Bayesian Inference and Global Sensitivity Analysis for Ambient Solar Wind Prediction

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 widely used method to predict the radial velocity near Earth involves coupling three models: Potential Field Source Surface, Wang‐Sheeley‐Arge (WSA), Heliospheric Upwind eXtrapolation. However, model chain has 11 uncertain parameters that are mainly non‐physical due empirical relations simplified physics assumptions. We, therefore, propose comprehensive uncertainty quantification (UQ) framework able successfully quantify reduce parametric uncertainties chain. UQ utilizes variance‐based global sensitivity analysis followed by Bayesian inference via Markov Monte Carlo learn posterior densities most influential parameters. results indicate five all WSA Additionally, we show such vary greatly from one Carrington rotation next. trying overcompensate for missing chain, highlighting need enhance robustness choice ensemble predictions generated learned significantly Earth.