John C. Coxon

Geomagnetically Induced Currents (GICs) are a key space weather hazard to ground‐based infrastructure, and can cause mis‐operation or even equipment failure. The solar wind interacts with the Earth's geomagnetic field, causing the magnetic field at ground level to vary with time. This variability causes GICs in grounded, conducting networks via coupling with the subsurface geology. Most studies of the hazard focus on short, high amplitude GICs, but long intervals of lower amplitude GIC can also be problematic, representing a rare but known pathway to equipment damage and power failure. We evaluate an interval in June 2015 where the power network in New Zealand experienced long‐duration, steadily increasing GICs across the South Island. These GICs were up to 20A and lasted approximately 90 min during an otherwise moderate storm interval, while New Zealand was in the dawn sector. We investigate the magnetospheric cause, attributing them to a possible diversion of the ring current into the ionosphere. We test the ability of modeling and proxies to capture this hazard, finding that it can be recreated, but is dependent upon the regional geology, and requires detailed knowledge of the network configuration. We model two hypothetical examples, (a) where the magnetic perturbation is larger and (b) where a different mid‐latitude location is exposed, finding that larger currents are possible and that the vulnerability is not unique to New Zealand. Our case study highlights the limitations of geomagnetic indices, which we show to be vulnerable to influence from such rare mid‐latitude phenomena.

A geomagnetic storm is a significant and prolonged disturbance of the Earth's magnetic field caused by an enhancement of the ring current. Storms are typically identified using magnetic field measurements from multiple ground‐based magnetometers, and the corresponding dates and times of multiple storms can be collated to form a storm list. There is currently no quantitative definition of a geomagnetic storm, so the contents of a particular storm list are highly dependent on the identification criteria that were used to generate it. The different definitions and identification methods can cause the physical properties of storms in different lists to vary, and these variations may be significant. We take several storm lists with different identification methods, and we compare the probability distributions of a range of solar wind variables and geomagnetic indices between those lists. We also compare the temporal behavior of the storm lists, and discuss how the differences may affect the way geomagnetic storms are defined and studied. We find that changing the definition of the quiet time has a direct impact on the start and end times of the storms, and this can result in a storm being significantly shorter when defined using a more negative quiet time definition. We also find that changes to the threshold value of minimum magnetic disturbance have a greater impact on the properties of the identified storms than changes to the quiet time definition. We provide recommendations on which storm lists to use for different scenarios.

We quantify the asymmetry between the Birkeland currents (also known as field‐aligned currents) in the Northern and Southern Hemispheres. We use data from the Active Magnetosphere and Planetary Electrodynamics Response Experiment (AMPERE) in 2010–2024 inclusive for both hemispheres, compute the observed asymmetry, and then subtract the modeled asymmetry. We use Block 1 data (based on the original Iridium constellation) and NEXT data (based on Iridium‐NEXT spacecraft), and compare the effect of changing the data processing pipeline on the latter (yielding NEXT v1 and NEXT v2 subdatasets). We find that the asymmetry is significantly smaller in NEXT v2 data than in Block 1 or NEXT v1 data . The previously reported asymmetry seen in Swarm is , indicating that the new data processing pipeline goes from approximately twice the Swarm asymmetry to approximately half. This may indicate that the new AMPERE processing pipeline overestimates the amount of current flowing in the Southern Hemisphere, whereas the old one underestimated this quantity. We use frequency analysis to find that the asymmetry varies with frequencies corresponding to seasonal effects, CME occurrence, Iridium constellation orbit precession, and geomagnetic storms. We find a correlation with IMF , indicating that more current flows in the Northern Hemisphere during positive and vice versa. We find that Block 1 data in 2010–2017 shows correlations with AL, AU, , PCN, and PCS index which indicate that the asymmetry is less pronounced when Dungey Cycle convection is stronger, but NEXT data in 2019–2024 do not show this.

