Abstract Variations in the solar wind, particularly those associated with interplanetary coronal mass ejections (ICMEs) and high‐speed streams (HSSs), drive geomagnetic disturbances that produce rapid changes in the ground magnetic field. These variations, commonly quantified by the time derivative of the horizontal magnetic field , are closely related to geomagnetically induced currents (GICs), which pose risks to technological infrastructure. In this study, we analyze 609 geomagnetic storms (SYM‐H nT) from 1995 to 2024, interplanetary coronal mass ejection and HSS driven events, to investigate how different solar wind drivers modulate the occurrence, intensity, and spatiotemporal distribution of spikes. We find that enhancements are concentrated between 60 and 75 magnetic latitude for both drivers, but exhibit distinct Magnetic Local Time (MLT) distributions. HSS‐driven storms predominantly produce pre‐midnight enhancements, consistent with substorm‐related processes, whereas ICME‐driven storms generate a broader MLT distribution, including significant post‐midnight activity associated with multiple current systems, such as the substorm current wedge and field‐aligned currents. Additionally, we show that the occurrence and intensity of spikes vary systematically with the solar cycle, with ICME‐driven activity peaking during solar maxima and HSS‐driven activity dominating the declining and minimum phases. These results provide a unified long‐term perspective on how solar wind drivers control rapid geomagnetic field variations, highlighting the importance of distinguishing between driver types for improving space weather modeling and GIC risk assessment.

Using Parker Solar Probe measurements of the solar wind, we demonstrate that β_∥ is the main driver that determines which instabilities limit proton temperature anisotropy. At radial distances from 10 to 30 solar radii, β_∥<1 drives electromagnetic ion-cyclotron and parallel firehose instabilities, in contrast to the situation at 1 astronomical unit, where, due to most β_∥>1, mirror and oblique firehose modes are dominant instead. Furthermore, we show that the temperature anisotropy radially evolves following the semi-empirical anti-correlation T_⊥/T_∥_∥^-0.55, consistent with observations at larger distances from the Sun.

Geomagnetically induced currents (GICs) pose a significant space weather hazard, driven by geomagnetic field variation due to the coupling of the solar wind to the magnetosphere-ionosphere system. Extensive research has been dedicated to understanding ground-level geomagnetic field perturbations as a GIC proxy. Still, the non-uniform aspect of geomagnetic fluctuations make it difficult to fully characterize the ground-level magnetic field across large regions of the globe. Here, we focus on localized geomagnetic disturbances (LGMDs) in the North American region and specify the degree to which these disturbances are localized. Employing the electrodynamics-informed Spherical Elementary Current Systems (SECS) method, we spatially interpolate magnetic field perturbations between ground-based magnetometer stations. In this way, we represent the ground magnetic field as a series of heatmaps at high temporal and spatial resolution. We leverage heatmaps from storm time during solar cycle 24 to automatically identify LGMDs. We build a statistical picture of the frequency with which LGMDs occur, their scale sizes, and their latitude-longitude aspect ratios. Additionally, we use an information theory approach to quantify the dependence of these three attributes on the phase of the solar cycle. We find no clear influence of the solar cycle on any of the three attributes. We offer some avenues toward explaining why LGMDs might behave broadly the same whether they arise during solar maximum or solar minimum.

In this work, we used a dataset of 60 relativistic electron enhancement events measured at geostationary orbit (GEO) to study the correlation between the >2 MeV and >4 MeV electron fluxes. We then use the fluxes at GEO to compare against in-situ measurements from the Van Allen Probes mission and study the radial response of outer belt fluxes and the correlation between the fluxes at GEO and those closer to the Earth. The enhancement events occurred between 1 October 2012 and 31 December 2017 and were identified using Geostationary Operational Environmental Satellite (GOES) 15 >2 MeV fluxes at GEO. We compare with fluxes measured by the Van Allen probes Energetic Particle, Composition and Thermal Plasma Suite Relativistic Electron-Proton Telescope (ECT-REPT) between 2.5 < L < 6.0 at E = 2.1 MeV and E = 4.2 MeV. We found that the response of the radiation belts during enhancement events is very homogeneous for L > 4.0 and extremely similar for L > 5.0. Post-enhancement maximum fluxes show a remarkable correlation for all L > 4.0 at both the 2.1 MeV and 4.2 MeV energies, indicating that modeling or forecasting efforts of the outer radiation belt using data from geostationary orbit are justified.

