Mapping the urban canopy drag coefficient using CFD and machine learning

The urban drag coefficient (C_d​) is a key parameter in urban climate modeling, influencing airflow, pollutant dispersion, and energy exchange. This study aims to map the spatial distribution of Cd​ in the Guangdong-Hong Kong-Macau Greater Bay Area (GBA) by combining computational fluid dynamics (CFD) simulations and machine learning techniques. We employ the Reynolds-Averaged Navier-Stokes (RANS) turbulence model to simulate airflow over idealized urban surfaces with varying building density, average height, and height variability. Two groups of urban configurations are designed—one with uniform building heights and another with heterogeneous heights—considering multiple plan area fractions (λ_p​) and standard deviation of building heights (σ_H​). Machine learning models, particularly Random Forest, are trained on CFD-derived Cd​ values to predict spatially distributed drag coefficients at a 1-km resolution. SHapley Additive exPlanations (SHAP) analysis reveals that λ_pand σ_H​ are the dominant factors influencing C_d​. The resulting drag coefficient map captures the spatial variability of urban aerodynamic resistance, providing a refined parameterization scheme for mesoscale climate models. This study enhances the representation of urban canopy processes and can be extended to other metropolitan regions, improving the accuracy of urban climate simulations.