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

Aerodynamic roughness length (z0 ${z}_{0}$) fundamentally affects land surface momentum loss and wind resource simulation, but ground truth data of z0 ${z}_{0}$ are sparse in space, causing z0 ${z}_{0}$ datasets used in atmospheric models are empirically estimated from land cover types through a look-up table. In this study, we derived z0 ${z}_{0}$ values from 101 anemometer towers in China. Taking them as ground truth, we show that existing gridded z0 ${z}_{0}$ datasets determined from either a look-up table or a machine-learning method contain considerable uncertainty and fail to capture the variability of z0 ${z}_{0}$ within each land cover type, although the latter performs better. Even for the widely used ERA5, its z0 ${z}_{0}$ is overestimated in wind-rich regions of China, causing an underestimation of near-surface wind speed. This highlights the necessity to improve z0 ${z}_{0}$ data in atmospheric models. Current rapidly expanding anemometer towers may substantially enrich z0 ${z}_{0}$ truth data and thus provide potential to improve wind resource modeling.