Earth is surrounded by two highly dynamic concentric belts of particle radiation. The outer radiation belt exhibits coherent variability at solar-cycle, seasonal, and 27-day (Carrington) timescales. While the solar-cycle and Carrington variations have been attributed to the recurrence of coronal hole solar wind, the seasonal variation has long been explained by local geometric effects that modulate the solar wind-magnetosphere coupling. Here, we challenge this paradigm by showing that the periodic recurrence of coronal hole solar wind likewise drives the seasonal variation. We further demonstrate that the Alfvénic nature of this solar wind is responsible for the observed electron flux enhancement in the outer radiation belt. These findings provide a unifying framework linking solar magnetic topology, solar wind properties, and magnetospheric dynamics across multiple timescales at Earth and beyond.

The physical trigger of substorm onset remains one of the key unresolved problems in magnetospheric physics. Understanding how, when, and why stored energy in Earth’s magnetotail is explosively released is central to space-weather science. To identify the instability responsible for detonation, recent studies have focused on the earliest auroral signatures of onset—small-scale, quasi-periodic structures known as auroral beads. Previous work has linked these beads to plasma instabilities and to magnetotail dynamics through kinetic Alfvén waves.To further understand the substorm onset mechanism, we use new measurements from a narrow-field, high-cadence auroral imager. By extending the Kalmoni et al. (2018) methodology, we track the temporal evolution and dispersion characteristics of “mini beads”, in effect beads-within-beads. Our analysis shows that all types of beads move in the same eastward direction but that mini beads precede the larger beads by at least one minute. However, in contrast to larger-scale beads, mini beads obey different dispersion relations, suggesting that mini beads arise from a distinct physical process and represent an earlier or new stage of the instability development leading to substorm onset. This means that we need to understand the near-Earth transition region on multiple scales far earlier than currently thought, challenging all current substorm onset paradigms. We discuss the implications of this analysis for determining the role of multi-scale physical processes in substorm onset for multi-spacecraft missions such as Plasma Observatory.

Abstract Quasilinear diffusion coefficients can be used to model the response of charged particles to resonant wave‐particle interactions. The calculation of these coefficients is sufficiently complicated and arduous to render it prohibitive to many potential users, because of the expense in time spent developing the code. The PIRAN software package (”Particles In ResonANce”) is written using Python, and allows the user to calculate local and bounce‐averaged relativistic diffusion coefficients in energy and pitch‐angle space via the two main current proposed methods in the literature. The code is predominantly based upon the formalisms and methods presented in Glauert and Horne (2005, https://doi.org/10.1029/2004JA010851) and Cunningham (2023, https://doi.org/10.1029/10.1029/2023JA031703). We solve for diffusion coefficients using exact relativistic formulae. We use Gaussian spectra in wave frequency and in tangent of the wave normal angle and solve the full cold‐plasma dispersion relation. At present the code supports fully tested calculations for electron diffusion coefficients based on whistler‐mode waves in a fully ionized proton‐electron cold plasma. However the codebase architecture is built such that future developments to include other wave modes and other plasma compositions should involve incremental additions. The initial release of PIRAN may not have the same number of features as some other numerical codes, but is has the advantages of being a fully open‐source diffusion coefficient code that: (a) supports calculation of both local and bounce‐averaged diffusion coefficients via both of the two proposed methods; (b) is written fully in Python; (c) has detailed user pages, commit history and changelog on GitHub.

Space weather describes the dynamic conditions in near-Earth space, mostly driven by the variable interaction between the continuous flow of the solar wind and the Earth’s magnetic field. Extreme space weather has the potential to disrupt or damage key infrastructure on which we rely, for example through the generation of large, anomalous Geomagnetically Induced Currents (GICs) in power networks and transformers. Accurately forecasting a risk of large GICs would enable key actions to be taken to mitigate their impact.Given the sparsity of direct GIC measurements, and their inherent specificity to the contemporaneous network properties and configuration, we turn to forecasting the driving factor: the changing ground magnetic field (R). In this talk we discuss a recent model developed to forecast whether the rate of change of the ground magnetic field (R) will exceed specific, high thresholds in the United Kingdom. The model uses a common space weather forecasting framework: an interval of data from the upstream solar wind is used to make a prediction as to future conditions at the Earth. We will use this model as an example to discuss forecasting performance, particularly with respect to different magnetospheric driving and processes. We demonstrate the use of techniques such as SHAP (Shapley Additive exPlanations) to investigate how and why the model is making the predictions that it does. What physical processes can this model set up capture? Where do we need to go in the future?

