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\begin{document}

\title{Urban Air Temperature Model Using GOES-16 LST and a Diurnal Regressive
NeuralNetwork Algorithm}



\author[1]{Joshua Hrisko}%
\author[2]{Prathap Ramamurthy}%
\author[3]{Yunyue Yu}%
\author[4]{Peng Yu}%
\affil[1]{CUNY City College of New York}%
\affil[2]{Princeton University}%
\affil[3]{NOAA}%
\affil[4]{NOAA National Environmental Satellite, Data, and Information Service}%


\vspace{-1em}



  
  \date{June 14, 2024}


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\begin{abstract}
An urban air temperature model is presented using GOES-16 land surface
temperature. The Automated Surface Observing System (ASOS) serves as
ground truth air temperature for calibration and testing of the model.
The National Land Cover Database (NLCD) is used to calculate a weighted
distribution of 20 land classifications for each satellite pixel
surrounding a nearby ASOS station. A time-match algorithm aligns the
ground and satellite measurements within 5-minutes of one another, and
the resulting matched LST and air temperature are compared over nine
months to investigate their cross-correlation. A model is constructed by
fitting their difference using a gaussian profile. Landcover, latitude,
longitude, local time, and elevation are inputted into an artificial
regressive neural network to fit each unique GOES-16 pixel. Over 100
urban stations and satellite pixels throughout the continental U.S. are
used to construct the diurnal gaussian model and approximate air
temperature. Early statistics indicate favorable results, competing with
other studies with more complicated and intensive calculations. The
presentation of this model is intended to simplify the calculation of
air temperature from satellite LST and create a successful model that
performs well in urban environments. The improvement of urban air
temperature calculations will also result in improved satellite land
surface products such as relative humidity and heat index.%
\end{abstract}%



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