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On 10 May 2024, a series of coronal mass ejections were detected at Earth followed by one of the most powerful geomagnetic storms since November 2003. Leveraging a multi–technique approach, this paper provides an account of the ground geomagnetic response during the 10–11 May 2024 extreme geomagnetic storm. More specifically, we show that at the mid-latitudes in the American sector, the storm produced extreme ground geomagnetic field perturbations between 01:50 UT and 02:30 UT on 11 May. Then using the Spherical Elementary Current System method, it is shown that the perturbations were associated with an intense westward propagating auroral westward electrojet current. Finally, with the aid of auroral all-sky images from the Missouri Skies Observatory, we demonstrate that an intense isolated substorm event with onset located between the Great Lakes region and the East Coast United States was the main source of the extreme westward electrojet current and the geomagnetic field perturbations at these typical mid-latitude locations. This study emphasizes the increased risk associated with expansion of the auroral oval into the mid-latitudes during extreme geomagnetic activity. 

Abstract Ultraviolet images of Earth's polar regions obtained by high altitude spacecraft have proved to be immensely useful for documenting numerous features of the aurora and understanding the coupling between Earth's magnetosphere and ionosphere. In this study we have examined images obtained by the far ultraviolet Spectrographic Imager camera on the IMAGE satellite during the first three years of its mission (2000–2002) for comparison with observations of large geomagnetic disturbances (GMDs) by ground‐based magnetometers in eastern Arctic Canada. To our knowledge, this is the first study to investigate the use of high‐altitude imager data to identify the global context of GMDs. We found that rapid auroral motions or localized intensifications visible in these images coincide with regions of largedB/dtas well as localized and closely spaced up/down vertical currents and increased equivalent ionospheric currents, but one of the two events presented did not appear to be related to substorm processes. These magnetic perturbations and currents can appear or disappear in a few tens of seconds, thus highlighting the importance of images with a high cadence. 

Abstract Natural hazards, such as weather in space and the terrestrial environment, have the potential to disrupt critical technologies and infrastructures that contribute to national security and economic advancement. Enhancing our understanding of natural hazards is a central part to developing mitigation strategies to avert their impact on technological assets and/or infrastructure. With the support of the broader scientific community, the International Space Weather Initiative (ISWI) and the African Geophysical Society (AGS) successfully organized two international events in September–October 2023, namely, the ISWI space weather school and the AGS Annual Conference. Both events were locally hosted by the Physics Society of Zambia in Lusaka, Zambia. This paper is a summary report of the two events, highlighting efforts focused on advancing scientific research in Africa. The report also outlines some of the major challenges faced and discusses key considerations for organizing future meetings. 

Understanding of Earth’s geomagnetic environment is critical to mitigating the space weather impacts caused by disruptive geoelectric fields in power lines and other conductors on Earth’s surface. These impacts are the result of a chain of processes driven by the solar wind and linking Earth’s magnetosphere, ionosphere, thermosphere and Earth’s surface. Tremendous progress has been made over the last two decades in understanding the solar wind driving mechanisms, the coupling mechanisms connecting the magnetically controlled regions of near-Earth space, and the impacts of these collective processes on human technologies on Earth’s surface. Studies of solar wind drivers have been focused on understanding the responses of the geomagnetic environment to spatial and temporal variations in the solar wind associated with Coronal Mass Ejections, Corotating Interaction Regions, Interplanetary Shocks, High-Speed Streams, and other interplanetary magnetic field structures. Increasingly sophisticated numerical models are able to simulate the magnetospheric response to the solar wind forcing associated with these structures. Magnetosphere-ionosphere-thermosphere coupling remains a great challenge, although new observations and sophisticated models that can assimilate disparate data sets have improved the ability to specify the electrodynamic properties of the high latitude ionosphere. The temporal and spatial resolution needed to predict the electric fields, conductivities, and currents in the ionosphere is driving the need for further advances. These parameters are intricately tied to auroral phenomena—energy deposition due to Joule heating and precipitating particles, motions of the auroral boundary, and ion outflow. A new view of these auroral processes is emerging that focuses on small-scale structures in the magnetosphere and their ionospheric effects, which may include the rapid variations in current associated with geomagnetically induced currents and the resulting perturbations to geoelectric fields on Earth’s surface. Improvements in model development have paralleled the advancements in understanding, yielding coupled models that better replicate the spatial and temporal scales needed to simulate the interconnected domains. Many realizations of such multi-component systems are under development, each with its own limitations and advantages. Challenges remain in the ability of models to quantify uncertainties introduced by propagation of solar wind parameters, to account for numerical effects in model codes, and to handle the special conditions occurring during extreme events. The impacts to technical systems on the ground are highly sensitive to the local electric properties of Earth’s surface, as well as to the specific technology at risk. Current research is focused on understanding the characteristics of geomagnetic disturbances that are important for geomagnetically induced currents, the development of earth conductivity models, the calculation of geoelectric fields, and the modeling of induced currents in the different affected systems. Assessing and mitigating the risks to technical systems requires quantitative knowledge of the range of values to be expected under all possible geomagnetic and technical conditions. Considering the progress that has been made in studying the chain of events leading to hazardous geomagnetic disturbances, the path forward will require concerted efforts to reveal missing physics, improve modeling capabilities, and deploy new observational assets. New understanding should be targeted to accurately quantify solar wind driving, magnetosphere-ionosphere-thermosphere coupling, and the impacts on specific technologies. The research, modeling, and observations highlighted here provide a framework for constructing a plan by which the international science community can comprehensively address the growing threat to human technologies caused by geomagnetic disturbances. 

