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

Agnit Mukhopadhyay,Xianzhe Jia,Michael W Liemohn,Daniel T Welling

doi:10.3389/fspas.2021.637197

Abstract

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.

Highlights

The global state of the terrestrial magnetosphere may be broadly characterized by two categories of physical identifiers: (a) geomagnetic indices which indicate variations in the near-Earth space environment due to activity (e.g., Dst, Sym-H, Kp, AE; Pulkkinen et al, 2011; Glocer et al, 2016; Liemohn et al, 2018), and (b) physical quantities that help describe the morphology and energy balance in the magnetosphere
The global and inner magnetospheric components are connected to the Ridley Ionosphere Model (RIM) which solves for the ionospheric electrodynamics using a prescribed empirical conductance model (Ridley et al, 2004; Mukhopadhyay et al, 2020)
Because modeled magnetopause standoff distances (MPSD) and cross polar cap potential (CPCP) were compared against multiple datasets, the lone usage of error metrics like Root-Mean-Square Error (RMSE) is not enough to meaningfully rank model performance (Liemohn et al, 2021) as has often been done before (e.g., Pulkkinen et al, 2011)

Summary

Introduction

The global state of the terrestrial magnetosphere may be broadly characterized by two categories of physical identifiers: (a) geomagnetic indices which indicate variations in the near-Earth space environment due to activity (e.g., Dst, Sym-H, Kp, AE; Pulkkinen et al, 2011; Glocer et al, 2016; Liemohn et al, 2018), and (b) physical quantities that help describe the morphology and energy balance in the magnetosphere (ground magnetic perturbations dB/dt and B, field alignedMagnetopause Distance and CPCP Performance currents, polar cap potential; Rastätter et al, 2011; Honkonen et al, 2013; Pulkkinen et al, 2013; Anderson et al, 2017; Welling et al, 2017). The CPCP, on the other hand, acts as an instantaneous indicator of the amount of energy flowing into the Earth’s magnetosphere-ionosphere system from the solar wind (e.g., Boyle et al, 1997; Burke et al, 1999; Russell et al, 2001; Liemohn and Ridley, 2002; Ridley and Liemohn, 2002; Ridley, 2005; Ridley et al, 2010), and is frequently used in conjunction with field aligned currents (FACs) to describe ionospheric electrodynamics (e.g., Reiff et al, 1981; Siscoe et al, 2002a,b; Ridley et al, 2004; Khachikjan et al, 2008; Mukhopadhyay et al, 2020) These two quantities are difficult to measure globally, with MPSD estimates largely depending on satellite crossings of the magnetopause over a distributed period of time (e.g., Shue et al, 1997), and CPCP depending on incomplete global coverage of the hemisphere using groundbased observations and/or in-situ measurements from space (e.g., Gao, 2012). This task is made especially precarious when studying extreme events, as most of these techniques were not designed to simulate extreme conditions (e.g., Welling et al, 2017; Mukhopadhyay et al, 2020)

Methods

Results

Discussion

Conclusion