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\begin{document}

\title{Multi-Model Ensembles for Upper Atmosphere Models}



\author[1]{Sean Elvidge}%
\author[1]{S R Granados}%
\author[2]{M J Angling}%
\author[1]{M K Brown}%
\author[1]{D R Themens}%
\author[1]{A G Wood}%
\affil[1]{Space Environment Group (SERENE), University of Birmingham}%
\affil[2]{In-Space Missions Ltd}%


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  \date{June 10, 2024}


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\begin{abstract}
Multi-model ensembles (MMEs) are used to improve the forecasts of
thermospheric neutral densities. A variety of algorithms for
constructing the model weights for the MMEs are described and have been
implemented including: performance weighting, independence weighting and
non-negative least squares. Using both empirical and physics-based
models, compared against in-situ CHAMP observations, the skill of each
MME weighting approach has been tested in both solar minimum and maximum
conditions. In both cases the MME performs better than any individual
model. A non-negative least squares weighting for the MME on a set of
bias corrected models provides a 68\% and 50\% reduction in the mean
square error compared to the best model (Jacchia-Bowman 2008) in the
solar minimum and maximum cases respectively.%
\end{abstract}%



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