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We study the spatial structure of a polarization jet/Sub-Auroral Ion Drift (PJ/SAID) based on data from the NorSat-1 and Swarm satellites during a geomagnetic storm. Observations of plasma parameters inside the PJ/SAID are obtained with NorSat-1 using a system of Langmuir probes with a nominal sampling rate of up to 1 kHz, which allowed measurements with such a high temporal resolution for the first time. A comparative analysis of plasma parameters and electron density spectra inside PJ according to the data from both satellites is presented. Our results show that fluctuations of plasma parameters inside the PJ increase at all scales with increasing geomagnetic activity. Small-scale irregularities in the PJ are measured in situ down to hundreds of meters. The role of large-scale effects in the PJ increases in comparison with the small-scale ones during high geomagnetic activity. The PJ consists of structures ∼0.2° latitude in size within which small-scale irregularities are present., (© 2022. The Authors.)

We develop an open source algorithm to apply T ransfer learning to A urora image classification and M agnetic disturbance E valuation (TAME). For this purpose, we evaluate the performance of 80 pretrained neural networks using the Oslo Auroral THEMIS (OATH) data set of all-sky images, both in terms of runtime and their features' predictive capability. From the features extracted by the best network, we retrain the last neural network layer using the Support Vector Machine (SVM) algorithm to distinguish between the labels "arc," "diffuse," "discrete," "cloud," "moon" and "clear sky/ no aurora". This transfer learning approach yields 73% accuracy in the six classes; if we aggregate the 3 auroral and 3 non-aurora classes, we achieve up to 91% accuracy. We apply our classifier to a new dataset of 550,000 images and evaluate the classifier based on these previously unseen images. To show the potential usefulness of our feature extractor and classifier, we investigate two test cases: First, we compare our predictions for the "cloudy" images to meteorological data and second we train a linear ridge model to predict perturbations in Earth's locally measured magnetic field. We demonstrate that the classifier can be used as a filter to remove cloudy images from datasets and that the extracted features allow to predict magnetometer measurements. All procedures and algorithms used in this study are publicly available, and the code and classifier are provided, which opens possibility for large scale studies of all-sky images., (© 2021. The Authors.)