Calculating the High-Latitude Ionospheric Electrodynamics Using a Machine Learning-Based Field-Aligned Current Model

Abstract

We introduce a new framework called Machine Learning (ML) based Auroral Ionospheric electrodynamics Model (ML-AIM). ML-AIM solves a current continuity equation by utilizing the ML model of Field Aligned Currents of Kunduri et al. (2020, https://doi.org/10.1029/2020JA027908), the FAC-derived auroral conductance model of Robinson et al. (2020, https://doi.org/10.1029/2020JA028008), and the solar irradiance conductance model of Moen and Brekke (1993, https://doi.org/10.1029/92gl02109). The ML-AIM inputs are 60-min time histories of solar wind plasma, interplanetary magnetic fields (IMF), and geomagnetic indices, and its outputs are ionospheric electric potential, electric fields, Pedersen/Hall currents, and Joule Heating. We conduct two ML-AIM simulations for a weak geomagnetic activity interval on 14 May 2013 and a geomagnetic storm on 7–8 September 2017. ML-AIM produces physically accurate ionospheric potential patterns such as the two-cell convection pattern and the enhancement of electric potentials during active times. The cross polar cap potentials (Φ_PC) from ML-AIM, the Weimer (2005, https://doi.org/10.1029/2004ja010884) model, and the Super Dual Auroral Radar Network (SuperDARN) data-assimilated potentials, are compared to the ones from 3204 polar crossings of the Defense Meteorological Satellite Program F17 satellite, showing better performance of ML-AIM than others. ML-AIM is unique and innovative because it predicts ionospheric responses to the time-varying solar wind and geomagnetic conditions, while the other traditional empirical models like Weimer (2005, https://doi.org/10.1029/2004ja010884) designed to provide a quasi-static ionospheric condition under quasi-steady solar wind/IMF conditions. Plans are underway to improve ML-AIM performance by including a fully ML network of models of aurora precipitation and ionospheric conductance, targeting its characterization of geomagnetically active times.