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...
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| Format: | Article |
| Language: | English |
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Wiley
2024-04-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2023SW003683 |
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| author | V. Sai Gowtam Hyunju Connor Bharat S. R. Kunduri Joachim Raeder Karl M. Laundal S. Tulasi Ram Dogacan S. Ozturk Donald Hampton Shibaji Chakraborty Charles Owolabi Amy Keesee |
| author_facet | V. Sai Gowtam Hyunju Connor Bharat S. R. Kunduri Joachim Raeder Karl M. Laundal S. Tulasi Ram Dogacan S. Ozturk Donald Hampton Shibaji Chakraborty Charles Owolabi Amy Keesee |
| author_sort | V. Sai Gowtam |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e3b137bce48b4f238986d46c89b971fd |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2024-04-01 |
| publisher | Wiley |
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| series | Space Weather |
| spelling | doaj-art-e3b137bce48b4f238986d46c89b971fd2025-01-14T16:27:27ZengWileySpace Weather1542-73902024-04-01224n/an/a10.1029/2023SW003683Calculating the High‐Latitude Ionospheric Electrodynamics Using a Machine Learning‐Based Field‐Aligned Current ModelV. Sai Gowtam0Hyunju Connor1Bharat S. R. Kunduri2Joachim Raeder3Karl M. Laundal4S. Tulasi Ram5Dogacan S. Ozturk6Donald Hampton7Shibaji Chakraborty8Charles Owolabi9Amy Keesee10Geophysical Institute University of Alaska Fairbanks Fairbanks AK USAGoddard Space Flight Center NASA Greenbelt MD USAVirginia Tech Blacksburg VA USAUniversity of New Hampshire Durham NH USADepartment of Physics and Technology University of Bergen Bergen NorwayIndian Institute of Geomagnetism Navi Mumbai IndiaGeophysical Institute University of Alaska Fairbanks Fairbanks AK USAGeophysical Institute University of Alaska Fairbanks Fairbanks AK USAVirginia Tech Blacksburg VA USAGeophysical Institute University of Alaska Fairbanks Fairbanks AK USAUniversity of New Hampshire Durham NH USAAbstract 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.https://doi.org/10.1029/2023SW003683auroral electrodynamicsmagnetosphere‐ionosphere couplingcross polar cap potentialfield aligned currentsauroral conductancemachine learning |
| spellingShingle | V. Sai Gowtam Hyunju Connor Bharat S. R. Kunduri Joachim Raeder Karl M. Laundal S. Tulasi Ram Dogacan S. Ozturk Donald Hampton Shibaji Chakraborty Charles Owolabi Amy Keesee Calculating the High‐Latitude Ionospheric Electrodynamics Using a Machine Learning‐Based Field‐Aligned Current Model Space Weather auroral electrodynamics magnetosphere‐ionosphere coupling cross polar cap potential field aligned currents auroral conductance machine learning |
| title | Calculating the High‐Latitude Ionospheric Electrodynamics Using a Machine Learning‐Based Field‐Aligned Current Model |
| title_full | Calculating the High‐Latitude Ionospheric Electrodynamics Using a Machine Learning‐Based Field‐Aligned Current Model |
| title_fullStr | Calculating the High‐Latitude Ionospheric Electrodynamics Using a Machine Learning‐Based Field‐Aligned Current Model |
| title_full_unstemmed | Calculating the High‐Latitude Ionospheric Electrodynamics Using a Machine Learning‐Based Field‐Aligned Current Model |
| title_short | Calculating the High‐Latitude Ionospheric Electrodynamics Using a Machine Learning‐Based Field‐Aligned Current Model |
| title_sort | calculating the high latitude ionospheric electrodynamics using a machine learning based field aligned current model |
| topic | auroral electrodynamics magnetosphere‐ionosphere coupling cross polar cap potential field aligned currents auroral conductance machine learning |
| url | https://doi.org/10.1029/2023SW003683 |
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