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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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Investigating Transport and Dissipation in the Subauroral E Region With Ionospheric Modification Experiments and Very High Frequency Radar Backscatter
Abstract Ionospheric modification experiments have been performed at the High‐Frequency Active Auroral Research Program (HAARP) facility in Gakona, Alaska, using a Very High Frequency (VHF) coherent scatter radar in Homer, Alaska, for experimental diagnostics. The experiments were intended to determine the threshold pump electric field required to initiate thermal parametric instability in theEregion. The pump power level was ramped systematically to determine the threshold, and the experiment was repeated at four closely spaced pump frequencies. This provided threshold estimates at fourEregion altitudes. The theory for thermal parametric instability based on the work of Dysthe et al. (1983,https://doi.org/10.1063/1.863993) has been modified for application in theEregion. The theory considers magneto‐ionic effects on the pump mode, linear mode conversion theory for upper hybrid wave generation, wave heating, and the effects of transport and dissipation based on fluid theory. The theory amounts to an eigenvalue problem where the eigenvalue is the threshold pump electric field for instability. The theory shows how the threshold depends on ionospheric transport coefficients and on the fractional cooling rate for inelastic electron‐neutral collisions. The theoretical predictions for threshold are roughly consistent with experimental values although the latter are probably affected by excess ionospheric absorption.
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- PAR ID:
- 10459902
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Radio Science
- Volume:
- 54
- Issue:
- 3
- ISSN:
- 0048-6604
- Page Range / eLocation ID:
- p. 245-253
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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