Estimates of Wind Speed Profiles from Surface Observations: Machine Learning Versus Monin–Obukhov Approach

Accurate estimation of near-surface wind profiles with surface observations is important for evaluating wind resources. Based on the multilayer perceptron algorithm, this study proposes a machine learning (ML) model by establishing the relationship between instantaneous near-surface atmospheric stability (e.g., exponent $$\alpha $$ in the power law method) and the mean, change and standard deviation values of the wind speed at 10 m, temperature and relative humidity at 2 m in the past hours. The evaluation results for six wind tower sites in semiarid and arid regions of northwestern China indicate that, compared with the Monin–Obukhov method (e.g., Holtslag in Boundary-Layer Meteorol 29:225–250, 1984), the mean relative errors of near-surface wind speed in the ML model could be reduced by 8.4%, 10.6% and 8.3% at 30 m, 50 m and 70 m, respectively. Further investigations suggest that compared to the MO model, the mean relative errors of near-surface wind profiles in the ML model could be reduced by 2–15% at different wind tower sites and under different near-surface stability conditions. In general, the estimation performance of near-surface wind profiles in the ML model is better under unstable conditions than under stable conditions. Results illuminate that the proposed method using near-surface variables in the past hours as inputs could be an effective way to improve the estimation of near-surface wind profiles.