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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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Interplay of Gravity Waves and Disturbance Electric Fields to the Abnormal Ionospheric Variations During the 11 May 2024 Superstorm
Abstract The strongest geomagnetic storm in the preceding two decades occurred in May 2024. Over these years, ground‐based observational capabilities have been significantly enhanced to monitor the ionospheric weather. Notably, the newly established Sanya incoherent scatter radar (SYISR) (Yue, Wan, Ning, & Jin, 2022,https://doi.org/10.1038/s41550‐022‐01684‐1), one of the critical infrastructures of the Chinese “Meridian Project,” provides multiple parameter measurements in the upper atmosphere at low latitudes over Asian longitudies. Unique ionospheric changes on superstorm day 11 May were first recorded by the SYISR experiments and the geostationary satellite (GEO) total electron content (TEC) network over the Asian sector. The electron density or TEC displayed wavelike structures rather than a regular diurnal pattern. Surprisingly, two humps, a common feature in the daytime equatorial ionization anomaly structure, disappeared. The SYISR observations revealed that multiple wind surges accompanied the downward phase propagation caused by atmospheric gravity waves (AGWs) originating from auroral zones. Meanwhile, strong upward and large downward drifts were respectively observed in the daytime and around sunset. The Thermosphere‐Ionosphere Electrodynamics Global Circulation Model (TIEGCM) simulations demonstrated that abnormal ionospheric changes were attributed to meridional wind disturbances associated with AGWs and recurrent penetration electric fields corresponding to largerBzsouthward excursions and disturbance dynamo. The complicated interplay between AGWs and disturbance electric fields contributed to this unique ionospheric variation.
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- PAR ID:
- 10612069
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- AGU Advances
- Volume:
- 6
- Issue:
- 1
- ISSN:
- 2576-604X
- 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/10.1029/2024AV001379
