Utilising magnetic field measurements made by the Iridium satellites and by ground magnetometers in North America we calculate the full ionospheric current system and investigate the substorm current wedge. The current estimates are independent of ionospheric conductance, and are based on estimates of the divergence-free (DF) ionospheric current from ground magnetometers and curl-free (CF) ionospheric currents from Iridium. The DF and CF currents are represented using spherical elementary current systems (SECS), derived using a new inversion scheme that ensures the current systems’ spatial scales are consistent. We present 18 substorm events and find a typical substorm current wedge (SCW) in 12 events. Our investigation of these substorms shows that during substorm expansion, equivalent field-aligned currents (EFACs) derived with ground magnetometers are a poor proxy of the actual FAC. We also find that the intensification of the westward electrojet can occur without an intensification of the FACs. We present theoretical investigations that show that the observed deviation between FACs estimated with satellite measurements and ground-based EFACs are consistent with the presence of a strong local enhancement of the ionospheric conductance, similar to the substorm bulge. Such enhancements of the auroral conductance can also change the ionospheric closure of pre-existing FACs such that the ground magnetic field, and in particular the westward electrojet, changes significantly. These results demonstrate that attributing intensification of the westward electrojet to SCW current closure can yield false understanding of the ionospheric and magnetospheric state.

High-Intensity Long-Duration Continuous AE Activity (HILDCAA) intervals are driven by High Speed solar wind Streams (HSSs) during which the rapidly-varying interplanetary magnetic field (IMF) produces high but intermittent dayside reconnection rates. This results in several days of large, quasi-periodic enhancements in the auroral electrojet (AE) index. There has been debate over whether the enhancements in AE are produced by substorms or whether HILDCAAs represent a distinct class of magnetospheric dynamics. We investigate sixteen HILDCAA events using the expanding/contracting polar cap model as a framework to understand the magnetospheric dynamics occurring during HSSs. Each HILDCAA onset shows variations in open magnetic flux, dayside and nightside reconnection rates, the cross-polar cap potential, and AL that are characteristic of substorms. The enhancements in AE are produced by activity in the pre-midnight sector, which is the typical substorm onset region. The periodicities present in the intermittent IMF determine the exact nature of the activity, producing a range of behaviours from a sequence of isolated substorms, through substorms which merge into one-another, to almost continuous geomagnetic activity. The magnitude of magnetic fluctuations, $dB/dt$, in the pre-midnight sector during HSSs is sufficient to produce a significant risk of Geomagnetically Induced Currents, which can be detrimental to power-grids and pipelines.

We show the global dynamics of spatial cross-correlation of Pc2 wave activity can track the evolution of the 2015 St. Patrick’s Day geomagnetic storm for an 8 hour time window around onset. The global spatially coherent response is tracked by forming a dynamical network from 1 second data using the full set of 100+ ground-based magnetometer stations collated by SuperMAG and Intermagnet. The pattern of spatial coherence is then captured by a few network parameters which in turn track the evolution of the storm. At onset IMF B_z>0 and Pc2 power increases, we find a global response with stations being correlated over both local and global distances. Following onset, whilst B_z>0, the network response is confined to the day-side. When IMF B_z<0, there is a strong local response at high latitudes, consistent with the onset of polar cap convection driven by day-side reconnection. The spatially coherent response as revealed by the network grows and is maximal when both SME and SMR peak, consistent with an active electrojet and ring-current. Throughout the storm there is a coherent response both in stations located along lines of constant geomagnetic longitude, between hemispheres, and across magnetic local time. The network does not simply track the average Pc2 wave power, however is characterized by network parameters which track the evolution of the storm. This is a first study to parameterize global Pc2 wave correlation and offers the possibility of statistical studies across multiple events to detailed comparison with, and validation of, space weather models.

The Newcomb-Benford Law (NBL) prescribes the probability distribution of the first digit of variables which explore a broad range under conditions including aggregation. Long-term space weather relevant observations and indices necessarily incorporate changes in the contributing number and types of observing instrumentation over time and we find that this can be detected solely by comparison with the NBL. It detects when upstream solar wind magnetic field OMNI HRO Interplanetary Magnetic Field incorporated new data from WIND and ACE after 1995. NBL comparison can detect underlying changes in geomagnetic indices AE (activity dependent background subtraction) and SME (different station types) that select individual stations showing the largest deflection, but not where station data are averaged, as in the SMR index. As composite indices becomes more widespread across the geosciences, the NBL may provide a generic data flag to indicate changes in the constituent raw data, calibration or sampling method.

The auroral electrojet is traditionally measured remotely with magnetometers on ground or in low Earth orbit (LEO). The sparse distribution of measurements, combined with a vertical distance of some 100 km to ground and typically >300 km to LEO satellites, means that smaller scale sizes can not be detected. Because of this, our understanding of the spatiotemporal characteristics of the electrojet is incomplete. Recent advances in measurement technology give hope of overcoming these limitations by multi-point remote detections of the magnetic field in the mesosphere, very close to the electrojet. We present a prediction of the magnitude of these disturbances, inferred from the spatiotemporal characteristics of magnetic field-aligned currents. We also discuss how Zeeman magnetic field sensors (Yee et al., 2020) onboard the Electrojet Zeeman Imaging Explorer (EZIE) satellites will be used to essentially image the equivalent current at unprecedented spatial resolution. The electrojet imaging is demonstrated by combining carefully simulated measurements with a spherical elementary current representation using a novel inversion scheme.

