环境太阳风在行星际日冕物质抛射的传播中起着重要作用，并且是空间天气地磁暴的重要驱动因素。一种计算高效且广泛使用的预测地球附近环境太阳风径向速度的方法涉及耦合三个模型：Potential Field Source Surface、Wang-Sheeley-Arge (WSA) 和 Heliospheric Upwind eXtrapolation。然而，该模型链有十一个不确定参数，这些参数由于经验关系和简化的物理假设而主要是非物理的。因此，我们提出了一个全面的不确定性量化（UQ）框架，该框架能够成功地量化和减少模型链中的参数不确定性。该UQ框架利用基于方差的全局敏感性分析，随后通过马尔可夫链蒙特卡洛进行贝叶斯推断，以学习最具影响力参数的后验密度。敏感性分析结果表明，五个最具影响力的参数全部是WSA参数。此外，我们表明这些有影响力参数的后验密度从一个卡林顿自转周到下一个卡林顿自转周变化很大。这些有影响力的参数试图过度补偿模型链中缺失的物理过程，这凸显了增强模型链对WSA参数选择鲁棒性的必要性。从学习到的后验密度生成的集合预测显著降低了地球附近太阳风速度预测的不确定性。
英文摘要：
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.
