Formulation and Implementation of a Dynamic Roughness Length for Improved Surface Wind Speeds in WRF

Abstract

Accurate near‐surface winds from the Weather Research and Forecasting (WRF) model are essential for wind‐energy applications, yet persistent biases remain that depend on terrain, season, and land cover. This study develops and evaluates an observation‐informed, time‐varying aerodynamic roughness length for WRF. The roughness length is inferred from the neutral log‐law using measured 10‐m winds and friction velocity from control WRF runs. A random‐forest model, trained on local meteorology is then used to produce a continuous 3‐hourly dynamic roughness length series. The dynamic roughness length is implemented in WRF by overwriting its default values each cycle while retaining the local land‐surface properties. The framework is tested at three Texas environments: complex topography, homogeneous cropland, and a coastal site during January and July 2021. Across sites, the inferred roughness length frequently exceeds WRF's default bounds, especially in summer, indicating under‐representation of effective surface drag in default tables. With the dynamic roughness length implemented into WRF, model performance improves across sites: mean‐bias magnitude decreases, point‐by‐point scatter tightens, explained variance increases, and high‐wind events are captured with smaller errors. These gains appear alongside frequent exceedance of the static WRF default bounds, indicating that observation‐informed roughness more faithfully represents effective surface drag. Overall, allowing roughness length to vary in time, anchored by local measurements—provides a practical and physically motivated lever to reduce near‐surface wind errors in WRF and highlights limitations of fixed, class‐based roughness values.