Abstract Atmospheric neutral density is a crucial component to accurately predict and track the motion of satellites. During periods of elevated solar and geomagnetic activity atmospheric neutral density becomes highly variable and dynamic. This variability and enhanced dynamics make it difficult to accurately model neutral density leading to increased errors which propagate from neutral density models through to orbit propagation models. In this paper we investigate the dynamics of neutral density during geomagnetic storms. We use a combination of solar and geomagnetic variables to develop three Random Forest machine learning models of neutral density. These models are based on (a) slow solar indices, (b) high cadence solar irradiance, and (c) combined high‐cadence solar irradiance and geomagnetic indices. Each model is validated using an out‐of‐sample data set using analysis of residuals and typical metrics. During quiet‐times, all three models perform well; however, during geomagnetic storms, the combined high cadence solar iradiance/geomagnetic model performs significantly better than the models based solely on solar activity. The combined model capturing an additional 10% in the variability of density and having an error up to six times smaller during geomagnetic storms then the solar models. Overall, this work demonstrates the importance of including geomagnetic activity in the modeling of atmospheric density and serves as a proof of concept for using machine learning algorithms to model, and in the future forecast atmospheric density for operational use.

We have constructed a new high-resolution auroral imager, called Embla, to simultaneously measure energy and flux of auroral precipitation, neutral temperature, and electric fields in fine scale aurora. Embla is designed primarily for studies of auroral electrodynamics, substorm onset, and neutral heating by aurora. The instrument has recently been installed at Skibotn, Norway, very close to the EISCAT_3D radar transmitter site, and we expect the combination of radar and optical observations to enable better measurements for our science than either instrument can provide alone. Embla builds on work done using the Auroral Structure and Kinetics (ASK) instrument, which has been stationed at the EISCAT Svalbard Radar since 2007. Embla has a spatial resolution in the E-region of ~30 m and a planned temporal resolution of at least 32 frames per second, allowing us to resolve the fine-scale structure and rapid dynamics of auroral features. It consists of 4 co-aligned imagers with identical 9 degree fields of view centred on magnetic zenith. Each imager is equipped with a different narrow passband interference filter, targetting emissions in N2 1P (2 imagers), OI 777.4 nm, and O+ 2P. The combination of N2 1P and OI 777.4 nm observations allows us to image the characteristic energy and flux of auroral electron precipitation. The O+ 2P emission has a long lifetime of 5 s, providing a means to observe ion drift perpendicular to the magnetic field and therefore a way to determine ionospheric electric fields at very high cadence in localised regions around the aurora. Finally, by combining observations from two of the imagers in separate regions of the N2 1P band we can image the neutral temperature at the altitude of the auroral emission. The simultaneous measurement of these properties of the aurora and ionosphere will allow us to investigate auroral electrodynamics in detail.

