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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. Plain Language Summary: The Super Dual Auroral Radar Network (SuperDARN) is an international network of radars that operate in the High Frequency (HF) range (between 8 and 18 MHz). The radars are primarily used for monitoring the motion of plasma in the charged portion of Earth's upper atmosphere, called the ionosphere. A key feature of HF radio waves is that they are refracted or "bent" by the ionosphere. As a result, these frequencies are usually reflected back by decameter‐scale density structures in the ionosphere or by the ground, depending on the density of the ionosphere and operational characteristics of the radar. SuperDARN data is therefore mostly composed of measurements from the ionosphere, ground/sea, and meteor trails. At high latitudes, it is usually straightforward to distinguish between fast‐moving ionospheric and the slow‐moving ground observations. However, at mid‐latitudes, due to the similarities between ionospheric and ground measurements it is usually difficult to distinguish between these two types of measurements. In this study, we present a new methodology that uses a physics‐based approach to encode certain HF propagation characteristics into a machine learning model that can: (a) provide the ability to classify between different types of measurements, and (b) determine the uncertainties in the model ionosphere used to characterize HF propagation. Key Points: We developed a new machine learning model that is guided by ray‐tracing and the International Reference Ionosphere (IRI) to examine SuperDARN backscatter modesOur model can be used to classify SuperDARN backscatter into different categories such as meteor, E‐/F‐region ionosphere and ground/seaThe model's error rate is lowest in winter and highest in summer, suggesting uncertainties in the IRI ionosphere are larger in summer [ABSTRACT FROM AUTHOR]

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. Plain Language Summary: The sub‐auroral polarization stream (SAPS) is an important phenomenon that controls the dynamics of the sub‐auroral region. While SAPS has been studied for several decades, our understanding of the drivers of this phenomenon is still limited. In this study, we analyze an event to examine the validity of different SAPS mechanisms, and to determine the importance of the ionosphere‐thermosphere system in driving and sustaining SAPS flows. We find that the ionosphere can play an important role in determining SAPS location and that SAPS can be sustained even after the magnetospheric driver has weakened. Key Points: During the recovery phase of a small geomagnetic storm, substorm activity drove a new set of FACs into the ionosphere, initiating a sub‐auroral polarization stream (SAPS)The SAPS location remained relatively constant, firmly inside the trough, independent of the variability in the field‐aligned currents (FACs)The SAPS flows were sustained for almost an hour after the FACs weakened and retreated polewards [ABSTRACT FROM AUTHOR]