Solar wind particles interact with the Earth's magnetic field and can cause rapid changes in the magnetic field on the ground. This can result in Geomagnetically Induced Currents capable of causing significant damage to infrastructure, making it vital to predict when and where the fluctuations will occur so the impact can be limited. The fluctuations can occur on both a large and highly localized scale, further complicating precise predictions. Machine learning (ML) techniques have emerged as an effective method of predicting space weather phenomena, with their largest complication being their lack of explainability. Here we seek to use such ML methods, combined with a model explainability technique called SHapley Additive exPlanation to both predict dBH/dt $d{B}_{H}/dt$ and times of extreme localization. Using L1 solar wind data and magnetometer data from SuperMAG, we train two different types of models, one predicting extreme dBH/dt $d{B}_{H}/dt$ and one predicting large Region-to-Specific Difference (RSD). We are seeking to forecast the maximum of RSD and dBH/dt $d{B}_{H}/dt$ within a rolling 60-min window, beginning 30 min in the future. The models perform well across a variety of latitudes and Magnetic Local times. While traditional drivers of space weather (BZIMF ${B}_{Z}<^>{\mathit{IMF}}$ and VX ${V}_{X}$) are important drivers of the ML models, other not often examined parameters (particularly BXIMF ${B}_{X}<^>{\mathit{IMF}}$) exhibit non-uniform spatial and latitudinal dependencies which cannot be attributed to correlation with more influential parameters. Additionally, the inertia of the internal geomagnetic field on a regional scale exhibits a more nuanced behavior compared to previous studies on individual magnetometer stations.

We performed a statistical study on the correlation between electromagnetic Ultra Low Frequency (ULF) waves and the evolution of relativistic electron fluxes in the outer radiation belt for 3.1<L∗<6.0 during 101 geomagnetic storms that occurred between January 2013 and November 2018. We used the Van Allen Probes MagEIS and REPT instruments to study electron fluxes from 0.47 MeV to 5.2 MeV, and we utilized magnetic field data from EMFISIS to calculate magnetic field fluctuations parallel and perpendicular to the background magnetic field direction and obtain the ULF integrated power between 1 mHz and 10 mHz. We analyzed the data during the following three different time intervals: the main phase, the recovery phase, and the entire storm. We computed the Pearson’s correlation coefficient and mutual information score between the ratio of fluxes before and after each given phase and the total integrated ULF power during the same time interval. Our results show a significant correlation between ULF wave power and changes in fluxes of hundreds of keV electrons during the main phase of the storms and for MeV electrons during the recovery phase of the storms. By studying fluxes at independent L∗, the largest correlations correspond to changes in fluxes before and after the entire storm and ULF fluctuations parallel to the field, especially for L∗<4.6. We evaluated the drift resonance frequency for azimuthal wavenumber 1≤m≤10 and found that for all considered energies and frequencies, the drift resonance with Pc5 ULF waves may occur in our region of study, which is consistent with the statistical results.

