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 (ΦPC) 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.
