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

Abstract

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