Van Allen radiation belt electron dynamics are governed by a multitude of physical processes that can simultaneously drive acceleration, transport and loss. However, each individual process can be linked to a specific energy-dependent pitch angle distribution (PAD). We employ a new, unsupervised machine learning technique on 7-year of Van Allen Probe Relativistic Electron-Proton Telescope data and discover that six PADs successfully describe 93% of outer belt relativistic electrons, two each of: pancake, butterfly, and flattop. We investigate the occurrence and storm-time evolution of each PAD through 45 geomagnetic storms. We find new populations of PADs, including: "shadowing-like" and wave-particle interaction signatures at low-L, and radial diffusion and substorm injections at higher-L, as well as determining that wave-particle interaction dominated PADs are swamped by radial diffusion processes through geomagnetic storms. Our results clearly demonstrate that PAD characterization is a key component of understanding Van Allen radiation belt electron dynamics. Plain Language Summary The population of relativistic electrons that occupy the Van Allen radiation belts are influenced by processes that occur within the near-Earth environment. These processes can result in the acceleration, transport and loss of electrons at different energies and pitch angles. Using a new, unsupervised machine learning technique on electron measurements from the entire Van Allen Probe mission, we classify similar populations of relativistic electron pitch angle distributions (PADs). Six different PAD shapes were identified, each with fundamentally different characteristics as a result of different physical processes. We investigate the occurrence and evolution of each PAD type through 45 geomagnetic storms and find clear evidence that PAD characterization is a key component of understanding Van Allen radiation belt behavior.

Space weather refers to both the dynamic conditions in interplanetary space driven by the Sun and its subsequent impact on the near-Earth environment. Space weather can disrupt or destroy ground- and space-based infrastructure. Although not fully understood, its significance is growing rapidly with our increasing dependence on modern technology.

Magnetic reconnection is the key to explosive phenomena in the universe. The flux rope is crucial in three-dimensional magnetic reconnection theory and is commonly considered to be generated by secondary tearing mode instability. Here we show that the parallel electron flow moving toward the reconnection diffusion region can spontaneously form flux ropes. The electron flows form parallel current tubes in the separatrix region where the observational parameters suggest the tearing and Kelvin-Helmholtz instabilities are suppressed. The spontaneously formed flux ropes could indicate the importance of electron dynamics in a three-dimensional reconnection region.

Magnetic reconnection is a critically important process in defining the dynamics and energy transport within plasma environments. In near-Earth space we may track where and when reconnection occurs by identifying associated coherent magnetic structures. On a global scale these structures facilitate the flow of mass and magnetic flux into, within, and out of the magnetospheric system, whilst contributing to local plasma heating. In the Earth's magnetotail there are two similar structures we identify in this work: magnetic flux ropes and loops. We present a robust, automated and model independent method by which encounters with such structures may be identified using the Magnetospheric Multiscale (MMS) mission. The magnetic structures are first identified through their magnetic field signatures at a single spacecraft (MMS1), including checks on the local minimum variance coordinate system. Next, the local curvature of the magnetic field is evaluated with all four MMS spacecraft. Finally, the plasma conditions are checked to ensure that the interpretation is fully self-consistent. We evaluate the data obtained by MMS between 2017 and 2022. In total we find 181 self-consistent magnetic flux ropes and 263 magnetic loops, which fit an exponentially decaying size distribution with a scale size comparable to the ion gyroradius (similar to 0.23 RE/1,400 km). If we remove the requirements on the plasma properties of the structure, we locate 648 potential magnetic flux ropes and 1,073 magnetic loops. The magnetic structures are preferentially observed in the pre-midnight region of the magnetotail, with most identifications occurring beyond 20 RE. All catalogs are provided to the community. Automated, model independent method presented to identify ion-scale magnetotail magnetic flux ropes and loop-like plasmoids We provide comprehensive catalogs of magnetotail structures observed by MMS between 2017 and 2022 (Smith et al., 2023) Structures show a strong dawn-dusk asymmetry: predominantly observed pre-midnight, and follow an exponentially decaying size distribution

