Bayesian Inference and Global Sensitivity Analysis for Ambient Solar Wind Prediction Journal Article uri icon

Overview

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.

publication date

  • May 25, 2023

has restriction

  • green

Date in CU Experts

  • June 7, 2023 2:54 AM

Full Author List

  • Issan O; Riley P; Camporeale E; Kramer B

author count

  • 4

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