Mervyn. P. Freeman

Three dimensional radiation belt models like the BAS radiation belt model and the VERB code assume that trapped electrons within the model space remain at a fixed kinetic energy in the absence of wave-particle interactions. However, it is also accepted that at lower energies convection due to the electric field can significantly alter the trajectories and kinetic energy of trapped electrons. Using coordinates of electric potential, magnetic field strength, and the modified second invariant (UBK), we present a mathematically simple approach to describing the full phase space of possible particle trajectories within given magnetic and electric field models. We further demonstrate that it can be used to determine the change in kinetic energy around any particle orbit, and that changing trajectories and kinetic energies can have a significant effect on satellite measurements of energy spectra.

The enhanced variation of the magnetic field during severe to extreme geomagnetic storms induces a large geoelectric field in the subsurface. Grounded infrastructure can be susceptible to geomagnetically induced currents (GICs) during these events. Modeling the effect in real‐time and forecasting the magnitude of GICs are important for allowing operators of critical infrastructure to make informed decisions on potential impacts. As part of the UK‐funded Space Weather Innovation, Measurement, Modeling and Risk (SWIMMR) program, we implemented nine research‐level models into operational codes capable of running consistently and robustly to produce estimates of GICs in the Great Britain high voltage power transmission network, the high pressure gas pipeline network and the railway network. To improve magnetic coverage and geoelectric field modeling accuracy, three new variometer sites were installed in the UK and a 3 year campaign of magnetotelluric measurements at 53 sites was undertaken. The models rely on real‐time ground observatory data and solar wind data from satellites at the L1 Lagrange point. A mixture of empirical machine learning and numerical magnetohydrodynamic models are used for forecasting. In addition to nowcast capabilities, contextual information on the likelihood of substorms, sudden commencements and large rates of change of the magnetic field were developed. The final nowcast and forecast codes were implemented in a cloud‐based environment using modern software tools and practices. We describe the process to move from research to operations (R2O).

Substorms have been identified from negative bays in the AL/SML index, which traces the minimum northward ground magnetic deflection at auroral latitudes, produced by enhancements of the westward electrojet. For substorms, negative bays are caused by the closure of the Substorm Current Wedge through the ionosphere, typically localized to the nightside and centered around 23‐00 magnetic local time (MLT). In this case, the equivalent current pattern that causes the magnetic deflections is given the name Disturbance Polar (DP) 1. However, negative bays may also form when the westward electrojet is enhanced by increased convection, driving Pedersen and Hall currents in the auroral zone. Convection enhancements also strengthen the eastward electrojet, monitored by AU/SMU index. In this case, the equivalent current pattern that produces the magnetic deflections is called DP2. Unlike other substorm identification methods, the Substorm Onsets and PHases from Indices of the Electrojets technique by Forsyth et al. (2015), https://doi.org/10.1002/2015ja021343 attempts to distinguish between the DP1 and DP2 enhancements that cause substorm‐like SML bays. Despite this, we find evidence that between 1997 and 2019 up to 59% of the 30,329 events originally identified as substorms come from enhancements of DP2 on top of the 2,627 convection enhancement events already identified. We explore ways to improve substorm identification using auroral indices to fully separate the DP1 and DP2 bays but conclude that there is insufficient information in the auroral indices to achieve this. In reality, any “substorm” list is a list of magnetic enhancements, auroral enhancements, etc., which may or may not correspond to substorm activity and should be treated that way.

Using line-of-sight velocity measurements made by SuperDARN (The Super Dual Auroral Radar Network) radars with overlapping fields of view, it is possible to estimate the vorticity of the ionospheric convection flow over a wide range of scales. Here we exploit previous statistical analyses of 6 years of SuperDARN vorticity measurements to study the spatial variation of meso-scale flows in ionospheric convection. By making certain assumptions, we can statistically separate probability density functions (PDFs) of vorticity made at different locations in the ionosphere into two populations: (i) That due to the large-scale two-cell convection flow driven primarily by magnetic reconnection, and (ii) that due to meso-scale flow structures driven by processes such as turbulence. The resulting PDFs are fit by model functions using maximum likelihood estimation, and the spatial variation of the estimators is determined. The spatial variations of the large-scale vorticity estimators are ordered by the average convection flow, which is highly dependent on the IMF direction. The spatial variations of the meso-scale vorticity estimators appear independent of the senses of vorticity and IMF direction, but have a different character in the polar cap, the cusp, the auroral region, and the sub-auroral region.

Substorms have been identified from negative bays in the AL/SML index, which traces the minimum northward ground magnetic deflection at auroral latitudes, produced by enhancements of the westward electrojet. For substorms, negative bays are caused by the closure of the Substorm Current Wedge through the ionosphere, typically localised to the nightside and centred around 23-00 magnetic local time (MLT). In this case, the equivalent current pattern that causes the magnetic deflections is given the name Disturbance Polar (DP) 1. However, negative bays may also form when the westward electrojet is enhanced by increased convection, driving Pedersen and Hall currents in the auroral zone. Convection enhancements also strengthen the eastward electrojet, monitored by the maximum northward ground magnetic deflection as the AU/SMU index. In this case, the equivalent current pattern that produces the magnetic deflections is called DP2. Unlike other substorm identification methods, the SOPHIE technique by Forsyth et al. (2015) attempts to distinguish between the DP1 and DP2 enhancements that cause substorm-like SML bays by also examining the SMU index. Despite this, we find evidence that between January 1997 and December 2019 up to 59% of the 30329 events originally identified as substorms come from enhancements of DP2 on top of the 2627 convection enhancement events already identified. We explore ways to improve substorm identification using auroral indices to fully separate the DP1 and DP2 bays, but conclude that there is insufficient information in the auroral indices alone to achieve this.