We suggest that the next era of Heliophysics should focus on the Sun-Heliosphere and Geospace as each a system-of-systems, and recommend a coordinated, deliberate, worldwide scientific effort to answer long-standing questions that will remain unanswered without a unified program. Many of the biggest unanswered science questions that remain across Heliophysics center around the interconnectivity of the different physical systems and the role of mesoscale dynamics in modulating, regulating, and controlling that interconnected behavior. Heliophysics has made key progress understanding both the large-scale dynamics and the microphysical processes that occur in these dynamic systems. Such understanding grew out of a systematic approach to study both limits of the system, from global, with the coordinated missions of the International Solar Terrestrial Physics (ISTP) program, to micro, with largely uncoordinated (albeit coincident) missions such as Cluster, Time History of Events and Macroscale Interactions during Substorms (THEMIS), Van Allen Probes, Magnetospheric Multiscale (MMS), Parker Solar Probe, and Solar Orbiter. We suggest that the international Heliophysics community should embark on a grand program to study these system-of-systems holistically, with coordinated, multipoint measurements. We particularly recommend an emphasis on resolving the mesoscale dynamics that links micro to global, and a whole-of-science approach that includes ground-based measurements and advanced numerical modeling. In effect, we propose a mesoscale ISTP type program that would consist of a system of Great Observatories capable of revealing the connections among systems from the solar interior to the top of Earth's atmosphere. The paradigm and specific approaches outlined in this paper could serve as a strategic imperative and overarching theme that binds our Solar and Space Physics communities together under a common scientific objective. By its very nature, the type of program we argue for would be large, with several coordinated elements, and international in scope. It would include space-borne missions and coordinated ground-based observatories, artificial intelligence/machine learning (AI/ML) methods of analyzing large and complex datasets, and next-generation numerical modeling. The need to coordinate and integrate these different elements is independent of any specific mission implementation. Hence, we suggest the Heliophysics community organize around an ISTP-type program, ISTPNext, with associated Heliophysics "Great Observatories".

Critically important phenomena in Earth's magnetosphere often occur briefly, or in small spatial regions. These processes are sampled with orbiting spacecraft or by fixed ground observatories and so rarely appear in data. Identifying such intervals can be an incredibly time consuming task. We apply a novel, powerful method by which two dimensional data can be automatically processed and embeddings created that contain key features of the data. The distance between embedding vectors serves as a measure of similarity. We apply the state-of-the-art method to two example datasets: MMS electron velocity distributions and auroral all sky images. We show that the technique creates embeddings that group together visually similar observations. When provided with novel example images the method correctly identifies similar intervals: when provided with an electron distribution sampled during an encounter with an electron diffusion region the method recovers similar distributions obtained during two other known diffusion region encounters. Similarly, when provided with an interesting auroral structure the method highlights the same structure observed from an adjacent location and at other close time intervals. The method promises to be a useful tool to expand interesting case studies to multiple events, without requiring manual data labeling. Further, the models could be fine-tuned with relatively small set of labeled example data to perform tasks such as classification. The embeddings can also be used as input to deep learning models, providing a key intermediary step-capturing the key features within the data. Space plasma physics missions and observatories often collect large datasets. These data do not come with interpretation as to what is happening at each time interval, this must be applied manually by experts. This is a very time consuming process and can inhibit authors from studying interesting processes. In this work we showcase a ground breaking technique that can process huge quantities of data automatically to produce encoded versions of the original data. By comparing the encoded versions we can find similar observations without needing to manually search through the data. The method works exceptionally with rare events, finding two of only a few examples in several thousand images. Method creates encoded representations from unlabeled two dimensional data, small intra-embedding distance can indicate similar examples When provided with an electron distribution from a known diffusion region encounter, the method successfully finds other known examples When given an all sky image of an auroral arc, the method locates an image of the same arc from an adjacent location

The generation and propagation of Ultra Low Frequency (ULF) waves are intrinsically coupled to the cold plasma population in the terrestrial magnetosphere. During geomagnetic storms, extreme reconfigurations of the cold plasma creates a complex and dynamic system that drastically modifies this coupling. The extent and manner in which this coupling is affected remains an open question. In this report, we assess the coupling between ULF waves and cold plasmaspheric plumes during geomagnetic storms, and investigate the implications for ULF wave-driven radial transport of the outer radiation belt population. We present a series of event studies of Van Allen Probes observations. For each event, we use inferred measurements of the cold plasma density during plume crossings, in combination with magnetic and electric field observations of ULF waves. The event studies show very different, and at times contrasting, wave behaviour. This includes events where ULF waves appear to be spatially confined within plume structures. Initial estimates show that the localised patches of ULF wave power have significant implications for radial diffusion processes, and highlights the need for caution in estimating radial diffusion coefficients. We suggest that the cold plasma dynamics is an important source of uncertainty in radial diffusion models, and understanding cold plasma-ULF wave coupling is a critical area of future investigations.

