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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.

We perform ensemble simulations of radiation belt electron acceleration using the quasi‐linear approach during the storm on 9 October 2012, where chorus waves dominated electron acceleration atL = 5.2. Based on a superposed epoch analysis of 11 similar storms when both multi‐MeV electron flux enhancements and chorus wave activities were observed by Van Allen Probes, we use percentiles to sample the normalized input distributions for the four key inputs to estimate their relative perturbations. Using 11 points in each input parameter including chorus wave amplitudeB_w, chorus wave peak frequencyf_m, background magnetic fieldB₀, and electron densityN_e, we ran 11⁴simulations to quantify the impact of uncertainties in the input parameters on the resulting simulated electron acceleration by chorus. By comparing the simulations to observations, our ensemble simulations reveal that inaccuracies in all four input parameters significantly affect the simulated electron acceleration, with the largest simulation errors attributed to the uncertainties inB_w,N_e, andf_m. The simulation can deviate from the observations by four orders of magnitude, while members with largest probability density (smallest perturbations in the input) provide reasonable estimations of output fluxes with log accuracy errors concentrated between ∼−2.0 and 0.5. Quantifying the uncertainties in our study is a prerequisite for the validation of our radiation belt electron model and improvements of accurate electron flux predictions.