We use the Space Weather Modeling Framework (SWMF) Geospace configuration to simulate a total of 122 storms from the period 2010-2019. With the focus on the storm main phase, each storm period was run for 54 hours starting from 6 hours prior to the start of the Dst depression. The simulation output of ground magnetic variations were compared with ground magnetometer station data provided by SuperMAG to statistically assess the Geospace model regional magnetic perturbation prediction performance. Our results show that the regional predictions at mid-latitudes are quite accurate, but the high-latitude regional disturbances are still difficult to predict.

Rapid changes of magnetic fields associated with nighttime magnetic perturbation events (MPEs) with amplitudes |ΔB| of hundreds of nT and 5-10 min periods can induce geomagnetically-induced currents (GICs) that can harm technological systems. In this study we compare the occurrence and amplitude of nighttime MPEs with |dB/dt| ≥ 6 nT/s observed during 2015 and 2017 at five stations in Arctic Canada ranging from 75.2° to 64.7° in corrected geomagnetic latitude (MLAT) as functions of magnetic local time (MLT), the SME and SYM/H magnetic indices, and time delay after substorm onsets. Although most MPEs occurred within 30 minutes after a substorm onset, ~10% of those observed at the four lower latitude stations occurred over two hours after the most recent onset. A broad distribution in local time appeared at all 5 stations between 1700 and 0100 MLT, and a narrower distribution appeared at the lower latitude stations between 0200 and 0700 MLT. There was little or no correlation between MPE amplitude and the SYM/H index; most MPEs at all stations occurred for SYM/H values between -40 and 0 nT. SME index values for MPEs observed more than 1 hour after the most recent substorm onset fell in the lower half of the range of SME values for events during substorms, and dipolarizations in synchronous orbit at GOES 13 during these events were weaker or more often nonexistent. These observations suggest that substorms are neither necessary nor sufficient to cause MPEs, and hence predictions of GICs cannot focus solely on substorms.

This study considers 28 geomagnetic storms with Dst $\leq-50$ nT driven by high-speed streams (HSSs) and associated stream interaction regions (SIRs) during 2010-2017. Their impact on ionospheric horizontal and field-aligned currents (FACs) have been investigated using superposed epoch analysis of SuperMAG and AMPERE data, respectively. The zero epoch ($t_0$) was set to the onset of the storm main phase. Storms begin in the SIR with enhanced solar wind density and compressed southward oriented magnetic field. The integrated FAC and equivalent currents maximise 40 and 58 min after $t_0$, respectively, followed by a small peak in the middle of the main phase ($t_0$+4h), and a slightly larger peak just before the Dst minimum ($t_0$+5.3h). The currents are strongly driven by the solar wind, and the correlation between the Akasofu $\varepsilon$ and integrated FAC is $0.90$. The number of substorm onsets maximises near $t_0$. The storms were also separated into two groups based on the solar wind dynamic pressure p_dyn in the vicinity of the SIR. High p_dyn storms reach solar wind velocity maxima earlier and have shorter lead times from the HSS arrival to storm onset compared with low p_dyn events. The high p_dyn events also have sudden storm commencements, stronger solar wind driving and ionospheric response at $t_0$, and are primarily responsible for the first peak in the currents after $t_0$. After $t_0+2$ days, the currents and number of substorm onsets become higher for low compared with high p_dyn events, which may be related to higher solar wind speed.

Estimation of the ionospheric conductance is a crucial step in coupling the magnetosphere & ionosphere (MI). Since the high-latitude ionosphere closes magnetospheric currents, conductance in this region is pivotal to examine & predict MI coupling dynamics, especially during extreme events. In spite of its importance, only recently have impacts of key magnetospheric & ionospheric contributors affecting auroral conductance (e.g., particle distribution, ring current, anomalous heating, etc.) been explored using global models. Addressing these uncertainties require new capabilities in global magnetosphere - ionosphere - thermosphere models, in order to self-consistently obtain the multi-scale, dynamic sources of conductance. This work presents the new MAGNetosphere - Ionosphere - Thermosphere (MAGNIT) auroral conductance model, which delivers the requisite capabilities to fully explore the sources of conductance & their impacts. MAGNIT has been integrated into the Space Weather Modeling Framework to couple dynamically with the BATSRUS magnetohydrodynamic (MHD) model, the Rice Convection Model (RCM) of the ring current, the Ridley Ionosphere Model (RIM) & the Global Ionosphere Thermosphere Model (GITM). This new model is used to address the precise impact of diverse conductance contributors during geomagnetic events. First, the coupled MHD-RIM-MAGNIT model is used to establish diffuse & discrete precipitation using kinetic theory. The key innovation is to include the capability of using distinct particle distribution functions (PDF) in a global model: in this study, we explore precipitation fluxes estimated using isotropic Maxwellian & Kappa PDFs. RCM is then included to investigate the effect of the ring current. Precipitating flux computed on closed field lines by RCM is compared against MAGNIT results, to show that expected results are alike. Lastly, GITM is coupled to study the impact of the ionosphere thermosphere system. Using the MAGNIT model, aforementioned conductance sources are progressively applied in idealized simulations & compared against the OVATION Prime Model. Finally, data-model comparisons against SSUSI, AMPERE & SuperMAG measurements during the March 17, 2013 Storm are shown. Results show remarkable progress of conductance modeling & MI coupling layouts in global models.