AbstractChanges in the Earth's geomagnetic field induce geoelectric fields in the solid Earth. These electric fields drive Geomagnetically Induced Currents (GICs) in grounded, conducting infrastructure. These GICs can damage or degrade equipment if they are sufficiently intense—understanding and forecasting them is of critical importance. One of the key magnetospheric phenomena are Sudden Commencements (SCs). To examine the potential impact of SCs we evaluate the correlation between the measured maximum GICs and rate of change of the magnetic field (H′) in 75 power grid transformers across New Zealand between 2001 and 2020. The maximum observed H′ and GIC correlate well, with correlation coefficients (r2) around 0.7. We investigate the gradient of the relationship between H′ and GIC, finding a hot spot close to Dunedin: where a given H′ will drive the largest relative current (0.5 A nT−1 min). We observe strong intralocation variability, with the gradients varying by a factor of two or more at adjacent transformers. We find that GICs are (on average) greater if they are related to: (a) Storm Sudden Commencements (SSCs; 27% larger than Sudden Impulses, SIs); (b) SCs while New Zealand is on the dayside of the Earth (27% larger than the nightside); and (c) SCs with a predominantly East‐West magnetic field change (14% larger than North‐South equivalents). These results are attributed to the geology of New Zealand and the geometry of the power network. We extrapolate to find that transformers near Dunedin would see 2000 A or more during a theoretical extreme SC (H′ = 4000 nT min−1).

The sequence of events associated with the triggering of energy release during substorm expansion phase onset is still not well‐understood. Oberhagemann and Mann (2020b, https://doi.org/10.1029/2019gl085271) proposed a new substorm onset mechanism, where the transition toward parallel proton pressure anisotropy during tail stretching in the late growth phase could trigger a pressure anisotropic ballooning instability. Here we examine the evolution of energetic proton parallel pressure anisotropy at geosynchronous altitudes, seeking evidence in support of the proposed substorm onset mechanism. We use the Geostationary Operational Environment Satellite (GOES) proton flux and magnetometer data combined with substorm onset indicators derived from ground‐based magnetometers. Superposed epoch analysis of substorm onset times for 2014 using the isolated substorm list (Ohtani & Gjerloev, 2020, https://doi.org/10.1029/2020ja027902) clearly shows signatures of energetic proton parallel pressure anisotropy immediately before substorm onset, potentially supportive of the Oberhagemann and Mann theory.

AbstractGeomagnetically Induced Currents (GICs) are a severe space weather hazard, driven through coupling between the solar wind and magnetosphere. GICs are rarely measured directly, instead the ground magnetic field variability is often used as a proxy. Recently space weather models have been developed to forecast whether the magnetic field variability (R) will exceed specific, extreme thresholds. We test an example machine learning‐based model developed for the northern United Kingdom. We evaluate its performance (discriminative skill and calibration) as a function of magnetospheric state, solar wind input and magnetic local time. We find that the model's performance is highest during active conditions, for example, geomagnetic storms, and lowest during isolated substorms and “quiet” intervals, despite these conditions dominating the training data set. Correspondingly, the performance is high when the solar wind conditions are elevated (i.e., high velocity, large total magnetic field strength, and the interplanetary magnetic field oriented southward), and at a minimum when the north‐south component of the magnetic field is highly variable or around zero. Regarding magnetic local time, performance is highest within the dusk and night sectors, and lowest during the day. The model appears to capture multiple modes of magnetospheric activity, including substorms and viscous interactions, but poorly predicts impulsive phenomena (i.e., storm sudden commencements) and longer timescale coupling processes. Future models of mid‐latitude magnetic field variability will need to effectively use longer time intervals of unpropagated (i.e., observations from L1) solar wind to more completely describe the magnetospheric conditions and response.

