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Implementation of WRF-Urban Asymmetric Convective Model (UACM) for Simulating Urban Fog over Delhi, India
  • +8
  • Utkarsh Prakash Bhautmage,
  • Sachin D. Ghude,
  • Avinash N. Parde,
  • Harsh G. Kamath,
  • Narendra Gokul Dhangar,
  • Jonathan E. Pleim,
  • Michael Mau Fung Wong,
  • Sandeep Dnyandeo Wagh,
  • Rakesh Kumar,
  • Dev Niyogi,
  • Rajeevan M
Utkarsh Prakash Bhautmage
National University of Singapore

Corresponding Author:[email protected]

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Sachin D. Ghude
Indian Institute of Tropical Meteorology
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Avinash N. Parde
Indian Institute of Tropical Meteorology
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Harsh G. Kamath
The University of Texas at Austin
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Narendra Gokul Dhangar
Department of Atmospheric and Space Sciences, Pune University, India
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Jonathan E. Pleim
U.S. EPA
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Michael Mau Fung Wong
Hong Kong University of Science and Technology
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Sandeep Dnyandeo Wagh
Unknown
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Rakesh Kumar
India Meteorological Department
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Dev Niyogi
The University of Texas
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Rajeevan M
Ministry of Earth Sciences
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Abstract

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) on a fog day. UACM also demonstrates improved accuracy in simulating temperature and a significant reduction in biases for RH2 and wind speed 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 the 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 its application in operational settings, thus offering invaluable insights for preventive measures and mitigating disruptions in urban regions.
10 Jan 2024Submitted to ESS Open Archive
02 Feb 2024Published in ESS Open Archive