Global Magnetohydrodynamic Simulations: Performance Quantification of Magnetopause Distances and Convection Potential Predictions

• DOI: 10.3389/fspas.2021.637197
• Publication Date: 2021-04-21T09:28:44Z
• Authors: Agnit Mukhopadhyay; Xianzhe Jia; Daniel T. Welling; Michael W. Liemohn
• Keywords: NASA CCMC; ionospheric potential; space weather; QC801-809; Astronomy; model validation and analysis; Geophysics. Cosmic physics; QB1-991; 01 natural sciences; magnetopause standoff distance; global MHD modeling; 13. Climate action; 0105 earth and related environmental sciences

The performance of three global magnetohydrodynamic (MHD) models in estimating the Earth's magnetopause location and ionospheric cross polar cap potential (CPCP) have been presented. Using the Community Coordinated Modeling Center's Run-on-Request system and extensive database on results of various magnetospheric scenarios simulated for a variety of solar weather patterns, the aforementioned model predictions have been compared with magnetopause standoff distance estimations obtained from six empirical models, and with cross polar cap potential estimations obtained from the Assimilative Mapping of Ionospheric Electrodynamics (AMIE) Model and the Super Dual Auroral Radar Network (SuperDARN) observations. We have considered a range of events spanning different space weather activity to analyze the performance of these models. Using a fit performance metric analysis for each event, the models' reproducibility of magnetopause standoff distances and CPCP against empirically-predicted observations were quantified, and salient features that govern the performance characteristics of the modeled magnetospheric and ionospheric quantities were identified. Results indicate mixed outcomes for different models during different events, with almost all models underperforming during the extreme-most events. The quantification also indicates a tendency to underpredict magnetopause distances in the absence of an inner magnetospheric model, and an inclination toward over predicting CPCP values under general conditions.