A number of interdependent conditions and processes contribute to ionospheric-origin energetic ion outflows. Due to these interdependences and the associated observational challenges, energetic ion outflows remain a poorly understood facet of atmosphere-ionosphere-magnetosphere coupling. Here we demonstrate the relationship between east-west magnetic field fluctuations ($\Delta B_{\textrm{EW}}$) and energetic outflows in the magnetosphere-ionosphere transition region. We use dayside cusp-region FAST satellite observations made at apogee ($\sim$4200-km altitude) near fall equinox and solstices in both hemispheres to derive statistical relationships between ion upflow and ($\Delta B_{\textrm{EW}}$) spectral power as a function of spacecraft-frame frequency bands between 0 and 4 Hz. Identification of ionospheric-origin energetic ion upflows is automated, and the spectral power $P_{EW}$ in each frequency band is obtained via integration of $\Delta B_{\textrm{EW}}$ power spectral density. Derived relationships are of the form $J_{\parallel,i} = J_{0,i} P_{EW}^\gamma$ for upward ion flux $J_{\parallel,i}$ at 130-km altitude. The highest correlation coefficients are obtained for spacecraft-frame frequencies $\sim$0.1–0.5 Hz. Summer solstice and fall equinox observations yield power law indices $\gamma \simeq$ 0.9–1.3 and correlation coefficients $r \geq 0.92$, while winter solstice observations yield $\gamma \simeq$ 0.4–0.8 with $r \gtrsim 0.8$. Mass spectrometer observations reveal that the oxygen/hydrogen ion composition ratio near summer solstice is much greater than the corresponding ratio near winter. These results thus reinforce the importance of ion composition in any outflow model. If observed $\Delta B_{\textrm{EW}}$ variations are purely spatial and not temporal, we show that spacecraft-frame frequencies $\sim$0.1–0.5 Hz correspond to perpendicular spatial scales of several to tens of kilometers.

We investigate sharp changes in magnetic field that can produce Geomagnetically Induced Currents (GICs) which damage pipelines and power grids. We use one-minute cadence SuperMAG observations to find the occurrence distribution of magnetic field “spikes”. Recent studies have determined recurrence statistics for extreme events and charted the local time distribution of spikes; however, their relation to solar activity and conditions in the solar wind is poorly understood. We study spike occurrence during solar cycles 23 and 24, roughly 1995 to 2020. We find three local time hotspots in occurrence: the pre-midnight region associated with substorm onsets, the dawn sector associated with omega band activity, and the pre-noon sector associated with the Kelvin-Helmholtz instability occurring at the magnetopause. Magnetic field perturbations are mainly North-South for substorms and KHI, and East-West for omega bands. Substorm spikes occur at all phases of the solar cycle, but maximise in the declining phase. Omega-band and KHI spikes are confined to solar maximum and the declining phase. Substorm spikes occur during moderate solar wind driving, omega band spikes during strong driving, and KHI spikes during quiet conditions but with high solar wind speed. We show that the shapes of these distributions do not depend on the magnitude of the spikes, so it appears that our results can be extrapolated to extreme events.

The magnetosphere, ionosphere and thermosphere (MIT) act as a coherently integrated system (geospace), driven in part by solar influences and characterized by variability and complexity. Among the most important and yet uncertain aspects of the geospace system is energy and momentum coupling between regions, which is, in part, accomplished by the transfer of charged particles from the magnetosphere to the ionosphere in a process known as particle precipitation, and in the opposite direction by ion outflow. Both processes are inherently multiscale and manifest the variabilities and complexities of the geospace system. Despite the importance of the transfer of particles, existing models are increasingly ill-equipped to provide the specification necessary for the growing demand for geospace now- and forecasts. Due to recent trends in the availability of data, we now face an exciting opportunity to progress particle transfer in geospace through the intersection of traditional approaches and state-of-the-art data-driven sciences. We reveal novel particle transfer models utilizing machine learning (ML), present results from the models, and provide an evaluation of their capabilities including comparisons with observations and the current ’state-of-the-art’ models (e.g., OVATION Prime for particle precipitation and the Gamera-Ionosphere Polar Wind Model for ion outflow). We detail the data wrangling required to utilize the available geospace observations to make progress on the long-standing challenge of particle transfer and place specific emphasis on the discovery possible when ML models are appropriate and robustly interrogated in the context of physical understanding. Our presentation helps illustrate the trends in the application of data science in space science.