  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.
