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Abstract We comprehensively analyzed geomagnetic perturbations using ground magnetic records from over 400 stations spanning four solar cycles, from 1976 to 2023. We assess the perturbations in the three magnetic components separately. Our study covers low, middle, and high magnetic latitudes in the northern magnetic hemisphere, with the primary objective of quantifying extreme values and evaluating their variability on magnetic latitude, local time, and solar cycle phases “minimum, ascending, maximum, and declining.” Our findings reveal spatial patterns to be less discernible as perturbations intensify, with distinct responses at middle and high latitudes. The extreme values, defined as percentiles 0 and 100, were observed to be localized and randomly distributed in local time, especially in the east magnetic component. Additionally, we observed dusk‐dawn asymmetries in the magnitude of perturbations related to the auroral electrojets, indicating complex interactions between the magnetosphere and ionosphere. Furthermore, the results reveal a preference for the most significant extreme values to occur in the declining phase of the solar cycle. These insights deepen our understanding of geomagnetic perturbations and their variability, contributing to space weather forecasting and mitigation strategies. 

Abstract Ionospheric conductance is a crucial factor in regulating the closure of magnetospheric field‐aligned currents through the ionosphere as Hall and Pedersen currents. Despite its importance in predictive investigations of the magnetosphere‐ionosphere coupling, the estimation of ionospheric conductance in the auroral region is precarious in most global first‐principles‐based models. This impreciseness in estimating the auroral conductance impedes both our understanding and predictive capabilities of the magnetosphere‐ionosphere system during extreme space weather events. In this article, we address this concern, with the development of an advanced Conductance Model for Extreme Events (CMEE) that estimates the auroral conductance from field‐aligned current values. CMEE has been developed using nonlinear regression over a year's worth of 1‐min resolution output from assimilative maps, specifically including times of extreme driving of the solar wind‐magnetosphere‐ionosphere system. The model also includes provisions to enhance the conductance in the aurora using additional adjustments to refine the auroral oval. CMEE has been incorporated within the Ridley Ionosphere Model (RIM) of the Space Weather Modeling Framework (SWMF) for usage in space weather simulations. This paper compares performance of CMEE against the existing conductance model in RIM, through a validation process for six space weather events. The performance analysis indicates overall improvement in the ionospheric feedback to ground‐based space weather forecasts. Specifically, the model is able to improve the prediction of ionospheric currents, which impact the simulateddB/dtandΔB, resulting in substantial improvements indB/dtpredictive skill. 

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. 

Key Points Large dB / dt “spikes” in ground magnetometer data occur in three local time hotspots in the pre‐midnight, dawn, and pre‐noon sectors These are consistent with spikes produced by substorm onsets, omega bands, and the Kelvin‐Helmholtz instability, respectively Spike occurrence is controlled by solar activity, maximizing in the declining phase of the solar cycle, esp. solar cycle 23 

Key Points These indices are not totally interchangeable, consideration should be given to index choice in model validation or cross‐study comparison Hourly averaged SMR and SYM‐H return levels track Dst for return periods below 10 years. Above that they exceed Dst; at 100 years by >10% One minute cadence SMR and SYM‐H 5, 10, 50, and 100 year return levels exceed that of Dst by about 10%, 12%, 20%, and 25% respectively