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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.

Medium‐scale Traveling Ionospheric Disturbances (MSTIDs) are prominent and ubiquitous features of the mid‐latitude ionosphere, and are observed in Super Dual Auroral Radar Network (SuperDARN) and high‐resolution Global Navigational Satellite Service (GNSS) Total Electron Content (TEC) data. The mechanisms driving these MSTIDs are an open area of research, especially during geomagnetic storms. Previous studies have demonstrated that nightside MSTIDs are associated with an electrodynamic instability mechanism like Perkins, especially during geomagnetically quiet conditions. However, dayside MSTIDs are often associated with atmospheric gravity waves. Very few studies have analyzed the mechanisms driving MSTIDs during strong geomagnetic storms at mid‐latitudes. In this study, we present mid‐latitude MSTIDs observed in de‐trended GNSS TEC data and SuperDARN radars over the North American sector, during a geomagnetic storm (peakKpreaching 9) on 7–8 September 2017. In SuperDARN, MSTIDs were observed in ionospheric backscatter with Line of Sight (LOS) velocities exceeding 800 m/s. Additionally, radar LOS velocities oscillated with amplitudes reaching ±500 m/s as the MSTIDs passed through the fields‐of‐view. In detrended TEC, these MSTIDs produced perturbations reaching ∼50 percent of background TEC magnitude. The MSTIDs were observed to propagate in the westward/south‐westward direction with a time period of ∼15 min. Projecting de‐trended GNSS TEC data along SuperDARN beams showed that enhancements in TEC were correlated with enhancements in SuperDARN SNR and positive LOS velocities. Finally, SuperDARN LOS velocities systematically switched polarities between the crests and the troughs of the MSTIDs, indicating the presence of polarization electric fields and an electrodynamic instability process during these MSTIDs.

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.

The Super Dual Auroral Radar Network (SuperDARN) is a network of High Frequency (HF) radars that are typically used for monitoring plasma convection in the Earth's ionosphere. A majority of SuperDARN backscatter can broadly be divided into three categories: (a) ionospheric scatter due to reflections from plasma irregularities in the E and F regions of the ionosphere, (b) ground scatter caused by reflections from the ground/sea surface following reflection in the ionosphere, and (c) backscatter from meteor trails left by meteoroids as they enter the Earth's atmosphere. Due to the complex nature of HF propagation and mid‐latitude electrodynamics, it is often not straightforward to distinguish between different modes of backscatter observed by SuperDARN. In this study, we present a new two‐stage machine learning algorithm for identifying different backscatter modes in SuperDARN data. In the first stage, a neural network that “mimics” ray‐tracing is used to predict the probability of ionospheric and ground scatter occurring at a given location along with parameters like the elevation angles, reflection heights etc. The inputs to the network include parameters that control HF propagation, such as signal frequency, season, UT time, and geomagnetic activity levels. In the second stage, the output probabilities from the neural network and actual SuperDARN data are clustered together to determine the category of the backscatter. Our model can distinguish between meteor scatter, 1/2 hop E‐/F‐region ionospheric as well as ground/sea scatter. We validate our model by comparing predicted elevation angles with those measured at a SuperDARN radar.

Sudden enhancement in high‐frequency absorption is a well‐known impact of solar flare‐driven Short‐Wave Fadeout (SWF). Less understood, is a perturbation of the radio wave frequency as it traverses the ionosphere in the early stages of SWF, also known as the Doppler flash. Investigations have suggested two possible sources that might contribute to it’s manifestation: first, enhancements of plasma density in the D‐and lower E‐regions; second, the lowering of the F‐region reflection point. Our recent work investigated a solar flare event using first principles modeling and Super Dual Auroral Radar Network (SuperDARN) HF radar observations and found that change in the F‐region refractive index is the primary driver of the Doppler flash. This study analyzes multiple solar flare events observed across different SuperDARN HF radars to determine how flare characteristics, properties of the traveling radio wave, and geophysical conditions impact the Doppler flash. In addition, we use incoherent scatter radar data and first‐principles modeling to investigate physical mechanisms that drive the lowering of the F‐region reflection points. We found, (a) on average, the change in E‐ and F‐region refractive index is the primary driver of the Doppler flash, (b) solar zenith angle, ray’s elevation angle, operating frequency, and location of the solar flare on the solar disk can alter the ionospheric regions of maximum contribution to the Doppler flash, (c) increased ionospheric Hall and Pedersen conductance causes a reduction of the daytime eastward electric field, and consequently reduces the vertical ion‐drift in the lower and middle latitude ionosphere, which results in lowering of the F‐region ray reflection point.

Over‐the‐Horizon communication is strongly dependent on the state of the ionosphere, which is susceptible to solar flares. Trans‐ionospheric high frequency (HF, 3–30 MHz) signals can experience strong attenuation following a solar flare that lasts typically for an hour, commonly referred to as shortwave fadeout (SWF). In this study, we examine the role of dispersion relation and collision frequency formulations on the estimation of SWF in riometer observations using a new physics‐based model framework. The new framework first uses modified solar irradiance models incorporating high‐resolution solar flux data from the GOES satellite X‐ray sensors as input to compute the enhanced ionization produced during a flare event. The framework then uses different dispersion relation and collision frequency formulations to estimate the enhanced HF absorption. The modeled HF absorption is compared with riometer data to determine which formulation best reproduces the observations. We find the Appleton‐Hartree dispersion relation in combination with the averaged collision frequency profile reproduces riometer observations with an average skill score of 0.4, representing 40% better forecast ability than the existing D‐region Absorption Prediction model. Our modeling results also indicate that electron temperature plays an important role in controlling HF absorption. We suggest that adoption of the Appleton‐Hartree dispersion relation in combination with the averaged collision frequency be considered for improved forecasting of ionospheric absorption following solar flares.