The Earth’s upper atmosphere is highly sensitive to solar activity and the solar wind-magnetosphere interaction. Magnetospheric current systems close through the ionosphere, where ion-neutral collisions and enhanced energetic particle precipitation can significantly modulate the spatial and temporal variability of the atmosphere's outer extent. Unlike the many isolated in-situ measurements conducted by previous space missions, distributed observations of neutral particles, plasma, and magnetic fields by a tetrahedron of micro-satellites, combined with precise tracking of satellite orbital dynamics, provide the global perspective needed to disentangle the complex transfers of energy and momentum through the tightly coupled magnetosphere-ionosphere-thermosphere system.In this presentation, we outline the ROARS F3 mission architecture. This mission seeks to obtain the first full curlometer magnetic field and energetic particle precipitation measurements in low Earth orbit (LEO), alongside concurrent measurements of the ambient plasma and neutral populations across a range of altitudes, latitudes, and longitudes. The measurement strategy is designed to resolve and characterise the energy and momentum entering the upper atmosphere, the multi-scale pathways through which these are redistributed, and the feedback mechanisms coupling back to the broader geospace environment. A comprehensive ground segment will simultaneously provide context by relating information on D- and E-region dynamics to the in-situ measurements.

Large Geomagnetically Induced Currents (GICs) are a key space weather hazard to ground-based infrastructure. These GICs act as a DC offset in typically AC power networks and when very large can cause mis-operation or even equipment failure. The dynamic solar wind interacts with the Earth’s geomagnetic field, causing the magnetic field as measured on the ground to vary with time. Via coupling with the geology of the solid Earth, this variability causes GICs to be created in grounded conducting networks. Most studies of the GIC hazard focus on short, fast changes of the geomagnetic field, for example large “spikes” in the one-minute rate of change of the horizontal geomagnetic field.However, recent work has reminded researchers that long intervals of lower (relative) GIC can also be problematic, and represents a known pathway to equipment damage and long-lasting power failure. In this study we evaluate a curious interval where the power network in New Zealand experienced long duration, steadily increasing GICs across the South Island. These GICs were up to 20 A and lasted approximately 90 minutes, manifesting in the mid-latitude dawn sector during an otherwise moderate geomagnetic storm. We investigate the magnetospheric cause of the ground observations, and the ability of contemporary modelling techniques to capture this facet of the GIC hazard to ground-based infrastructure. Further, this case study highlights the limitations of geomagnetic indices, which we show to be vulnerable to contamination from such rare mid-latitude phenomena.

We employ ground magnetometers in North America, Greenland, and Antarctica and use the Spherical Elementary Current (SEC) technique in order to investigate the currents flowing between January 2015 and December 2016. We convert the measurements into altitude-adjusted corrected geomagnetic (AACGM) coordinates to allow us to investigate the hemispheric asymmetries between conjugate points. There are data gaps during the Southern Hemisphere winters due to difficulties of making ground-based observations at these times. We subset the measurements to control for the different spatial extents of the data in either hemisphere, and then average spatially and temporally so that we can compute the asymmetry. We contextualise the asymmetry in terms of AMPERE, Swarm, and DMSP-observed asymmetries, and discuss what this implies for the ionospheric conductance in either hemisphere.

A specific aspect of space weather that remains poorly understood is the exchange of information from space to the ground through the ionosphere. A central component of this process involves understanding how current systems such as field-aligned currents transfer energy and momentum between the magnetosphere and the ionosphere. However, the non-linear behaviour of these current systems poses significant challenges for identifying the drivers of ionospheric currents and understanding the inner dynamics of the ionosphere itself.To tackle these complexities and their effects on the ground, we adopt a data-driven approach using space-based observations from the Active Magnetosphere and Planetary Electrodynamics Response Experiment (AMPERE). Specifically, we focus on gaining insights into what drives these current systems by finding underlying statistical patterns (dominant modes) in the data using unsupervised machine learning methods. We employ techniques such as β - Variational Autoencoders (β-VAEs), which have been proven useful in identifying patterns in unlabelled observational data.We extract dominant modes and connect them to physical drivers of the system with a two-step approach. First, we quantify model performance using a physically motivated goodness-of-fit metric to ensure that the learned model reconstructions capture the essential dynamics in the current system. Second, we analyse the model’s latent space, representing a compressed representation of the high dimensional input data. We then analyse the latent space and connect the influence of the individual latents to physical drivers of the system through the usage of the OMNI dataset. This approach enables a systematic interpretation of the model’s internal representations in terms of underlying physical processes.

