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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.
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Modeling Stable Auroral Red (SAR) Arcs at Geomagnetic Conjugate Points: Implications for Hemispheric Asymmetries in Heat Fluxes
Abstract Stable auroral red (SAR) arcs provide opportunities to study inner magnetosphere‐ionosphere coupling at midlatitudes. An imaging system at a single‐site obtains evidence of seasonal variations in SAR arc brightness and occurrence rates using events widely separated in time, as observed during different geomagnetic storms. The first SAR arc observed using two all‐sky imagers at geomagnetic conjugate points described seasonal effects at the same time for the same storm (Martinis, Mendillo, et al., 2019,https://doi.org/10.1029/2018JA026018). Here we report on modeling studies that enable specification of the roles of local “receptor conditions” in each hemisphere, plus the division of driving energy from a single source region into conjugate ionospheres. The geomagnetic storm of 1 June 2013 produced SAR arcs observed by conjugate all‐sky imagers yielding 73 Rayleighs (R) at Millstone Hill (L= 2.64) in the summer hemisphere, and 300 R during local winter at Rothera (L= 2.92). With incoherent scatter radar data not available to specify input conditions, we offer a new simulation approach using non‐incoherent scatter radar observations to specify local receptor conditions. These include a combination of semiempirical models (International Reference Ionosphere and MSIS) calibrated by local ionosonde and DMSP satellite data. We find that the driving mechanism (heat conduction entering the ionosphere) is not an equal partition of energy from the ring current source region, but one that is weaker in the summer hemisphere where the local receptor conditions are poised to produce fainter SAR arcs. The relationship between SAR arcs and recently discovered STEVE events are discussed and require further study.
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- Award ID(s):
- 1659304
- PAR ID:
- 10445707
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Space Physics
- Volume:
- 124
- Issue:
- 7
- ISSN:
- 2169-9380
- Page Range / eLocation ID:
- p. 6330-6342
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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