Testing Nowcasts of the Ionospheric Convection from the Expanding and Contracting Polar Cap Model

The expanding/contracting polar cap (ECPC) model, or the time-dependent Dungey cycle, provides a theoretical framework for understanding solar wind-magnetosphere-ionosphere coupling. The ECPC describes the relationship between magnetopause reconnection and substorm growth phase, magnetotail reconnection and substorm expansion phase, associated changes in auroral morphology, and ionospheric convective motions. Despite the many successes of the model, there has yet to be a rigorous test of the predictions or nowcasts made regarding ionospheric convection, which remains a final hurdle for the validation of the ECPC. In this study we undertake a comparison of ionospheric convection, as measured in situ by ion drift meters on board DMSP (Defense Meteorological Satellite Program) satellites and from the ground by SuperDARN (Super Dual Auroral Radar Network), with motions nowcasted by a theoretical model. The model is coupled to measurements of changes in the size of the polar cap made using global auroral imagery from the IMAGE FUV (Imager for Magnetopause to Aurora Global Exploration Far Ultraviolet) instrument, as well as the dayside reconnection rate, estimated using the OMNI data set. The results show that we can largely nowcast the magnitudes of ionospheric convection flows using the context of our understanding of magnetic reconnection at the magnetopause and in the magnetotail. Plain Language Summary We test a physics-based model which describes flows in the ionosphere near the magnetic poles due to solar wind driving of the activity within the Earth's magnetic environment using spacecraft and radar measurements of the flows. The results of this comparison show that our knowledge of the interactions of the solar wind, the Earth's magnetic environment, and ionosphere encompasses the general pattern of flows well, as well as the flow strengths. Further work is required to expand our understanding of asymmetric flows and to be able to model them better.