Changing aerodynamic roughness in WRF reduces bias and improves accuracy in near‐surface wind simulations

Song, Hongquan, Ni, Xueli, Chang, Yujie, Chappell, Adrian ORCID: https://orcid.org/0000-0002-0694-7348, Zhou, Zhuoli and Liu, Boyan 2026. Changing aerodynamic roughness in WRF reduces bias and improves accuracy in near‐surface wind simulations. Geophysical Research Letters 53 (6) , e2025GL120811. 10.1029/2025gl120811

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Abstract

Plain Language Summary: Accurate near‐surface wind simulations are essential for weather forecasting, air‐quality assessment, dust‐storm prediction, and wind‐energy planning. However, most climate models show substantial bias in simulating near‐surface wind speeds because they rely on fixed and overly simplified aerodynamic roughness that cannot represent continuous changes in land surface conditions. In this study, we introduce a Changing Aerodynamic Roughness (CAR) scheme into the Weather Research and Forecasting (WRF) model, using satellite observations to link wind friction to real variations in land‐surface properties. By dynamically adjusting surface roughness in response to changes in vegetation cover, soil texture, and land use, CAR provides a realistic representation of surface‐atmosphere interactions. Applied to simulations over China in 2023, CAR substantially improved the agreement between modeled and observed winds. The mean bias in 10‐m wind speed decreased from 14.6% to 2.2%, with consistent improvements across seasons, particularly over cropland, shrubland, grassland, and bare land. This approach produces more physically realistic wind fields to enhance applications such as air‐quality modeling, dust‐emission prediction, and wind‐energy assessment.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Earth and Environmental Sciences
Additional Information:	License information from Publisher: LICENSE 1: URL: http://creativecommons.org/licenses/by/4.0/
Publisher:	Wiley
ISSN:	0094-8276
Date of First Compliant Deposit:	19 March 2026
Date of Acceptance:	17 February 2026
Last Modified:	19 Mar 2026 09:45
URI:	https://orca.cardiff.ac.uk/id/eprint/185865

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