Strong Thermal Emission Velocity Enhancement (STEVE) is a fascinating optical phenomenon typically observed in the mid-latitude ionosphere. Recent observations reveal an exceptional STEVE event occurring at high latitudes, approximately 10 degrees poleward of previously documented cases. This event, recorded in Yellowknife, Canada, by a TREx RGB imager and a citizen scientist, coincided with Swarm satellite measurements of extreme westward ion drift velocities exceeding 4 km/s. Such velocities are generally associated with subauroral regions at mid-latitudes, making this high-latitude occurrence particularly striking.Notably, this event unfolded in the absence of a substorm, a departure from previous STEVE and extreme drift velocity observations. High-latitude radars detected rapid equatorward ionospheric flows, while GOES satellites recorded no particle injections, suggesting a highly inflated inner magnetosphere.This unique case study challenges existing paradigms of subauroral dynamics and highlights the significant influence of magnetospheric configurations on ionospheric responses. In this talk, we will discuss the characteristics of this event and examine the associated solar wind, magnetosphere, and ionosphere interactions.

The start of the space era marked the discovery of the Earth’s radiation belts. Sixty-five years after that discovery, there are still many unknowns in its dynamics, but there is also a lot of understanding about many processes. This review summarizes a broad overview of a particular topic: the effect of geomagnetic storms on the radiation belts from a historical perspective. In writing this review, we aim to preserve, rescue, and put together the initial attempts at understanding the radiation belts since most of the literature is somewhat hidden or forgotten. This is particularly relevant for students and young researchers, who did not live the initial stages of the space age. To help in this endeavor, we included the description of the main satellite missions and their instruments starting from the first satellites like Sputnik and Explorer and finishing with contemporary missions like Van Allen Probes and Arase, and different techniques. In this review, we also tried to include the majority of points of view about the dynamics of radiation belts, giving everybody a chance to analyze them and make their conclusions. We selected a limited number of papers from one side that are representative with respect to a specific topic and from the other side help to see the evolution of our knowledge about radiation belts. We made a special tribute to pioneering works of the 50s and 60s and also included some interesting results, which are almost impossible to read in English. So this review is a good starting point for navigating the ocean of modern space physics related to the outer radiation belt.

Recognition for All: A Way Forward to Enhance Diversity, Equity and Inclusion in Space Physics M. Fraz Bashir,1 Amy M. Keesee,2, 3 Seth G. Claudepierre,4 Michael D. Hartinger,5 Elizabeth A. MacDonald,6 and Allison Jaynes7 Department of Earth, Planetary and Space Sciences, UCLA, Los Angeles, CA, USA∗ Department of Physics and Astronomy, University of New Hampshire, Durham, NH, USA Space Science Center, University of New Hampshire, Durham, NH, USA Department of Atmospheric and Oceanic Sciences, UCLA, Los Angeles, CA, USA Center for Space Plasma Physics, Space Science Institute, Boulder, CO, USA NASA Goddard Space Flight Center, Greenbelt, MD, USA Department of Physics Astronomy, University of Iowa, IA, USA

Abstract The prediction of large fluctuations in the ground magnetic field (dB/dt) is essential for preventing damage from Geomagnetically Induced Currents. Directly forecasting these fluctuations has proven difficult, but accurately determining the risk of extreme events can allow for the worst of the damage to be prevented. Here we trained Convolutional Neural Network models for eight mid‐latitude magnetometers to predict the probability that dB/dt will exceed the 99th percentile threshold 30–60 min in the future. Two model frameworks were compared, a model trained using solar wind data from the Advanced Composition Explorer (ACE) satellite, and another model trained on both ACE and SuperMAG ground magnetometer data. The models were compared to examine if the addition of current ground magnetometer data significantly improved the forecasts of dB/dt in the future prediction window. A bootstrapping method was employed using a random split of the training and validation data to provide a measure of uncertainty in model predictions. The models were evaluated on the ground truth data during eight geomagnetic storms and a suite of evaluation metrics are presented. The models were also compared to a persistence model to ensure that the model using both datasets did not over‐rely on dB/dt values in making its predictions. Overall, we find that the models using both the solar wind and ground magnetometer data had better metric scores than the solar wind only and persistence models, and was able to capture more spatially localized variations in the dB/dt threshold crossings.