Forecasting ground magnetic field perturbations has been a long-standing goal of the space weather community. The availability of ground magnetic field data and its potential to be used in geomagnetically induced current studies, such as risk assessment, have resulted in several forecasting efforts over the past few decades. One particular community effort was the Geospace Environment Modeling (GEM) challenge of ground magnetic field perturbations that evaluated the predictive capacity of several empirical and first principles models at both mid- and high-latitudes in order to choose an operative model. In this work, we use three different deep learning models-a feed-forward neural network, a long short-term memory recurrent network and a convolutional neural network-to forecast the horizontal component of the ground magnetic field rate of change ( dB H / dt ) over 6 different ground magnetometer stations and to compare as directly as possible with the original GEM challenge. We find that, in general, the models are able to perform at similar levels to those obtained in the original challenge, although the performance depends heavily on the particular storm being evaluated. We then discuss the limitations of such a comparison on the basis that the original challenge was not designed with machine learning algorithms in mind. 

Abstract Our current knowledge of the geomagnetic poleward and equatorward boundary dynamics is limited, particularly, how deep those two latitudinal boundaries can extend into lower geomagnetic latitudes during magnetic storms. We want to understand the motion of the boundary because it is important in terms of the location and magnitude of the effects of geomagnetic disturbances associated with storms on the ground. In this study we derive spherical elementary ionospheric currents from ground magnetometer arrays covering North America and Greenland during six magnetic storms in 2015 and 2018. With two dimensional maps of the auroral region current, we select the equatorward boundary of the region 2 currents by‐eye and fit the boundary with an ellipse to derive the location of the equatorward boundary at magnetic midnight. We have obtained over 500 boundaries and find that the midnight boundary location varies between 45° and 66° magnetic latitude. We examine the influence of the interplanetary magnetic field (IMF), solar wind plasma, and geomagnetic indices on the location of the magnetic midnight equatorial boundary and find that the equatorial boundary location is best correlated with the IMF Bz, VBz, and the Sym‐H index. We demonstrate that as the Bz component becomes more negative, the magnitude of VBz increases, and the magnitude of the Sym‐H index increases, the magnetic midnight equatorial boundary shifts equatorward during periods of moderate to high geomagnetic activity. 

Abstract The growing depth and breadth of data spanning the solar‐terrestrial environment requires new ways of representing and analyzing the available information. This paper applies one such new data representation—network analysis—to the study of Geomagnetically Induced Currents (GICs) in electric power lines. This work uses newly available electric current data collected by power utilities through the the Electric Power Research Institute (EPRI) SUNBURST project and magnetometer data from the Super Magnetometer Initiative. The magnetometer data are analyzed using wavelet analysis. This new analysis method shows deviations to be more likely for equatorial stations close to water, which may be caused by the coast effect. The deviation likelihood is a complex function of latitude and magnetic local time. The GIC data are analyzed using “Quiet Day Curves” (QDCs) which help isolate geomagnetic disturbances. We find that current deviations are more common in the early morning sector, but this trend differs from station to station. These current and magnetometer data are represented in a network as nodes which are connected when both the current and magnetic measurements have a statistically significant deviation from their baseline behavior. This network is used to study the link between space weather and GICs. To do this, times when a current deviation exists are compared to times when magnetic deviations exist for each magnetometer ‐ current sensor pair. Current deviations are, on average, 1.83 times more likely when there are magnetic deviations. However, some magnetometer deviations are more indicative than others, with the strongest probability multipliers reaching 3. 

Abstract Geomagnetically induced currents (GICs) at middle latitudes have received increased attention after reported power grid disruptions due to geomagnetic disturbances. However, quantifying the risk to the electric power grid at middle latitudes is difficult without understanding how the GIC sensors respond to geomagnetic activity on a daily basis. Therefore, in this study the question “Do measured GICs have distinguishable and quantifiable long‐period and short‐period characteristics?” is addressed. The study focuses on the long‐term variability of measured GIC, and establishes the extent to which the variability relates to quiet‐time geomagnetic activity. GIC quiet‐day curves (QDCs) are computed from measured data for each GIC node, covering all four seasons, and then compared with the seasonal variability of thermosphere‐ionosphere‐electrodynamics general circulation model (TIE‐GCM)‐simulated neutral wind and height‐integrated current density. The results show strong evidence that the middle‐latitude nodes routinely respond to the tidal‐driven Sq variation, with a local time and seasonal dependence on the direction of the ionospheric currents, which is specific to each node. The strong dependence of GICs on the Sq currents demonstrates that the GIC QDCs may be employed as a robust baseline from which to quantify the significance of GICs during geomagnetically active times and to isolate those variations to study independently. The QDC‐based significance score computed in this study provides power utilities with a node‐specific measure of the geomagnetic significance of a given GIC observation. Finally, this study shows that the power grid acts as a giant sensor that may detect ionospheric current systems. 

Abstract Geomagnetic indices are convenient quantities that distill the complicated physics of some region or aspect of near‐Earth space into a single parameter. Most of the best‐known indices are calculated from ground‐based magnetometer data sets, such as Dst, SYM‐H, Kp, AE, AL, and PC. Many models have been created that predict the values of these indices, often using solar wind measurements upstream from Earth as the input variables to the calculation. This document reviews the current state of models that predict geomagnetic indices and the methods used to assess their ability to reproduce the target index time series. These existing methods are synthesized into a baseline collection of metrics for benchmarking a new or updated geomagnetic index prediction model. These methods fall into two categories: (1) fit performance metrics such as root‐mean‐square error and mean absolute error that are applied to a time series comparison of model output and observations and (2) event detection performance metrics such as Heidke Skill Score and probability of detection that are derived from a contingency table that compares model and observation values exceeding (or not) a threshold value. A few examples of codes being used with this set of metrics are presented, and other aspects of metrics assessment best practices, limitations, and uncertainties are discussed, including several caveats to consider when using geomagnetic indices.