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\begin{document}

\title{Implementation of WRF-Urban Asymmetric Convective Model (UACM) for
Simulating Urban Fog over Delhi, India}



\author[1]{Utkarsh Prakash Bhautmage}%
\author[2]{Sachin D. Ghude}%
\author[2]{Avinash N. Parde}%
\author[3]{Harsh G. Kamath}%
\author[4]{Narendra Gokul Dhangar}%
\author[5]{Jonathan E. Pleim}%
\author[6]{Michael Mau Fung Wong}%
\author[7]{Sandeep Dnyandeo Wagh}%
\author[8]{Rakesh Kumar}%
\author[9]{Dev Niyogi}%
\author[10]{Rajeevan M}%
\affil[1]{National University of Singapore}%
\affil[2]{Indian Institute of Tropical Meteorology}%
\affil[3]{The University of Texas at Austin}%
\affil[4]{Department of Atmospheric and Space Sciences, Pune University, India}%
\affil[5]{U.S. EPA}%
\affil[6]{Hong Kong University of Science and Technology}%
\affil[7]{Unknown}%
\affil[8]{India Meteorological Department}%
\affil[9]{The University of Texas}%
\affil[10]{Ministry of Earth Sciences}%


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  \date{May 25, 2024}


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\begin{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 \textasciitilde{}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.%
\end{abstract}%



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\textbf{Hosted file}

\verb`984020_0_art_file_11755110_s6xzxx.docx`  available at \url{https://authorea.com/users/710675/articles/698924-implementation-of-wrf-urban-asymmetric-convective-model-uacm-for-simulating-urban-fog-over-delhi-india}

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