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Free, publicly-accessible full text available January 1, 2025
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Abstract We analyzed the contribution of electromagnetic ion cyclotron (EMIC) wave driven electron loss to a flux dropout event in September 2017. The evolution of electron phase space density (PSD) through the dropout showed the formation of a radially peaked PSD profile as electrons were lost at high
L* , resembling distributions created by magnetopause shadowing. By comparing 2D Fokker Planck simulations of pitch angle diffusion to the observed change in PSD, we found that theμ andK of electron loss aligned with maximum scattering rates at dropout onset. We conclude that, during this dropout event, EMIC waves produced substantial electron loss. Because pitch angle diffusion occurred on closed drift paths near the last closed drift shell, no radial PSD minimum was observed. Therefore, the radial PSD gradients resembled solely magnetopause shadowing loss, even though the local pitch angle scattering produced electron losses of several orders of magnitude of the PSD. -
Abstract In this study we present AI Prediction of Equatorial Plasma Bubbles (APE), a machine learning model that can accurately predict the Ionospheric Bubble Index (IBI) on the Swarm spacecraft. IBI is a correlation (
R 2) between perturbations in plasma density and the magnetic field, whose source can be Equatorial Plasma Bubbles (EPBs). EPBs have been studied for a number of years, but their day‐to‐day variability has made predicting them a considerable challenge. We build an ensemble machine learning model to predict IBI. We use data from 2014 to 2022 at a resolution of 1s, and transform it from a time‐series into a 6‐dimensional space with a corresponding EPBR 2(0–1) acting as the label. APE performs well across all metrics, exhibiting a skill, association and root mean squared error score of 0.96, 0.98 and 0.08 respectively. The model performs best post‐sunset, in the American/Atlantic sector, around the equinoxes, and when solar activity is high. This is promising because EPBs are most likely to occur during these periods. Shapley values reveal that F10.7 is the most important feature in driving the predictions, whereas latitude is the least. The analysis also examines the relationship between the features, which reveals new insights into EPB climatology. Finally, the selection of the features means that APE could be expanded to forecasting EPBs following additional investigations into their onset. -
Abstract Azimuthal structuring is usually observed within the brightening auroral substorm onset arc; such structure has been linked to the exponential growth of electromagnetic ultralow‐frequency (ULF) waves. We present a case study investigating the timing and frequency dependence of such ULF waves on the ground and in the near‐Earth magnetotail. In the magnetotail, we observe an increase in broadband wave power across the 10‐ to 100‐s period range. On the ground, the arrival times spread from an epicenter. The onset of longer period waves occurs first and propagates fastest in latitude and longitude, while shorter periods appear to be more confined to the onset arc. The travel time from the spacecraft to the ground is inferred to be approximately 1–2 min for ULF wave periods between 15 and 60 s, with transit times of 60 s or less for longer period waves. This difference might be attributed to preferential damping of the shorter period waves, as their amplitude would take longer to rise above background levels. These results have important consequences for constraining the physics of substorm onset processes in the near‐Earth magnetotail and their communication to the ground.
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Abstract We must be able to predict and mitigate against geomagnetically induced current (GIC) effects to minimize socio‐economic impacts. This study employs the space weather modeling framework (SWMF) to model the geomagnetic response over Fennoscandia to the September 7–8, 2017 event. Of key importance to this study is the effects of spatial resolution in terms of regional forecasts and improved GIC modeling results. Therefore, we ran the model at comparatively low, medium, and high spatial resolutions. The virtual magnetometers from each model run are compared with observations from the IMAGE magnetometer network across various latitudes and over regional‐scales. The virtual magnetometer data from the SWMF are coupled with a local ground conductivity model which is used to calculate the geoelectric field and estimate GICs in a Finnish natural gas pipeline. This investigation has lead to several important results in which higher resolution yielded: (1) more realistic amplitudes and timings of GICs, (2) higher amplitude geomagnetic disturbances across latitudes, and (3) increased regional variations in terms of differences between stations. Despite this, substorms remain a significant challenge to surface magnetic field prediction from global magnetohydrodynamic modeling. For example, in the presence of multiple large substorms, the associated large‐amplitude depressions were not captured, which caused the largest model‐data deviations. The results from this work are of key importance to both modelers and space weather operators. Particularly when the goal is to obtain improved regional forecasts of geomagnetic disturbances and/or more realistic estimates of the geoelectric field.
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Abstract We show that a white‐light all‐sky imager can estimate Pedersen conductance with an uncertainty of 3 mho or 40%. Using a series of case studies over a wide range of geomagnetic activity, we compare estimates of Pedersen conductance from the backscatter spectrum of the Poker Flat Incoherent Scatter Radar with auroral intensities. We limit this comparison to an area bounding the radar measurements and within a limited area close to (but off) imager zenith. We confirm a linear relationship between conductance and the square root of auroral intensity predicted from a simple theoretical approximation. Hence, we extend a previous empirical result found for green‐line emissions to the case of white‐light off‐zenith emissions. The difference between the radar conductance and the best‐fit relationship has a mean of −0.76 ± 4.8 mho and a relative mean difference of 21% ± 78%. The uncertainties are reduced to −0.72 ± 3.3 mho and 0% ± 40% by averaging conductance over 10 min, which we attribute to the time that auroral features take to move across the imager field being greater than the 1‐min resolution of the radar data. Our results demonstrate and calibrate the use of Time History of Events and Macroscale Interactions during Substorms all‐sky imagers for estimating Pedersen conductance. This technique allows the extension of estimates of Pedersen conductance from Incoherent Scatter Radars to derive continental‐scale estimates on scales of ~1–10 min and ~100 km2. It thus complements estimates from low‐altitude satellites, satellite auroral imagers, and ground‐based magnetometers.
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Abstract Substorms are a highly variable process, which can occur as an isolated event or as part of a sequence of multiple substorms (compound substorms). In this study we identify how the low‐energy population of the ring current and subsequent energization varies for isolated substorms compared to the first substorm of a compound event. Using observations of H+and O+ions (1 eV to 50 keV) from the Helium Oxygen Proton Electron instrument onboard Van Allen Probe A, we determine the energy content of the ring current in L‐MLT space. We observe that the ring current energy content is significantly enhanced during compound substorms as compared to isolated substorms by ∼20–30%. Furthermore, we observe a significantly larger magnitude of energization (by ∼40–50%) following the onset of compound substorms relative to isolated substorms. Analysis suggests that the differences predominantly arise due to a sustained enhancement in dayside driving associated with compound substorms compared to isolated substorms. The strong solar wind driving prior to onset results in important differences in the time history of the magnetosphere, generating significantly different ring current conditions and responses to substorms. The observations reveal information about the substorm injected population and the transport of the plasma in the inner magnetosphere.