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In this study, a detailed metric survey on the “Galaxy 15” (April 2010) space weather event is conducted to validate MAGNetosphere–Ionosphere–Thermosphere (MAGNIT), a semi-physical auroral ionospheric conductance model characterizing four precipitation sources, against AMPERE measurements via field-aligned current (FAC) characteristics. As part of this study, the comparative performance of three ionosphere electrodynamic specifications involving auroral conductance models, MAGNIT, Ridley Legacy Model (RLM) (empirical), and Conductance Model for Extreme Events (CMEE) (empirical), within the Space Weather Modeling Framework (SWMF), is demonstrated. Overall, MAGNIT exhibits marginally improved predictions; root mean square error values in upward and downward FACs of MAGNIT predictions compared to AMPERE data are smaller than those of CMEE and Ridley Ionosphere Model (RIM) by $\sim$12.7% and $\sim$6.24% before the storm, $\sim$4.52% and $\sim$2.13% better during the main phase, $\sim$1.98% and $\sim$1.27% worse during the second minimum, and better by $\sim$1.84% and $\sim$1.49% by the beginning of the recovery, respectively. In all three model configurations, the dusk and night magnetic local time (MLT) sectors over-predict throughout the storm, while the day and dawn MLT sectors under-predict in response to interplanetary magnetic field (IMF) conditions. In addition to accuracy and bias, similar results and conclusions are drawn from additional metrics, including in the categories of correlation, precision, extremes, and skill, and recommendations are made for the best-performing model configuration in each metric category. Visual data–model comparisons conducted by studying the FAC location and latitude/MLT spread throughout various phases of the storm suggest that the spatial extent of the FACs is captured relatively well in the night-side auroral oval, unlike in the day-side oval. The spread in latitude of the FACs matches that in the previous literature on other model performances. This information on auroral precipitation sources and their weight on FACs, along with metrics from model–data comparisons, can be used to modify MAGNIT settings to optimize SWMF model performance. 

During geomagnetic storms a large amount of energy is transferred into the ionosphere-thermosphere (IT) system, leading to local and global changes in e.g., the dynamics, composition, and neutral density. The more steady energy from the lower atmosphere into the IT system is in general much smaller than the energy input from the magnetosphere, especially during geomagnetic storms, and therefore details of the lower atmosphere forcing are often neglected in storm time simulations. In this study we compare the neutral density observed by Swarm-C during the moderate geomagnetic storm of 31 January to 3 February 2016 with the Thermosphere-Ionosphere-Electrodynamics-GCM (TIEGCM) finding that the model can capture the observed large scale neutral density variations better in the southern than northern hemisphere. The importance of more realistic lower atmospheric (LB) variations as specified by the Whole Atmosphere Community Climate Model eXtended (WACCM-X) with specified dynamics (SD) is demonstrated by improving especially the northern hemisphere neutral density by up to 15% compared to using climatological LB forcing. Further analysis highlights the importance of the background atmospheric condition in facilitating hemispheric different neutral density changes in response to the LB perturbations. In comparison, employing observationally based field-aligned current (FAC) versus using an empirical model to describe magnetosphere-ionosphere (MI) coupling leads to an 7–20% improved northern hemisphere neutral density. The results highlight the importance of the lower atmospheric variations and high latitude forcing in simulating the absolute large scale neutral density especially the hemispheric differences. However, focusing on the storm time variation with respect to the quiescent time, the lower atmospheric influence is reduced to 1–1.5% improvement with respect to the total observed neutral density. The results provide some guidance on the importance of more realistic upper boundary forcing and lower atmospheric variations when modeling large scale, absolute and relative neutral density variations. 

Abstract We examine the statistical distribution of large‐scale Birkeland currents measured by the Active Magnetosphere and Planetary Electrodynamics Response Experiment in four unique categories of geomagnetic activity for the first time: quiet times, storm times, quiet‐time substorms, and storm‐time substorms. A novel method is employed to sort data into one of these four categories, and the categorizations are provided for future research. The mean current density is largest during substorms and its standard deviation is largest during geomagnetic storms. Current densities which are above a low threshold are more likely during substorms, but extreme currents are far more likely during geomagnetic storms, consistent with a paradigm in which geomagnetic storms represent periods of enhanced variability over quiet times. We demonstrate that extreme currents are most likely to flow within the Region 2 current during geomagnetic storms. This is unexpected in a paradigm of the current systems in which Region 1 current is generally larger. 

Abstract Characterization of Earth's magnetic field is key to understanding dynamics of the core. We assess whether Iridium Communications magnetometer data can be used for this purpose since. The 66 Iridium satellites are in 86° inclination, 780 km altitude, circular orbits, with 11 satellites in each of six orbit planes. In one day the constellation returns 300,000 measurements spanning the globe with <2° spacing. We used data from January 2010 through November 2015, and compared against International Geomagnetic Reference Field (IGRF‐11) to inter‐calibrate all data to the same model. Geomagnetically quiet 24‐h intervals were selected using the total Birkeland current, auroral electrojet, and ring current indices. Thez‐scores for these quantities were combined and the quietest 16 intervals from each quarter selected for analysis. Residuals between the data and IGRF‐11 yield consistent patterns that evolve gradually from 2010 to 2015. Residuals for each day were binned in 9° latitude by 9° longitude and the distributions about the mean in each bin are Gaussian with 1‐sigma standard errors of ∼3 nT. Spherical harmonic coefficients for each quiet day were computed and time series of the coefficients used to identify artifacts at the orbit precession (8 months) and seasonal (12 months) periods and their harmonics which were then removed by notch filtering. This analysis yields time series at 800 virtual geomagnetic observatories each providing a global field map using a single day of data. The results and CHAOS 7.4 generally agree, but systematic differences larger than the statistical uncertainties are present that warrant further exploration.