IntroductionIn this study, we use 7 years (2012–2019) of pitch angle resolved electron flux measurements from Van Allen Probe-B spacecraft to study the variation of near-equatorial pitch angle distributions (PADs) of outer radiation belt (L ≥ 3) relativistic electrons (E ≥ 0.5 MeV) with different levels of geomagnetic activity.MethodsWe calculate a pitch angle anisotropy index (PAI) to categorize the PADs into three types: pancake, PAI ≥ 1.05; butterfly, PAI ≤ 0.95; and flattop, 0.95 < PAI < 1.05.Results and DiscussionOur statistical results show that L shells ≥ 5 are dominated by pancake PADs on the dayside (9 < MLT < 15), butterfly PADs on the nightside (21 < MLT < 3), and flattop PADs in the dawn (3 < MLT < 9) and dusk (15 < MLT < 21) sectors, across almost all relativistic energies. In the inner L shells, the pancake and flattop PADs exhibit dependence on both L-shell and energy, with the occurrence rate increasing with decreasing L and increasing energy. For the butterfly PADs, we discovered a second population of low-L butterflies that are present at almost all local times. When the variation of electron PAI is compared with solar wind dynamic pressure Pdyn and geomagnetic indices SYM-H and AL, Pdyn is found to be the dominant parameter in driving the outer radiation belt pitch angle anisotropy. During periods of enhanced Pdyn, pancake PADs on the dayside become more 90°-peaked, butterfly PADs on the nightside exhibit enhanced flux dips around 90° pitch angle along with an enhanced azimuthal and radial extent, and flattop PADs turn into either pancake or butterfly PADs.

Magnetic reconnection and current disruption are two key processes in driving energy conversion and dissipation in planetary magnetospheres. At the Earth, the two processes usually occur at different locations: the current disruption process occurs more frequently in the near-Earth magnetotail ∼10 R _E , while the magnetotail reconnection process is expected to take place in the more distant region where the current sheet is thinner. Occasionally, under very intense solar wind perturbations, reconnection could be located closer to the Earth where current disruption processes usually operate. But it is unclear what the situation is at giant planets, in which the plasma environment is very different from the Earth. In this study, we investigate a middle-Jupiter reconnection event at ∼43 R _J . During the event, the inferred integrated cross-field currents were substantially reduced, which we argue is a signature of current disruption. In this case, we suggest that magnetic reconnection could be colocated with a current disruption process in the Jovian magnetosphere, which is roughly analogous to the situation in the extremely perturbed Earth’s magnetosphere.

Plasma flow vorticity is ubiquitous in space and plays a key role in material mixing and energy transfer. The five THEMIS satellites orbiting in different regions of the terrestrial magnetotail provide an unprecedented opportunity to study the temporal–spatial evolution characteristics of a plasma flow vortex on large spatial scales. We present an analysis of the flow vorticity that occurred between 05:50 and 06:30 UT on 2009 March 23 during a quiet time of the terrestrial magnetotail. The uneven distributions of the density and temperature of the vortex observed by the three near-Earth satellites (THA, THD, and THE) indicate that the plasma in the solar wind gradually mixed into the near-Earth magnetotail through a series of nonequilibrium processes. Both the flow vortices and dipolarization observed by the three near-Earth satellites were about 10 minutes earlier than those observed by the two satellites (THB and THC) in the mid-magnetotail. Further analysis of the relationship between vorticity, field-aligned current, and energy transport reveals that the flow vortex interacted with the surrounding plasma during its tailward propagation. The field-aligned current related to the vorticity could only generate a pseudo-breakup of the aurora in the ionosphere. Thus, we speculate that this flow vortex mainly transports mass and energy from the solar wind to the near-magnetotail, propagates tailward to the mid-magnetotail, and heats the encountered plasma by dissipating its bulk flow and dominant thermal energy. These results shed light on the mass mixing, energy transport, and dissipation of the plasma flow vortex during quiet levels of geomagnetic activity on large spatial scales.