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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.

In this study we have used 7 years (2011–2017) of quiet (Kp ≤ 2+) to moderately disturbed (Kp = 3) time nightside line‐of‐sight measurements from six midlatitude Super Dual Auroral Radar Network radars in the U.S. continent to characterize the subauroral convection in terms of magnetic latitude, magnetic local time, month, season, Kp, and the interplanetary magnetic field (IMF) clock angle. Our results show that (1) the quiet time (Kp ≤ 2+) subauroral flows are predominantly westward (20–90 m/s) in all months and become meridional (−20–20 m/s) near dawn and dusk, with the flows being the strongest and most structured in December and January. (2) The Kp dependency is prominent in all seasons such that for higher Kp the premidnight westward flow intensifies and the postmidnight eastward flow starts to emerge. (3) Sorting by IMF clock angle shows Bz+/Bz− features consistent with lower/higher Kp conditions, as expected, but also shows distinct differences that are associated with By sign. (4) There is a pronounced latitudinal variation in the zonal flow speed between 18 and 2 magnetic local time in winter (November to February) that exists under all IMF conditions but is most pronounced under IMF Bz− and higher Kp. Our analysis suggests that the quiet time subauroral flows are due to the combined effects of solar wind/magnetosphere coupling leading to penetration electric field and the neutral wind dynamo with the ionospheric conductivity modulating their relative dominance.

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.