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\begin{document}

\title{Improved Neutral Density Predictions through Machine Learning Enabled
Exospheric Temperature Model}



\author[1]{Richard Joseph Licata}%
\author[1]{Piyush M Mehta}%
\author[2]{Daniel R Weimer}%
\author[3]{W. Kent Tobiska}%
\affil[1]{West Virginia University}%
\affil[2]{Virginia Tech}%
\affil[3]{Space Environment Technologies}%


\vspace{-1em}



  
  \date{May 22, 2024}


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\begin{abstract}
The community has leveraged satellite accelerometer datasets in previous
years to estimate neutral mass density and subsequently exospheric
temperatures. We utilize derived temperature data and optimize a
nonlinear machine-learned (ML) regression model to improve upon the
performance of the linear EXTEMPLAR (EXospheric TEMPeratures on a
PoLyherdrAl gRid) model. The newly developed EXTEMPLAR-ML model allows
for exospheric temperature predictions at any location with a single
model and provides performance improvements over its predecessor. We
achieve a 4.2 K reduction in mean absolute error and a 3.42 K reduction
in the standard deviation of the error. Like EXTEMPLAR, our model's
outputs can be utilized by the Naval Research Laboratory Mass
Spectrometer and Incoherent Scatter radar Extended (NRLMSISE-00) model
to more closely match satellite accelerometer-derived densities. We
conducted two case studies where we compare the CHAllenging
Minisatellite Payload (CHAMP) and Gravity Recovery and Climate
Experiment (GRACE) accelerometer-derived temperature and density
estimates to NRLMSISE-00, EXTEMPLAR, and EXTEMPALR-ML during two major
storm periods. The storm-time temperature comparison showed error
reductions of 7-10\% and 2-5\% relative to NRLMSISE-00 and EXTEMPLAR,
respectively, and the density comparison showed error reductions of
20-55\% and 8-12\%. We use Principal Component Analysis to identify the
dominant modes of variability in the model over one solar cycle. This
shows the model is dominantly driven by solar activity, and there is a
strong latitudinal variation related to the Summer and Winter
hemispheres.%
\end{abstract}%



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