Trans‐ionospheric high frequency (HF: 3–30 MHz) signals experience strong attenuation following a solar flare‐driven sudden ionospheric disturbance (SID). Solar flare‐driven HF absorption, referred to as short‐wave fadeout, is a well‐known impact of SIDs, but the initial Doppler frequency shift phenomena, also known as “Doppler flash” in the traveling radio wave is not well understood. This study seeks to advance our understanding of the initial impacts of solar flare‐driven SID using a physics‐based whole atmosphere model for a specific solar flare event. First, we demonstrate that the Doppler flash phenomenon observed by Super Dual Auroral Radar Network (SuperDARN) radars can be successfully reproduced using first‐principles based modeling. The output from the simulation is validated against SuperDARN line‐of‐sight Doppler velocity measurements. We then examine which region of the ionosphere, D, E, or F, makes the largest contribution to the Doppler flash. We also consider the relative contribution of change in refractive index through the ionospheric layers versus lowered reflection height. We find: (a) the model is able to reproduce radar observations with an root‐median‐squared‐error and a mean percentage error (δ) of 3.72 m/s and 0.67%, respectively; (b) the F‐region is the most significant contributor to the total Doppler flash (∼48%), 30% of which is contributed by the change in F‐region's refractive index, while the other ∼18% is due to change in ray reflection height. Our analysis shows lowering of the F‐region's ray reflection point is a secondary driver compared to the change in refractive index.

The term “sluggishness” was coined by E. V. Appleton in the 1950s to describe the time delay between peak irradiance at solar noon and the resulting peak in ionospheric electron density. Sluggishness can be understood as an inertial property of the ionosphere that manifests as a lag of the ionospheric response to a solar driver. As shown by Appleton, estimates of sluggishness can be used to study the chemistry of the lower ionosphere, of the D‐region in particular. In this study, for the first time, we have examined ionospheric sluggishness in terms of the time delay between the peak irradiance during a solar flare and the resulting peak in ionospheric electron density using HF instruments. Estimates of the delay are obtained using HF observations from riometers and SuperDARN radars that are primarily sensitive to absorption in the D‐region. Two new methods for measuring delay are introduced. Sluggishness is shown to be anti‐correlated with peak solar X‐ray flux and positively correlated with zenith angle and latitude. The choices of instrument, method, and reference solar waveband affect the sluggishness estimation. A simulation study was performed to estimate the effective recombination coefficient in the D‐region. The coefficient was found to vary by orders of magnitude with peak flare intensity. We argue that the variation in effective recombination coefficient with peak flare intensity is highly sensitive to changes in the negative and positive ion chemistry of the D‐region, which is sensitive to the incoming solar X‐ray and EUV radiation.

The sub‐auroral polarization stream (SAPS) is a region of westward high velocity plasma convection equatorward of the auroral oval that plays an important role in mid‐latitude space weather dynamics. In this study, we present observations of SAPS flows extending across the North American sector observed during the recovery phase of a minor geomagnetic storm. A resurgence in substorm activity drove a new set of field‐aligned currents (FACs) into the ionosphere, initiating the SAPS. An upward FAC system is the most prominent feature spreading across most SAPS local times, except near dusk, where a downward current system is pronounced. The location of SAPS flows remained relatively constant, firmly inside the trough, independent of the variability in the location and intensity of the FACs. The SAPS flows were sustained even after the FACs weakened and retreated polewards with a decline in geomagnetic activity. The observations indicate that the mid‐latitude trough plays a crucial role in determining the location of the SAPS and that SAPS flows can be sustained even after the magnetospheric driver has weakened.

The existence of Birkeland magnetic field‐aligned current (FAC) system was proposed more than a century ago, and it has been of immense interest for investigating the nature of solar wind‐magnetosphere‐ionosphere coupling ever since. In this paper, we present the first application of deep learning architecture for modeling the Birkeland currents using data from the Active Magnetosphere and Planetary Electrodynamics Response Experiment (AMPERE). The model uses a 1‐hr time history of several different parameters such as interplanetary magnetic field (IMF), solar wind, and geomagnetic and solar indices as inputs to determine the global distribution of Birkeland currents in the Northern Hemisphere. We present a comparison between our model and bin‐averaged statistical patterns under steady IMF conditions and also when the IMF is variable. Our deep learning model shows good agreement with the bin‐averaged patterns, capturing several prominent large‐scale features such as the Regions 1 and 2 FACs, the NBZ current system, and the cusp currents along with their seasonal variations. However, when IMF and solar wind conditions are not stable, our model provides a more accurate view of the time‐dependent evolution of Birkeland currents. The reconfiguration of the FACs following an abrupt change in IMF orientation can be traced in its details. The magnitude of FACs is found to evolve with e‐folding times that vary with season and MLT. When IMF Bz turns southward after a prolonged northward orientation, NBZ currents decay exponentially with an e‐folding time of∼25 min, whereas Region 1 currents grow with an e‐folding time of 6–20 min depending on the MLT.