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 degrees 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.

Abstract 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 F10.7 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 F10.7, 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.

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 joint European Space Agency and Chinese Academy of Sciences Solar wind Magnetosphere Ionosphere Link Explorer (SMILE) mission will explore global dynamics of the magnetosphere under varying solar wind and interplanetary magnetic field conditions, and simultaneously monitor the auroral response of the Northern Hemisphere ionosphere. Combining these large-scale responses with medium and fine-scale measurements at a variety of cadences by additional ground-based and space-based instruments will enable a much greater scientific impact beyond the original goals of the SMILE mission. Here, we describe current community efforts to prepare for SMILE, and the benefits and context various experiments that have explicitly expressed support for SMILE can of fer. A dedicated group of international scientists representing many different experiment types and geographical locations, the Ground-based and Additional Science Working Group, is facilitating these efforts. Preparations include constructing an online SMILE Data Fusion Facility, the discussion of particular or special modes for experiments such as coherent and incoherent scatter radar, and the consideration of particular observing strategies and spacecraft conjunctions. We anticipate growing interest and community engagement with the SMILE mission, and we welcome novel ideas and insights from the solarterrestrial community.

Measurements of ionospheric flow vorticity can be used for studying ionospheric plasma transport processes, such as convection and turbulence, over a wide range of spatial scales. Here, we present the spatial variation across the northern hemisphere high-latitude ionosphere of probability density functions (PDFs) of ionospheric vorticity as measured by the Super Dual Auroral Radar Network (SuperDARN) over a six-year interval (2000-2005 inclusive). These PDFs are subdivided for different polarities of the By component of the Interplanetary Magnetic Field (IMF), which allows the separation of the observed PDFs into two distinct components. These components relate to: (1) The large-scale ionospheric convection flow driven by magnetic reconnection, and (2) Meso- and small-scale processes such as turbulence. The convection vorticity PDFs are single-sided and well fit by Weibull distributions, whereas the turbulence vorticity PDFs are double-sided and symmetric, and are well fit by q-exponential distributions. Both the observed model distributions can be understood in the framework of solutions of the stationary Fokker-Planck equation for different environmental plasma conditions.

On 04 December 2021, a total solar eclipse occurred over west Antarctica. Nearly an hour beforehand, a geomagnetic substorm onset was observed in the northern hemisphere. Eclipses are suggested to influence magnetosphere‐ionosphere (MI) coupling dynamics by altering the conductivity structure of the ionosphere by reducing photoionization. This sudden and dramatic change in conductivity is not only likely to alter global MI coupling, but it may also introduce a variety of localized instabilities that appear in both hemispheres. Global navigation satellite system (GNSS) based observations of the total electron content (TEC) in the southern high latitude ionosphere during the December 2021 eclipse show signs of wave activity coincident with the eclipse peak totality. Ground magnetic observations in the same region show similar activity, and our analysis suggest that these observations are due to an “eclipse effect” rather than the prior substorm. We present the first multi‐point interhemispheric study of a total south polar eclipse with local TEC observational context in support of this conclusion.

Forecasting of the effects of thermospheric drag on satellites will be improved significantly with better modeling of space weather effects on the high-latitude ionosphere, in particular the Joule heating arising from electric field variability. We use a regression analysis to build a model of the ionospheric convection drift velocity which is driven by relatively few solar and solar wind variables. The model is developed using a solar cycle's worth (1997-2008 inclusive) of 5-min resolution Empirical Orthogonal Function (EOF) patterns derived from Super Dual Auroral Radar Network (SuperDARN) line-of-sight observations of the convection velocity across the high-latitude northern hemisphere ionosphere. At key stages of development of the model, we use the percentage of explained variance P to see how well the model reproduces the EOF data. The final model is driven by four variables: (a) the interplanetary magnetic field component B-y, (b) the solar wind coupling parameter epsilon e, (c) a trigonometric function of day-of-year, and (d) the monthly F-10.7 index. The model can reproduce the EOF velocities with a characteristic P = 0.7. The model and EOF data compare best around the solar maximum of 2001. P $P$ is lower around solar minimum, due to occasional limitations in the geographical and temporal coverage of the SuperDARN measurements. This may indicate the need to modify our model around the minimum of the solar cycle. Our model has the potential to be used to forecast the ionospheric electric field using the real-time solar wind data available from spacecraft located upstream of the Earth.

