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

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