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 21.61 % for U and 26.04 % for V, Root Mean Square Error (RMSE) reduced by 18.30 % for U and 22.63 % for V, and Nash–Sutcliffe Efficiency (NSE) improved by 33.27 % and 40.13%, respectively. On a daily mean basis, both MAE and RMSE are reduced to below 0.5 m s−1, 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, 42.18 % accuracy gains are observed in the near-surface layer when compared with radiosonde profiles. Moreover, convective case analyses indicate that YRD1km captures vertically coherent wind structures across multiple tropospheric levels that are closely linked to the initiation and maintenance 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.