre-rainfall evolution of VWS remain limited. To address this observational gap, we developed a high-resolution low-level VWS dataset from wind measurements collected by a network of 28 radar wind profiler (RWP) stations across China during 2023–2025, with a focus on the 2 h preceding rainfall onset. The dataset provides bulk VWS for both the fixed 0.6–3.0 km layer and arbitrary combinations of vertical levels, local layer VWS, layer-mean VWS, maximum VWS, and the altitude of maximum VWS, with a temporal resolution of 6 min and a vertical resolution of 120 m over 0.6–4.08 km AGL. Comparison with collocated radiosonde measurements supports the reliability of the RWP-derived VWS, while comparison with ERA5 shows that the reanalysis underestimates localized maximum VWS and does not reproduce its rapid minute-scale evolution. Non-HR events are generally associated with stronger but relatively steady background VWS, whereas HR events show a pronounced nonlinear increase in local low-level VWS during approximately the final 30 min before rainfall onset. The latter is accompanied by an upward shift in the altitude of maximum VWS, indicating a redistribution of the strongest shear within the lower troposphere. These contrasting evolution patterns demonstrate the ability of the dataset to resolve transient and vertically localized changes in pre-rainfall dynamic environments that are not adequately represented by coarser-resolution reanalysis data. The dataset is publicly available at https://doi.org/10.5281/zenodo.23432237 (Li and Guo, 2026), which can serve as a valuable benchmark for diagnosing pre-rainfall dynamic environments to advance regional HR nowcasting. Accordingly, to realize such forecasting improvements, we must leverage fine-scale dynamic structures derived from RWP measurements for more complete characterizations of prestorm environments.