Measurements of ionospheric flow vorticity can be used for studying ionospheric plasma transport processes, such as convection and turbulence, over a wide range of spatial scales. Here, we analyze probability density functions (PDFs) of ionospheric vorticity for selected regions of the northern hemisphere high‐latitude ionosphere as measured by the Super Dual Auroral Radar Network over a 6‐year interval (2000–2005 inclusive). Subdividing these PDFs for opposite polarities of the B y component of the prevailing interplanetary magnetic field allows the separation into two distinct components: (a) A single‐sided Weibull distribution which relates to the large‐scale convection driven by magnetic reconnection; (b) A double‐sided and symmetric q ‐exponential distribution which arises from meso‐scale plasma flow related to processes such as turbulence.

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. We take measurements from a set of 66 spacecraft orbiting Earth to look at electric currents that flow along Earth's magnetic field lines. We look at different types of space weather called "geomagnetic storms" and "substorms," and combine methods to detect when those types of space weather happen. We use our combined method to separate our measurements into the different types of space weather, and then we look at how strong the currents are during each type of space weather. We plot histograms of the strengths and then use those histograms to work out the underlying mathematics of the strengths: we can then plot further graphs showing how those underlying mathematics change. We then work out when the very strongest currents are likely to flow, and during which type of space weather this occurs, which is useful both for understanding the system and for mitigating against the risks of space weather. Geomagnetic storms are more likely than substorms to drive extreme field-aligned current densitiesExtreme current densities are most likely on the dayside and least likely within 3 hr of midnightThe highest probabilities of extreme current densities occur in Region 2 currents during geomagnetic storms

Abstract 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.

Auroral zones are regions where, in an average sense, aurorae due to solar activity are most likely spotted. Their shape and, similarly, the geographical locations most vulnerable to extreme space weather events (which we term ‘danger zones’) are modulated by Earth’s time-dependent internal magnetic field whose structure changes on yearly to decadal timescales. Strategies for mitigating ground-based space weather impacts over the next few decades can benefit from accurate forecasts of this evolution. Existing auroral zone forecasts use simplified assumptions of geomagnetic field variations. By harnessing the capability of modern geomagnetic field forecasts based on the dynamics of Earth’s core we estimate the evolution of the auroral zones and of the danger zones over the next 50 years. Our results predict that space-weather related risk will not change significantly in Europe, Australia and New Zealand. Mid-to-high latitude cities such as Edinburgh, Copenhagen and Dunedin will remain in high-risk regions. However, northward change of the auroral and danger zones over North America will likely cause urban centres such as Edmonton and Labrador City to be exposed by 2070 to the potential impact of severe solar activity.

<p>Forecasting of the effects of thermospheric drag on satellites will be improved significantly with more accurate modelling of space weather effects on the high-latitude ionosphere, in particular the Joule heating arising from electric field variability. This is the largest uncertainty in orbit prediction for satellites and space debris. We use a regression analysis to build a forecast model of the ionospheric convection <em><strong>E×B</strong></em> drift velocity which is driven by relatively few solar and solar wind variables. The model is developed using a solar cycle’s worth (1997 to 2008 inclusive) of 5-minute resolution reanalysis data derived from Super Dual Auroral Radar Network (SuperDARN) line-of-sight observations of the convection velocity across the high-latitude northern hemisphere ionosphere. At key stages of development of the forecast model, we use the Priestley skill score to see how well the model reproduces the reanalysis dataset. The final forecast model is driven by four variables: (1) the interplanetary magnetic field component <em>B<sub>y</sub></em>, (2) the solar wind coupling parameter epsilon ε, (3) a trigonometric function of day of year, (4) the monthly f10.7 index. The forecast model can reproduce the reanalysis plasma velocities, with a characteristic skill score of 0.7. The forecast and reanalysis data compare best around the solar maximum of 2001. The forecast skill is lower around solar minimum, due to occasional limitations in the geographical and temporal coverage of the SuperDARN instrumentation. In addition, this may also indicate the need to modify our model of driving processes around the minimum of the solar cycle.</p>

