Implementation of WRF‐Urban Asymmetric Convective Model (UACM) for Simulating Urban Fog Over Delhi, India

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Accurate fog prediction in densely urbanized cities poses a challenge due to the complex influence of urban morphology on meteorological conditions in the urban roughness sublayer. This study implemented a coupled WRF-Urban Asymmetric Convective Model (WRF-UACM) for Delhi, India, integrating explicit urban physics with Sentinel-updated USGS land-use and urban morphological parameters derived from the UT-GLOBUS dataset. When evaluated against the baseline Asymmetric Convective Model (WRF-BACM) using Winter Fog Experiment (WiFEX) data, WRF-UACM significantly improved urban meteorological variables like diurnal variation of 10-meter wind speed, 2-meter air temperature (T2), and 2-meter relative humidity (RH2) during a fog event. UACM also demonstrates improved accuracy in simulating temperature and significantly reducing biases for wind speed and daytime RH2 under clear sky conditions. UACM reproduced the nighttime urban heat island effect within the city, showing realistic diurnal heating and cooling patterns that are important for accurate fog onset and duration. UACM effectively predicts the onset, evolution, and dissipation of fog, aligning well with observed data and satellite imagery. Compared to WRF-BACM, WRF-UACM reduces the cold bias soon after sunset, thus improving the fog onset error by ~4 hours. This study underscores the UACM's potential in enhancing fog prediction, urging further exploration of various fog types and their application in operational settings, thus offering invaluable insights for preventive measures and mitigating disruptions in urban regions.