During periods of rapidly changing geomagnetic conditions electric fields form within the Earth’s surface and induce currents known as geomagnetically induced currents (GICs), which interact with unprotected electrical systems our society relies on. In this study, we train multi-variate Long-Short Term Memory neural networks to predict magnitude of north-south component of the geomagnetic field (| B N |) at multiple ground magnetometer stations across Alaska provided by the SuperMAG database with a future goal of predicting geomagnetic field disturbances. Each neural network is driven by solar wind and interplanetary magnetic field inputs from the NASA OMNI database spanning from 2000–2015 and is fine tuned for each station to maximize the effectiveness in predicting | B N |. The neural networks are then compared against multivariate linear regression models driven with the same inputs at each station using Heidke skill scores with thresholds at the 50, 75, 85, and 99 percentiles for | B N |. The neural network models show significant increases over the linear regression models for | B N | thresholds. We also calculate the Heidke skill scores for d| B N |/dt by deriving d| B N |/dt from | B N | predictions. However, neural network models do not show clear outperformance compared to the linear regression models. To retain the sign information and thus predict B N instead of | B N |, a secondary so-called polarity model is utilized. The polarity model is run in tandem with the neural networks predicting geomagnetic field in a coupled model approach and results in a high correlation between predicted and observed values for all stations. We find this model a promising starting point for a machine learned geomagnetic field model to be expanded upon through increased output time history and fast turnaround times.

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.

Interactions between plasma particles and electromagnetic waves play a crucial role in the dynamics and regulation of the state of space environments. From plasma physics theory, the characteristics of the waves and their interactions with the plasma strongly depend on the composition of the plasma, among other factors. In the case of the Earth’s magnetosphere, the plasma is usually composed of electrons, protons, O+ ions, and He+ ions, all with their particular properties and characteristics. Here, using plasma parameters relevant for the inner magnetosphere, we study the dispersion properties of kinetic Alfvén waves (KAWs) in a plasma composed of electrons, protons, He+ ions, and O+ ions. We show that heavy ions induce significant changes to the dispersion properties of KAWs, such as polarization, compressibility, and the electric-to-magnetic amplitude ratio, and therefore the propagation of KAWs is highly determined by the relative abundance of He+ and O+ in the plasma. These results, when discussed in the context of observations in the Earth’s magnetosphere, suggest that for many types of studies based on theory and numerical simulations, the inclusion of heavy ions should be customary for the realistic modeling of plasma phenomena in the inner magnetosphere or other space environments in which heavy ions can contribute a substantial portion of the plasma, such as planetary magnetospheres and comet plasma tails.

Using a time series of geomagnetic storm events between 1957 and 2019, obtained by selecting storms where Dst<-50 nT, we have analyzed the probability of occurrence of moderate, intense, and severe events. Considering that geomagnetic storms can be modeled as stochastic processes with a log-normal probability distribution over their minimum Dst index, the dataset was separated according to solar cycle (SC) and SC phases, and the distributions of events were fitted through maximum likelihood method in order to characterize the occurrence of storms in each cycle and phase, and then compare those occurrences to the SC24. Our results show that there is a strong dependence between the occurrence of intense storms, with Dst< -100 nT, and the strength of the SC measured by the sunspot numbers. In particular, SC24 is very similar to SC20. However, when comparing the occurrence of storms by SC phases, events tend to show similar activity toward the minimum phase and have significant differences in the maximum phases. By looking at the sigma value-the fit log-normal distribution "width" parameter-characteristic of the occurrence rate of storms, we have found that the sigma des (the sigma value in the descending phase of one cycle) shows the highest correlation (r=-0.76) with sigma max (the sigma value in the maximum phase of the next cycle) which allows us to estimate the occurrence rate of storms for SC25 to be similar to those of SC21 and SC22, suggesting a more intense cycle than the one that just ended.