Abstract Models of the high‐latitude ionospheric electric field (EF) are commonly used to specify the magnetospheric forcing in thermosphere or whole atmosphere models. The use of decades‐old models based on spacecraft data is still widespread. Currently the Heelis et al. (1982, https://doi.org/10.1029/ja087ia08p06339) and Weimer (2005b, https://doi.org/10.1029/2005ja011270) climatology models are most commonly used but it is possible a more recent EF model could improve forecasting functionality. Modern EF models, derived from radar data, have been developed to incorporate advances in data availability (Bristow et al., 2022, https://doi.org/10.1029/2021sw002920; Thomas & Shepherd, 2018, https://doi.org/10.1002/2018ja025280; Walach et al., 2022, https://doi.org/10.1029/2021ja029559). It is expected that climatologies based on this larger and up‐to‐date data set will better represent the high latitude ionosphere and improve forecasting abilities. An example of two such models, which have been developed using line‐of‐sight velocity measurements from the Super Dual Auroral Radar Network (SuperDARN) are the Thomas and Shepherd model (TS18) (Thomas & Shepherd, 2018, https://doi.org/10.1002/2018ja025280), and Walach and Grocott geomagnetic Storm model (WGS21) (Walach et al., 2021, https://doi.org/10.1029/2020ja028512). Here we compare the outputs of these EF models during the September 2017 storm, covering a range of solar wind and interplanetary magnetic field (IMF) conditions. We explore the relationships between the IMF conditions and the model output parameters such as transpolar voltage, the polar cap size and the lower latitude boundary of convection. We find that the electric potential and field parameters from the spacecraft‐based models have a significantly higher magnitude than the SuperDARN‐based models. We discuss the similarities and differences in topology and magnitude for each model.

The auroral zones indicate the locations on the Earth’s surface where, on average, it is most likely to spot aurorae as a consequence of increased solar activity. The shape of the auroral zones and, similarly, the geographical locations most vulnerable to extreme space weather events are modulated by the geomagnetic field of internal origin. As the latter evolves in time, the formers will be subject to variations on the same timescales. From available geomagnetic field forecasts (which provide an estimate of the future evolution of the geomagnetic field of internal origin) we derive AACGM latitudes and estimate the future evolution of the auroral zones. The novel aspect of this technique is that we make use of all available Gauss coefficients to produce the forecasts, while the majority of present techniques estimate the location of the auroral zones based on the dipolar coefficients only. Our results show that, while the shift of the geomagnetic dipole axis has a first order contribution, higher order Gauss coefficients contribute significantly to the location and shape of the auroral zones. The same technique is then extended to estimate the future location of the geographical location that would be, on average, most exposed to extreme space weather event. We find that the space-weather related risk will not change significantly for the UK over the next 50 years. For the Canadian provinces of Quebec and Ontario, however, we predict a significant increase in the risk associated to extreme solar activity.

The impact of a solar wind pressure pulse on the Earth’s magnetosphere causes rapid changes in the surface geomagnetic field, often termed Sudden Commencements (SCs). Such magnetic field changes can induce potentially damaging currents (GICs) in conducting infrastructure on the ground, and therefore represents a critical space weather hazard. Unfortunately, GICs are not often measured directly. Instead, large GICs are often inferred from easier-to-measure large magnetic perturbations. In this work we examine the coupling between SCs and observed GICs in New Zealand, where both measurements are available. Overall, we find excellent correlations between the maximum magnetic perturbations and GICs during SCs. Nevertheless, if the SC precedes a geomagnetic storm, then it is associated with 22% larger GICs, controlling for the size of the magnetic deflection. Further, if the SC is observed when New Zealand is on the dayside of the Earth then the associated GICs are 30% greater. We investigate these findings, and attribute them to the full vector directionality of the strongest magnetic field deflection and the full rate of change of the magnetic field of the SC, beyond that recorded in the one minute resolution data. Finally, we show that based on the properties of the solar wind shock, a skilful prediction can be made as to whether an SC and/or a geomagnetic storm will be observed, which may be used to guide interpretation of the coupling between the magnetic deflection and GICs.

