Average Ionospheric Electric Field Morphologies During Geomagnetic Storm Phases

We utilize principal component analysis to identify and quantify the primary electric potential morphologies during geomagnetic storms. Ordering data from the Super Dual Auroral Radar Network (SuperDARN) by geomagnetic storm phase, we are able to discern changes that occur in association with the development of the storm phases. Along with information on the size of the patterns, the first six eigenvectors provide over ∼80% of the variability in the morphology, providing us with a robust analysis tool to quantify the main changes in the patterns. Studying the first six eigenvectors and their eigenvalues shows that the primary changes in the morphologies with respect to storm phase are the convection potential enhancing and the dayside throat rotating from pointing toward the early afternoon sector to being more sunward aligned during the main phase of the storm. We find that the ionospheric electric potential increases through the main phase and then decreases once the storm phase begins. The dayside convection throat points toward the afternoon sector before the main phase and then as the potential increases throughout the main phase, the dayside throat rotates toward magnetic noon. Furthermore, we find that a two‐cell convection pattern is dominant throughout and that the dusk cell is overall stronger than the dawn cell. Plain Language Summary During geomagnetic storms, we see extreme changes to Earth's magnetic field structure. This is mainly due to an enhancement of electrical currents in geospace. This changes the Earth's magnetic environment, due to which we also see changes in the ionosphere, the layer of charged particles making up the top of the atmosphere where the current systems close. A geomagnetic storm has three phases: the initial phase, which is a precursor to the storm, the main phase where the current systems enhance abruptly, and a recovery phase. Here, we use a technique commonly used for pattern recognition to radar data to work out the changes to the average ionospheric flows. We find that most of the changes happen on the dayside. We suggest this means the average storm dynamics are driven directly by the solar wind. Key Points Using principal component analysis on SuperDARN data, we identify primary contributing basis convection patterns during geomagnetic storms The first six eigenvectors of the analysis provide over 80% of the total variance, excluding expansions and contractions of the pattern Main changes in the electric field