Past measurements of Saturn's upper atmosphere have allowed only a broad scale view of the temperature and ion density structures within the auroral region. However, Saturn's auroral currents include a planetary period current component that is produced by neutral atmospheric flows. In order to understand the ultimate source for Saturn's planetary period aurora, we need vastly improved measurements of Saturn's auroral region. Here, using the first James Webb Space Telescope (JWST)/NIRSpec observations of Saturn, we reveal incredibly high spatial resolution (<500 km/pixel) views of Saturn's auroral region for the first time. This provides at least an order‐of‐magnitude improvement in our measurement of the temperature and column density structure within Saturn's auroral region. It reveals an auroral region at the time of observation that is highly controlled by the planetary period currents, resulting in a series of upwards and downward currents from the pole to below the main auroral region. These are paired with reflected and inverted regions of heating and cooling across the auroral region. These patterns match surprisingly well to past model predictions, suggesting both the changes in column density and temperature are the direct result of localized heating positioned over the region of strongest particle precipitation. This strongly indicates that the planetary period currents are produced by a feedback loop, powered by the energy of magnetospherically generated aurora, preferentially heating one side of the auroral region.

A polar cap patch was observed to exit the polar cap to become a blob as it entered the auroral oval on the nightside of the Earth under moderately quiet geomagnetic conditions ( K _p = 3−). Auroral particle precipitation led to an increase in the electron density of the blob. Blobs can be formed by multiple formation mechanisms, including transportation effects and auroral precipitation. This blob exhibited characteristics which fit within the definitions of both boundary and auroral blobs. This study therefore presents direct observations of multiple blob formation mechanisms acting simultaneously on the same plasma. These observations were made using the European Incoherent SCATter (EISCAT) radars. The polar cap patch was first observed in the EISCAT Svalbard Radar (ESR) and is later observed as a blob by the EISCAT VHF and UHF radars as the plasma moves antisunward from the polar cap and into the auroral oval. The Super Dual Auroral Radar Network radars showed that the plasma was moving antisunward and data from the Active Magnetosphere and Planetary Electrodynamics Response Experiment provided supporting evidence for the location of the auroral boundary. In addition, Global Navigation Satellite Systems (GNSS) receivers monitored the scintillation effects that were associated with large‐scale structures. Overall, very low scintillation was observed by these receivers, suggesting that small‐scale structures (<365 m for GNSS L1 frequencies) did not develop significantly during this period.

A geomagnetic storm is a significant and prolonged disturbance of the Earth’s magnetic field caused by an enhancement of the ring current. Storms are typically identified using magnetic field measurements from multiple ground-based magnetometers, and the corresponding dates and times of multiple storms can be collated to form a storm list. There is currently no quantitative definition of a geomagnetic storm, so the contents of a particular storm list are highly dependent on the identification criteria that were used to generate it. The different definitions and identification methods can cause the physical properties of storms in different lists to vary, and these variations may be significant. We take several storm lists with different identification methods, and we compare the probability distributions of a range of solar wind variables and geomagnetic indices between those lists. We also compare the temporal behaviour of the storm lists, and discuss how the differences may affect the way geomagnetic storms are defined and studied. We find that changing the definition of the quiet time has a direct impact on the start and end times of the storms, and this can result in a storm being significantly shorter when defined using a more negative quiet time definition. We also find that changes to the threshold value of minimum magnetic disturbance have a greater impact on the properties of the identified storms than changes to the quiet time definition. We provide recommendations on which storm lists to use for different scenarios.