Gareth Chisham

The high latitudes are a distinct region of the geospace environment, where strong coupling between the atmosphere, ionosphere, magnetosphere, and solar wind create distinct regions where different physical processes dominate. Because of this, modelling and statistical studies of the geospace environment at high latitudes should avoid mixing data measured in different regions (e.g., the polar cap, the auroral oval, and the sub-auroral regions). This study presents two new sets of poleward and equatorward Auroral Luminosity Boundaries (ALBs) along with functional definitions that relate these ALBs to the poleward and equatorward particle precipitation boundaries that mark the open–closed magnetic field line boundary (OCB) that demarcates the transition between the polar cap and auroral oval and the diffuse auroral boundary at the equatorward edge of the auroral oval. These data sets are obtained from space-based auroral imagers flown on the Thermosphere Ionosphere Mesosphere Energetics and Dynamics (TIMED) satellite and the Defense Meteorological Satellite Program (DMSP) constellation, that observe slices of the auroral oval in both hemispheres. The validation of the derived OCB locations against an independent polar cap proxy shows that the ALBs identified at the Lyman-Birge-Hopfield (LBH) Long wavelengths may be used together with other auroral boundary data sets in scientific studies.

The 4 December 2021 Antarctic total solar eclipse enhanced anti-sunward polar cap plasma flow, as directly and independently observed by the SuperDARN McMurdo (MCM) radar and the Jang Bogo VIPIR, modulated by eclipse obscuration. AMPERE field-aligned currents (FACs) over the same interval likewise increased with eclipse obscuration, together indicating a possibility of eclipse-driven reconfiguration of polar cap electrodynamics. As supporting, model-dependent context, we combine GITM-derived ionospheric conductance with the AMPERE FACs in a pyMIX potential solver, which shows a loosely corresponding increase in cross-polar-cap potential - consistent with, though not independent confirmation of, the electrodynamic changes observed directly. Separately, the SuperDARN Falkland Islands (FIR) radar observed the sudden emergence of a new 1.5-hop HF propagation mode near the auroral oval boundary during totality and its location of appearance modulated by eclipse obscuration. We examine possible sources of this mode using ray-tracing simulations driven by the same eclipse-modified GITM electron density fields. Together, these multi-instrument observations show that eclipse-driven reduction in ionospheric conductance may drive the coupled magnetosphere--ionosphere (M--I) system through complex, nonlinear adjustment of currents and plasma flow, and that the accompanying changes in ionospheric density can also alter HF propagation conditions.

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.

Imagers that observe optical or ultraviolet emissions from the atmosphere are commonly used to identify and study ionospheric phenomena. These phenomena include the auroral oval, equatorial plasma bubbles, and traveling ionospheric disturbances. One difficulty with using imager observations is accurately and automatically retrieving locations of interest from these images. This article presents an automated method designed to identify auroral luminosity boundaries from space‐based imager data. This method was originally developed for the Imager for Magnetopause‐to‐Aurora Global Exploration (IMAGE) observations, but has been further adapted for use with a wider range of observations. This article discusses the updated boundary detection method, and demonstrates the process on two different satellite data sets. The updated detection method has been made publicly accessible through a new Python package, pyIntensityFeatures .

The high‐latitude ionosphere can be divided into three regions dominated by different types of coupling and characterized by distinctive behaviors. These are the polar cap, the auroral region, and the sub‐auroral region. Their locations are highly variable, changing in extent in response to driving conditions in the solar wind and within the magnetosphere. We discuss how defining high‐latitude coordinates relative to the boundaries between these physically significant regions (adaptive co‐ordinates) has major implications for statistical studies, modeling applications, and research combining magnetospheric and ionospheric data. We explore the impact of using adaptive co‐ordinates for statistical analyses of ionospheric vorticity, showing how using adaptive co‐ordinate systems provides a clearer picture of the latitudinal variation of vorticity, and how peaks and troughs in vorticity relate to the boundary locations.

Using auroral boundaries determined from far ultraviolet images of the aurora (Chisham et al., 2022), we have examined auroral occurrence with respect to magnetic latitude, local time and the Kp index. Our results show that auroral occurrence is highly correlated (R2>90%) with Kp between values of 0o and 5o. We use linear fits between occurrence and Kp to build a probabilistic Auroral Location Forecast (ALF) which gives the likelihood of the aurora occuring at a given magnetic latitude and local time for any level of Kp. The model includes both correlated relationships between Kp and occurrence at low latitudes and anti-correlated relationships between Kp and occurrence at high latitudes, enabling the model to replicate behaviour expected within the expanding-contracting polar cap paradigm. The model also shows higher variability in the location of the auroral boundary close to the interface between the upward Region 2 currents and downward Region 1 currents. Validation of the model returns high Brier Skill Scores for both the range of Kp used in the model creation (Kp=0 – 5, Brier Skill Score = 0.569) and the range unseen by the model (Kp=6 – 9, Brier Skill Score = 0.532) indicating that the model is skillful in predicting the location of the aurora. The results of our analysis and outputs of ALF may be of interest to space weather professionals and ‘aurora chasers’ in determining the likelihood of aurora being present, particularly when coupled with forecasts of the Kp index.

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.

Imagers that observe emissions from the atmosphere are commonly used to study various ionospheric phenomena. These phenomena include the auroral oval, equatorial plasma bubbles, and travelling ionospheric disturbances. A difficulty in using imager observations is accurately and automatically retrieving the locations of interest from these images. We present an automated method designed to identify the auroral luminosity boundaries from space-based imager data. These boundaries are important for high-latitude studies that use statistical or machine learning approaches, as geographic and magnetic coordinate systems that do not account for changes in the polar cap or equatorward auroral oval boundaries will mix together data from regions experiencing different types of coupling with the magnetosphere.The boundary identification method was originally developed for the Imager for Magnetopause-to-Aurora Global Exploration (IMAGE) observations, and has been further adapted for use in a wider variety of situations. We will discuss the updated detection method and demonstrate the process on two different satellite data sets. The updated detection method will be made publicly accessible through a new Python package, pyIntensityFeatures.