Bayesian Inference and Global Sensitivity Analysis for Ambient Solar Wind Prediction.

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 11 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. Plain Language Summary: Predicting the ambient solar wind is an important component of space weather forecasting. We use advanced statistical techniques to analyze the important parameters in a widely used ambient solar wind model. The numerical results show that five specific parameters have the largest impact on solar wind model predictions near Earth and that these parameters can fluctuate considerably over time. The statistical results give us a deeper understanding of the limitations and potential improvements for enhancing the accuracy and reliability of ambient solar wind forecasts. Such an understanding is essential for mitigating the potential impacts of severe geomagnetic storms. Key Points: We quantify and reduce the parametric uncertainties of the Potential Field Source Surface, Wang‐Sheeley‐Arge (WSA), and Heliospheric Upwind eXtrapolation models on the ambient solar wind predictions near EarthGlobal sensitivity analysis shows that the five most influential parameters are all numerical parameters in the WSA modelThe posterior of the influential parameters changes greatly in time, motivating the investigation of the forecasting capability of WSA [ABSTRACT FROM AUTHOR]