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Abstract A new technique has been developed to determine the high‐latitude electric potential from observed field‐aligned currents (FACs) and modeled ionospheric conductances. FACs are observed by the Active Magnetosphere and Planetary Electrodynamics Response Experiment (AMPERE), while the conductances are modeled by Sami3 is Also a Model of the Ionosphere (SAMI3). This is a development of the Magnetosphere‐Ionosphere Coupling approach first demonstrated by Merkin and Lyon (2010),https://doi.org/10.1029/2010ja015461. An advantage of using SAMI3 is that the model can be used to predict total electron content (TEC), based on the AMPERE‐derived potential solutions. 23 May 2014 is chosen as a case study to assess the new technique for a moderately disturbed case (min Dst: −36 nT, max AE: 909 nT) with good GPS data coverage. The new AMPERE/SAMI3 solutions are compared against independent GPS‐based TEC observations from the Multi‐Instrument Data Analysis Software (MIDAS) by Mitchell and Spencer (2003), and against Defense Meteorological Satellite Program (DMSP) ion drift data. The comparison shows excellent agreement between the location of the tongue of ionization in the MIDAS GPS data and the AMPERE/SAMI3 potential pattern, and good overall agreement with DMSP drifts. SAMI3 predictions of high‐latitude TEC are much improved when using the AMPERE‐derived potential as compared to Weimer's (2005),https://doi.org/10.1029/2005ja011270model. The two potential models have substantial differences, with Weimer producing an average 77 kV cross‐cap potential versus 60 kV for the AMPERE‐derived potential. The results indicate that the 66‐satellite Iridium constellation provides sufficient resolution of FACs to estimate large‐scale ionospheric convection as it impacts TEC.
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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 (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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- PAR ID:
- 10499842
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Space Weather
- Volume:
- 22
- Issue:
- 4
- ISSN:
- 1542-7390
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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