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Abstract Sporadic‐E (Es) are thin layers of enhanced ionization observed in the E‐region, typically between 95 and 120 km altitude. Es plays an important role in controlling the dynamics of the upper atmosphere and it is necessary to understand the geophysical factors influencing Es from both the scientific and operational perspectives. While the wind‐shear theory is widely accepted as an important mechanism responsible for the generation of Es, there are still gaps in the current state of our knowledge. For example, we are yet to determine precisely how changes in the dynamics of horizontal winds impact the formation, altitude, and destruction of Es layers. In this study, we report results from a coordinated experimental campaign between the Millstone Hill Incoherent Scatter Radar, the SuperDARN radar at Blackstone, and the Millstone Hill Digisonde to monitor the dynamics of mid‐latitude Es layers. We report observations during a 15‐hr window between 13 UT on 3 June 2022 and 4 UT on 4 June 2022, which was marked by the presence of a strong Es layer. We find that the height of the Es layer is collocated with strong vertical shears in atmospheric tides and that the zonal wind shears play a more important role than meridional wind shears in generating Es, especially at lower altitudes. Finally, we show that in the presence of Es, SuperDARN ground backscatter moves to closer ranges, and the height and critical frequency of the Es layer have a significant impact on the location and intensity of HF ground scatter. 

Abstract 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. 

Abstract 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. 

Abstract 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. 

Abstract 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. 

Abstract The auroral substorm has been extensively studied over the last six decades. However, our understanding of its driving mechanisms is still limited and so is our ability to accurately forecast its onset. In this study, we present the first deep learning‐based approach to predict the onset of a magnetic substorm, defined as the signature of the auroral electrojets in ground magnetometer measurements. Specifically, we use a time history of solar wind speed (V_x), proton number density, and interplanetary magnetic field (IMF) components as inputs to forecast the occurrence probability of an onset over the next 1 hr. The model has been trained and tested on a data set derived from the SuperMAG list of magnetic substorm onsets and can correctly identify substorms ∼75% of the time. In contrast, an earlier prediction algorithm correctly identifies ∼21% of the substorms in the same data set. Our model's ability to forecast substorm onsets based on solar wind and IMF inputs prior to the actual onset time, and the trend observed in IMFB_zprior to onset together suggest that a majority of the substorms may not be externally triggered by northward turnings of IMF. Furthermore, we find that IMFB_zandV_xhave the most significant influence on model performance. Finally, principal component analysis shows a significant degree of overlap in the solar wind and IMF parameters prior to both substorm and nonsubstorm intervals, suggesting that solar wind and IMF alone may not be sufficient to forecast all substorms, and preconditioning of the magnetotail may be an important factor.