Improvement of near-surface wind speed modeling through refined aerodynamic roughness length in high-roughness surface regions: implementation and validation in the Weather Research and Forecasting (WRF) model version 4.0

Abstract

Aerodynamic roughness length (z0) is a key parameter determining near-surface wind profiles, significantly influencing wind-related studies and applications. In high-roughness surface areas, surface roughness has been substantially altered by land use changes such as urbanization. However, many numerical models still assign long-standing and fixed z0 based on traditional land cover types, neither accounting for shifts in land cover nor updating class-specific z0, leaving z0 values in high-roughness surface regions outdated and unreliable. To address this issue, this study proposed a cost-effective method to estimate z0 values at weather stations by adjusting z0 values to minimize the wind speed differences between ERA5 reanalysis data and weather station observation data. Using this approach, z0 values were derived for 1837 stations in the high-roughness surface areas across China. Based on these estimates, a high-resolution monthly gridded z0 dataset was then developed for high-roughness surface areas in China using Random Forest Regression algorithm. Simulations with Weather Research and Forecasting (WRF) model show that implementation of the new z0 dataset significantly improves the accuracy of 10 m wind speed over high-roughness surface areas, reducing mean wind speed errors by 79.8 % and 78.0 % compared to the default z0 in WRF and a latest gridded z0 dataset from Peng et al. (2022), respectively. Independent validations of 100 m wind speed against anemometer tower data further confirm the dataset’s reliability. Therefore, this approach is valuable for wind-dependent studies and applications, such as urban planning, air quality management, and wind energy utilization, by enabling more accurate simulations of wind speed in high-roughness surface areas.