The adiabatic and non-adiabatic behavior of relativistic electrons in the outer radiation belt during the 1 June 2013 geomagnetic storm is studied using data from the Van Allen Probes and THEMIS missions. Analysis of the evolution of the plasma pressure shows that the pressure increased by one order of magnitude during the storm reaching a maximum value towards the end or slightly after the main phase of the storm. At the same time, the location of the maximum pressure moved towards the Earth, reaching the closest distance at L∼3.7. Relativistic electron fluxes show that the location of the peak fluxes also move closer to the Earth reaching the same L as the maximum of plasma pressure. In order to study whether the adiabatic mechanisms are relevant to explain the behavior of relativistic electrons, we analyzed the electron fluxes in the energy range from 1.8 to 4.2 MeV and found that the electron spectra fits well to a power law function. For a fixed L−shell, the power law index is conserved during the pre-storm time, increases during the main phase, and presents little variation again during the recovery phase, but the power law index calculated during the recovery phase is larger than during pre-storm phase. A strong depletion of electron fluxes during the main phase of the storm is also measured, with fluxes returning to the pre-storm level afterwards. The conservation of the slope of the electron spectra during a long time can be considered as an evidence of a dominant contribution of adiabatic processes, as it is difficult to explain this effect as other processes such acceleration and losses of relativistic electrons. An increase of the power law index towards the end or right after the main phase of the storm can be related to the increase of the losses with the electron energy, and/or to the processes of thermalization (relaxation) of the electron distribution functions.

The inner magnetosphere is a very important region to study, as with satellite-based communications increasing day after day, possible disruptions are especially relevant due to the possible consequences in our daily life. It is becoming very important to know how the radiation belts behave, especially during strong geomagnetic activity. The radiation belts response to geomagnetic storms and solar wind conditions is still not fully understood, as relativistic electron fluxes in the outer radiation belt can be depleted, enhanced or not affected following intense activity. Different studies show how these results vary in the face of different events. As one of the main mechanisms affecting the dynamics of the radiation belt are wave-particle interactions between relativistic electrons and ULF waves. In this work we perform a statistical study of the relationship between ULF wave power and relativistic electron fluxes in the outer radiation belt during several geomagnetic storms, by using magnetic field and particle fluxes data measured by the Van Allen Probes between 2012 and 2017. We evaluate the correlation between the changes in flux and the cumulative effect of ULF wave activity during the main and recovery phases of the storms for different position in the outer radiation belt and energy channels. Our results show that there is a good correlation between the presence of ULF waves and the changes in flux during the recovery phase of the storm and that correlations vary as a function of energy. Also, we can see in detail how the ULF power change for the electron flux at different L-shell We expect these results to be relevant for the understanding of the relative role of ULF waves in the enhancements and depletions of energetic electrons in the radiation belts for condition described.

Forecasting relativistic electron fluxes at geostationary Earth orbit (GEO) has been a long-term goal of the scientific community, and significant advances have been made in the past, but the relation to the interior of the radiation belts, that is, to lower L-shells, is still not clear. In this work we have identified 60 relativistic electron enhancement events at GEO to study the radial response of outer belt fluxes and the correlation between the fluxes at GEO and those at lower L-shells. The enhancement events occurred between 1 October 2012 and 31 December 2017 and were identified using Geostationary Operational Environmental Satellite (GOES) 15 >2 MeV fluxes at GEO, which we have used to characterize the radial response of the radiation belt, by comparing to fluxes measured by the Van Allen probes Energetic Particle, Composition and Thermal Plasma Suite Relativistic Electron-Proton Telescope (ECT-REPT) between 2.55.0 and generally similar for L>4.5. Post-enhancement maximum fluxes show a remarkable correlation for all L>4.0 although the magnitude of the pre-existing fluxes on the outer belt plays a significant role and makes the ratio of pre-enhancement to post-enhancement fluxes less predictable in the region 4.0