We analyze probability distributions of Birkeland current densities measured by the Active Magnetosphere and Planetary Electrodynamics Response Experiment. We find that the distributions are leptokurtic rather than normal and they are sometimes heavy‐tailed. We fit q‐exponential functions to the distributions and use these to estimate where the largest currents are likely to occur. The shape and scale parameters of the fitted q‐exponential distribution vary with location: The scale parameter maximizes for current densities with the same polarity and in the same location as the average Region 1 current, whereas the shape parameter maximizes for current densities with the same polarity and in the same location as the average Region 2 current. We find that current densities |J| ≥ 0.2 μA m−2 are most likely to occur in the average Region 1 current region, and second most likely to occur in the average Region 2 current region. However, for extreme currents (|J| ≥ 4.0 μA m−2), we find that the most likely location is colocated with the average Region 2 current region on the dayside, at a colatitude of 18°−22°.

Abstract We analyze a set of events in which both electron flux dropouts caused by magnetopause shadowing and geosynchronous magnetopause crossings (GMCs) are observed. These observations are compared to event‐specific last closed drift shell (LCDS) models derived from the TS05 and TS07 external field models and magnetopause standoff distance. The LCDS models show good association with losses due to magnetopause shadowing but fail to reproduce observations of GMCs on the timescale of minutes. We show that different satellites in geostationary orbit observe different trends in electron flux during storm events on timescales of less than a day due to their separation in longitude. These differences demonstrate that both satellite L* and magnetic local time must be taken into account when modeling rapid variations in the outer radiation belt, and at least three satellites in geostationary orbit, ideally more, may be required for accurate forecasting and reconstruction of these events on timescales shorter than days.

Modern high latitude ionospheric electric field models have been developed to incorporate advances in data availability, however the use of older spacecraft-based models is still widespread. AENeAS (Advanced Ensemble electron density [Ne] Assimilation System) is a physics-based, thermosphere-ionosphere, coupled, assimilative model, which makes possible thermospheric forecasts. Currently AENeAS uses the Heelis and Weimer electric field spacecraft climatology models but it is possible a more recent electric field model could improve its functionality. Two such models are calculated using line-of-sight velocity measurements from the Super Dual Auroral Radar Network (SuperDARN): the Thomas and Shepherd model (TS18), and the Time-Variable Ionospheric Electric Field model (TiVIE) . Here we compare the electric field models during the September 2017 storm, covering a range of solar wind and interplanetary magnetic field (IMF) conditions. We explore the relationships between the IMF conditions and model output parameters such as transpolar voltage, the polar cap size and the lower latitude boundary. We find the spacecraft-based model electric potential and field parameters to have a significantly higher magnitude than the SuperDARN-based models. We will discuss the similarities and differences in topology and magnitude for each model.

Nearly all studies of impulsive geomagnetic disturbances (GMDs, also known as magnetic perturbation events MPEs) that can produce dangerous geomagnetically induced currents (GICs) have used data from the northern hemisphere. In this study, we investigated GMD occurrences during the first 6 months of 2016 at four magnetically conjugate high latitude station pairs using data from the Greenland West Coast magnetometer chain and from Antarctic stations in the conjugate AAL‐PIP magnetometer chain. Events for statistical analysis and four case studies were selected from Greenland/AAL‐PIP data by detecting the presence of >6 nT/s derivatives of any component of the magnetic field at any of the station pairs. For case studies, these chains were supplemented by data from the BAS‐LPM chain in Antarctica as well as Pangnirtung and South Pole in order to extend longitudinal coverage to the west. Amplitude comparisons between hemispheres showed (a) a seasonal dependence (larger in the winter hemisphere), and (b) a dependence on the sign of the By component of the interplanetary magnetic field (IMF): GMDs were larger in the north (south) when IMF By was >0 (<0). A majority of events occurred nearly simultaneously (to within ±3 min) independent of the sign of By as long as | By | ≤ 2 | Bz |. As has been found in earlier studies, IMF Bz was <0 prior to most events. When IMF data from Geotail, Themis B, and/or Themis C in the near‐Earth solar wind were used to supplement the time‐shifted OMNI IMF data, the consistency of these IMF orientations was improved.