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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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Statistical Characterization of Joule Heating Associated With Ionospheric ULF Perturbations Using SuperDARN Data
Abstract Ultra low frequency (ULF; 1 mHz ‐ several Hz) waves are key to energy transport within the geospace system, yet their contribution to Joule heating in the upper atmosphere remains poorly quantified. This study statistically examines Joule heating associated with ionospheric ULF perturbations using Super Dual Auroral Radar Network (SuperDARN) data spanning middle to polar latitudes. Our analysis utilizes high‐time‐resolution measurements from SuperDARN high‐frequency coherent scatter radars operating in a special mode, sampling three “camping beams” approximately every 18 s. We focus on ULF perturbations within the Pc5 frequency range (1.6–6.7 mHz), estimating Joule heating rates from ionospheric electric fields derived from SuperDARN data and height‐integrated Pedersen conductance from empirical models. The analysis includes statistical characterization of Pc5 wave occurrence, electric fields, Joule heating rates, and azimuthal wave numbers. Our results reveal enhanced electric fields and Joule heating rates in the morning and pre‐midnight sectors, even though Pc5 wave occurrences peak in the afternoon. Joule heating is more pronounced in the high‐latitude morning sector during northward interplanetary magnetic field conditions, attributed to local time asymmetry in Pedersen conductance and Pc5 waves driven by Kelvin‐Helmholtz instability. Pc5 waves observed by multiple camping beams predominantly propagate westward at low azimuthal wave numbers , while high‐m waves propagate mainly eastward. Although Joule heating estimates may be underestimated due to assumptions about empirical conductance models and the underestimation of electric fields resulting from SuperDARN line‐of‐sight velocity measurements, these findings offer valuable insights into ULF wave‐related energy dissipation in the geospace system.
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- Award ID(s):
- 2307205
- PAR ID:
- 10581512
- Publisher / Repository:
- AGU
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Space Physics
- Volume:
- 130
- Issue:
- 3
- ISSN:
- 2169-9380
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1029/2024JA033452
