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Abstract This study investigates the impact of vertical ionospheric drift during daytime on the evolution of predawn equatorial plasma bubbles by conducting model simulations using “Sami3 is Another Model of the Ionosphere.” The upward drift of the ionosphere transports bubbles to higher altitudes, where their lifetime is set by the atomic oxygen photoionization rate. While the bubbles generated at predawn persist into dayside, the bubbles generated shortly after sunset diminish before sunrise. Therefore, post‐sunset bubbles do not contribute to daytime electron density irregularities. Bubbles maintain their field‐aligned characteristics throughout the daytime regardless of the vertical ionospheric drift. This property allows bubbles to exist near the magnetic equator despite poleward plasma transport by the fountain process. The shift of irregularity concentration to higher latitudes over time in satellite observations is explained by the combined effect of transport of bubbles to higher altitudes and rapid refilling of depletions near the magnetic equator. 

Abstract: Global Positioning System (GPS) scintillations are radio signal fluctuations due to ionospheric structures or irregularities. In this work, we utilize scintillation data to identify the source region (auroral oval vs. polar cap) of a high latitude irregularity responsible for any given scintillation instance, using a combination of ML and deep learning models. We used available high‐rate (50‐Hz) GPS data from three Canadian High Arctic Ionospheric Network stations for 2 yrs to develop a categorization methodology based on an unsupervised detection ML model called Isolation Forest (iForest), which detects scintillation instances to be fed to a supervised artificial neural network (ANN) model. The goal is to confidently categorize the high‐latitude ionospheric scintillations based on their regions of occurrence. Our method involves using low‐rate GPS scintillation indices to threshold high‐rate data to be fed to the iForest for event detection. We use satellite data‐derived auroral oval and polar cap boundaries to label our ANN data set. Our analysis of these preliminary data sets shows that the iForest algorithm detects events with high accuracy irrespective of the geomagnetic conditions and receiver location. The ANN consistently yielded an F1‐score close to 0.7 implying that the model can classify source regions based on the input data. The ANN‐based classification of source regions of scintillation events performed better during quiet times than stronger geomagnetic conditions. Our method can be applied to different ionospheric irregularity problems, making this a potentially useful tool for understanding scintillations and their relationship with different irregularity generation mechanisms.