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\begin{document}

\title{Bayesian Inference and Global Sensitivity Analysis for Ambient Solar
Wind Prediction}



\author[1]{Opal Issan}%
\author[2]{Pete Riley}%
\author[3]{Enrico Camporeale}%
\author[4]{Boris Kramer}%
\affil[1]{University of California San Diego}%
\affil[2]{Predictive Science Inc.}%
\affil[3]{University of Colorado}%
\affil[4]{Department of Mechanical and Aerospace Engineering University of California San Diego}%


\vspace{-1em}



  
  \date{May 26, 2024}


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\begin{abstract}
The ambient solar wind plays a significant role in propagating
interplanetary coronal mass ejections and is an important driver of
space weather geomagnetic storms. A computationally efficient and widely
used method to predict the ambient solar wind radial velocity near Earth
involves coupling three models: Potential Field Source Surface,
Wang-Sheeley-Arge (WSA), and Heliospheric Upwind eXtrapolation. However,
the model chain has eleven uncertain parameters that are mainly
non-physical due to empirical relations and simplified physics
assumptions. We, therefore, propose a comprehensive uncertainty
quantification (UQ) framework that is able to successfully quantify and
reduce parametric uncertainties in the model chain. The UQ framework
utilizes variance-based global sensitivity analysis followed by Bayesian
inference via Markov chain Monte Carlo to learn the posterior densities
of the most influential parameters. The sensitivity analysis results
indicate that the five most influential parameters are all WSA
parameters. Additionally, we show that the posterior densities of such
influential parameters vary greatly from one Carrington rotation to the
next. The influential parameters are trying to overcompensate for the
missing physics in the model chain, highlighting the need to enhance the
robustness of the model chain to the choice of WSA parameters. The
ensemble predictions generated from the learned posterior densities
significantly reduce the uncertainty in solar wind velocity predictions
near Earth.%
\end{abstract}%



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