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Abstract The use of supervised methods in space science have demonstrated powerful capability in classification tasks, but purely unsupervised methods have been less utilized for the classification of spacecraft observations. We use a combination of unsupervised methods, being principal component analysis, Self‐Organizing Maps, and hierarchical agglomerative clustering, to classify THEMIS and MMS observations as having occurred in the magnetosphere, magnetosheath, or the solar wind. The resulting classification are validated visually by analyzing the distribution of classifications and studying individual time series as well as by comparison to the labeled data set of a previous model, against which ours has an accuracy of 99.4. The model has a variety of applications beyond region classification such as deeper hierarchical analysis, magnetopause and bow shock crossing identification, and identification of bursty bulk flows, hot flow anomalies, and foreshock bubbles. 

Abstract. It is well known that the polar cap, delineated by the open–closed field line boundary (OCB),responds to changes in the interplanetary magnetic field (IMF).In general, the boundary moves equatorward when the IMF turns southward and contractspoleward when the IMF turns northward. However,observations of the OCB are spotty and limited in local time,making more detailed studies of its IMF dependence difficult.Here, we simulate five solar storm periods with the coupled model consisting of the OpenGeospace General Circulation Model (OpenGGCM) coupled with the Coupled Thermosphere IonosphereModel (CTIM) and the Rice Convection Model (RCM),i.e., the OpenGGCM-CTIM-RCM, to estimate the location and dynamics of the OCB.For these events, polar cap boundary location observations are also obtained from Defense MeteorologicalSatellite Program (DMSP) precipitation spectrograms and compared with the model output.There is a large scatter in the DMSP observations and in the model output.Although the model does not predict the OCB with high fidelity for every observation,it does reproduce the general trend as a function of IMF clock angle.On average, the model overestimates the latitude of the open–closed field line boundaryby 1.61∘. Additional analysis of the simulated polar cap boundary dynamics acrossall local times shows that the MLT of the largest polar cap expansion closely correlateswith the IMF clock angle, that the strongest correlation occurs when the IMF is southward, thatduring strong southward IMF the polar cap shifts sunward, and that the polar cap rapidlycontracts at all local times when the IMF turns northward. 

Abstract. In magnetospheric missions, burst-mode data sampling should be triggered in the presence of processes of scientific or operational interest. We present an unsupervised classification method for magnetospheric regions that could constitute the first step of a multistep method for the automatic identification of magnetospheric processes of interest. Our method is based on self-organizing maps (SOMs), and we test it preliminarily on data points from global magnetospheric simulations obtained with the OpenGGCM-CTIM-RCM code. The dimensionality of the data is reduced with principal component analysis before classification. The classification relies exclusively on local plasma properties at the selected data points, without information on their neighborhood or on their temporal evolution. We classify the SOM nodes into an automatically selected number of classes, and we obtain clusters that map to well-defined magnetospheric regions. We validate our classification results by plotting the classified data in the simulated space and by comparing with k-means classification. For the sake of result interpretability, we examine the SOM feature maps (magnetospheric variables are called features in the context of classification), and we use them to unlock information on the clusters. We repeat the classification experiments using different sets of features, we quantitatively compare different classification results, and we obtain insights on which magnetospheric variables make more effective features for unsupervised classification. 

Abstract Abrupt variations of auroral electrojets can induce geomagnetically induced currents, and the ability to model and forecast them is a pressing goal of space weather research. We report an auroral electrojet spike event that is extreme in magnitude, explosive in nature, and global in spatial extent that occurred on 24 April 2023. The event serves as a fundamental test of our understanding of the response of the geospace system to solar wind dynamics. Our results illustrate new and important characteristics that are drastically different from existing knowledge. Most important findings include (a) the event was only of ∼5‐min duration and was limited to a narrow (2°–3°) band of diffuse aurora; (b) the longitudinal span covered the entire nightside sector, possibly extending to the dayside; (c) the trigger seems to be a transient solar wind dynamic pressure pulse. In comparison, substorms usually last 1–2 hr and span almost the entire latitudinal width of the auroral oval. Magnetic perturbation events (MPEs) span hundreds km in radius. Both substorms and MPEs are mainly driven by disturbances in the magnetotail. A possible explanation is that the pressure pulse compresses the magnetosphere and enhances diffuse precipitation of electrons and protons from the inner plasma sheet, which elevates the ionospheric conductivity and intensifies the auroral electrojet. Therefore, the event exhibits a potentially new type of geomagnetic disturbance and highlights a solar wind driver that is enormously influential in driving extreme space weather events.