Deducing Aerodynamic Roughness Length From Abundant Anemometer Tower Data to Inform Wind Resource Modeling

Abstract

Aerodynamic roughness length ($z_0$) fundamentally affects land surface momentum loss and wind resource simulation, but ground truth data of $z_0$ are sparse in space, causing $z_0$ datasets used in atmospheric models are empirically estimated from land cover types through a look-up table. In this study, we derived $z_0$ values from 101 anemometer towers in China. Taking them as ground truth, we show that existing gridded $z_0$ datasets determined from either a look-up table or a machine-learning method contain considerable uncertainty and fail to capture the variability of $z_0$ within each land cover type, although the latter performs better. Even for the widely used ERA5, its $z_0$ is overestimated in wind-rich regions of China, causing an underestimation of near-surface wind speed. This highlights the necessity to improve $z_0$ data in atmospheric models. Current rapidly expanding anemometer towers may substantially enrich $z_0$ truth data and thus provide potential to improve wind resource modeling.