Substorms are a rapid release of energy that is redistributed throughout the magnetosphere‐ionosphere system, resulting in many observable signals, such as enhancements in the aurora, energetic particle injections, and ground magnetic field perturbations. Numerous substorm identification techniques and onset lists based on each of these signals have been provided in the literature, but often with no cross‐calibration. Since the signals produced are not necessarily unique to substorms and may not be sufficiently similar to be identified for each and every substorm, individual event lists may miss or misidentify substorms, hindering our understanding and the development and validation of substorm models. To gauge the scale of this problem, we use metrics derived from contingency tables to quantify the association between lists of substorms derived from SuperMAG SML/SMU indices, midlatitude magnetometer data, particle injections, and auroral enhancements. Overall, although some degree of pairwise association is found between the lists, even lists generated by applying conceptually similar gradient‐based identification to ground magnetometer data achieve an association with less than 50% event coincidence. We discuss possible explanations of the levels of association seen from our results, as well as their implications for substorm analyses.

The ability to understand and model ionospheric plasma flow on all spatial scales has important implications for operational space weather models. This study exploits a recently developed method to statistically separate large‐scale and meso‐scale contributions to probability density functions (PDFs) of ionospheric flow vorticity measured by the Super Dual Auroral Radar Network (SuperDARN). The SuperDARN vorticity data are first sub‐divided depending on the Interplanetary Magnetic Field (IMF) direction, and the separation method is applied to PDFs of vorticity compiled in spatial regions of size 1° of geomagnetic latitude by 1 hr of magnetic local time, covering much of the high‐latitude ionosphere in the northern hemisphere. The resulting PDFs are fit by model functions using maximum likelihood estimation (MLE) and the spatial variations of the MLE estimators for both the large‐scale and meso‐scale components are presented. The spatial variations of the large‐scale vorticity estimators are ordered by the average ionospheric convection flow, which is highly dependent on the IMF direction. The spatial variations of the meso‐scale vorticity estimators appear independent of the senses of vorticity and IMF direction, but have a different character in the polar cap, the cusp, the auroral region, and the sub‐auroral region. The paper concludes by discussing the sources of the vorticity components in the different regions, and the consequences for the fidelity of ionospheric plasma flow models.

A significant increase in the number of anthropogenic objects in Earth orbit has necessitated the development of satellite conjunction assessment and collision avoidance capabilities for new spacecraft. Neutral mass density variability in the thermosphere, driven by enhanced geomagnetic activity and solar EUV absorption, is a major source of satellite propagation error. This work investigates the impacts of space weather driver forecasting uncertainty on satellite drag and collision avoidance maneuver decision‐making. Since most operational space weather driver forecasts do not offer an uncertainty assessment, the satellite operator community is left to make dangerous assumptions about the trustworthiness of the forecast models they use to perform satellite state propagation. Climatological persistence‐based forecast models are developed for F_10.7 and Kp. These models accurately capture the heteroscedastic and, at times, highly non‐Gaussian uncertainty distribution on forecasts of the drivers of interest. A set of realistic satellite conjunction scenarios is simulated to demonstrate the contributions of space weather driver forecast uncertainty on the probability of collision and maneuver decisions. Improved driver forecasts, especially forecasts of F_10.7, are demonstrated to be very useful for enabling durable maneuver decisions with additional lead time (up to 24 hr for the period examined), though the improvement depends on the specific conjunction scenario of interest.

Solar eclipses present a rare glimpse into the impact of ionospheric electrodynamics on the magnetosphere independent of other well studied seasonal influences. Despite decades of study, we still do not have a complete description of the conditions for geomagnetic substorm onset. We present herein a mutual information based study of previously published substorm onsets and the past two decades of eclipses which indicates the likelihood of co‐occurrence is greater than random chance. A plausible interpretation for this relation suggests that the abrupt fluctuations in ionospheric conductivity during an eclipse may influence the magnetospheric preconditions of substorm initiation. While the mechanism remains unclear, this study presents strong evidence of a link between substorm onset and solar eclipses.

We examine the statistical distribution of large‐scale Birkeland currents measured by the Active Magnetosphere and Planetary Electrodynamics Response Experiment in four unique categories of geomagnetic activity for the first time: quiet times, storm times, quiet‐time substorms, and storm‐time substorms. A novel method is employed to sort data into one of these four categories, and the categorizations are provided for future research. The mean current density is largest during substorms and its standard deviation is largest during geomagnetic storms. Current densities which are above a low threshold are more likely during substorms, but extreme currents are far more likely during geomagnetic storms, consistent with a paradigm in which geomagnetic storms represent periods of enhanced variability over quiet times. We demonstrate that extreme currents are most likely to flow within the Region 2 current during geomagnetic storms. This is unexpected in a paradigm of the current systems in which Region 1 current is generally larger.