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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.

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.