A 1 km Hourly High-Resolution 3D Wind Field Dataset over the Yangtze River Delta Incorporating Dynamical Downscaling, Observational Assimilation, and Land Use Updates

Abstract. High-resolution three-dimensional (3D) wind field data are critical for a wide range of applications, including wind energy assessment, low-altitude aviation, air quality modeling, and extreme weather forecasting. Although ERA5 reanalysis remains widely used, its relatively coarse spatial resolution (~31 km) limits its ability to capture local-scale atmospheric processes. To address this, this study develops an hourly 3D dynamic wind field dataset with 1 km horizontal resolution covering the Yangtze River Delta (YRD) region during the summer months (June–August) from 2021 to 2023, namely YRD1km, generated through advanced dynamical downscaling of ERA5 using a customized Weather Research and Forecasting (WRF) model configuration. The methodology integrates multi-source observational nudging with high-resolution land use parameterization to enhance near-surface wind accuracy and terrain-induced flow representation, particularly in urban clusters and mountainous areas. Validation against ground-based observations confirms the superior performance of YRD1km over ERA5 for hourly 10-m wind components, with Mean Absolute Error (MAE) reduced by approximately 22 % for U and 26 % for V, Root Mean Square Error (RMSE) reduced by 18 % for U and 23 % for V, and Nash–Sutcliffe Efficiency (NSE) improved by 33 % and 40 %, respectively. On a daily mean basis, both MAE and RMSE are reduced to below 0.4 m/s, and NSE reaches approximately 0.88. Spatially, YRD1km captures finer spatial wind speed gradients and localized terrain-induced circulations that are not captured by ERA5. Temporally, consistent accuracy improvements with approximately 20 % lower hourly error variability are seen when compared to ERA5. Vertically, 42.2 % accuracy gains are observed in the near-surface layer when compared with radiosonde profiles. Moreover, in a representative convective storm case, YRD1km captures multi-level wind structures that are closely linked to the initiation and continuous development of deep convection, highlighting its diagnostic advantage in high-impact weather events. Overall, the YRD1km 3D wind field dataset and its integrated methodological framework provide a robust foundation for regional meteorological applications, including high-resolution AI-based forecasting, renewable energy planning, and weather risk management in rapidly developing regions such as the YRD. The YRD1km 3D wind field dataset is available at https://doi.org/10.57760/sciencedb.23752 (Zhang et al., 2025).