Ionospheric Response to the 24–27 February 2023 Solar Flare and Geomagnetic Storms Over the European Region Using a Machine Learning–Based Tomographic Technique
Abstract Two solar flares accompanied by coronal mass ejections (CMEs) occurred on 24–25 February (DOY 055–056), 2023, resulting in a large magnetic storm on DOY 058. We reconstructed the ionospheric electron density (IED) in Europe to analyze the spatial distribution of ionosphere and its temporal...
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Wiley
2025-01-01
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| Online Access: | https://doi.org/10.1029/2024SW004146 |
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| author | Ting Li Dunyong Zheng Changyong He Fei Ye Pengfei Yuan Yibin Yao Mengguang Liao Jian Xie |
| author_facet | Ting Li Dunyong Zheng Changyong He Fei Ye Pengfei Yuan Yibin Yao Mengguang Liao Jian Xie |
| author_sort | Ting Li |
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| description | Abstract Two solar flares accompanied by coronal mass ejections (CMEs) occurred on 24–25 February (DOY 055–056), 2023, resulting in a large magnetic storm on DOY 058. We reconstructed the ionospheric electron density (IED) in Europe to analyze the spatial distribution of ionosphere and its temporal evolution during this period. Computerized ionospheric tomography based on machine learning (CIT‐ML) was used to predict the IEDs of unobserved voxels. The IEDs were examined using observation data from the Swarm satellite. The CIT‐ML accuracy was 28.3% higher than the improved algebraic reconstruction technique with relaxation factor automatic search technology (IART‐AS), which effectively improved the typical ill‐posed problem of CIT. The first flare generated the Bz component of the interplanetary magnetic field (IMF), which continued southward for 13 hr, causing a small magnetic storm before the second flare occurred, resulting in an increased nighttime IED and nighttime medium‐scale traveling ionospheric disturbance (MSTID). The vertical total electron content (VTEC) and IED declined in the early stages of the main phase of the large magnetic storm, but later increased, indicating that negative‐positive biphasic storms were occurring in the ionosphere that altered the ionospheric daily cycle, resulting in the peak of the ionosphere being advanced by approximately 1.5 hr. The storms also caused nighttime MSTIDs during the main phase (DOY 057 at night) and the recovery phase (DOY 058 at night). To investigate the mechanisms of these results, we conducted a term analysis of the ion continuity equation using the Thermosphere Ionosphere Electrodynamics General Circulation Model (TIEGCM). The analysis showed that ambipolar diffusion driver nighttime MSTIDs during flares, while increased geomagnetic disturbances amplify the effects of neutral wind transport, E × B drift and chemical reactions during magnetic storms. These combined effects offset the alternating positive and negative structures induced by ambipolar diffusion, becoming the main cause of electron density variations during ionospheric storms. |
| format | Article |
| id | doaj-art-6f6af2033f67469c92e72440b145435f |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
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| series | Space Weather |
| spelling | doaj-art-6f6af2033f67469c92e72440b145435f2025-01-28T10:40:45ZengWileySpace Weather1542-73902025-01-01231n/an/a10.1029/2024SW004146Ionospheric Response to the 24–27 February 2023 Solar Flare and Geomagnetic Storms Over the European Region Using a Machine Learning–Based Tomographic TechniqueTing Li0Dunyong Zheng1Changyong He2Fei Ye3Pengfei Yuan4Yibin Yao5Mengguang Liao6Jian Xie7School of Earth Sciences and Spatial Information Engineering Hunan University of Science and Technology Xiangtan ChinaSchool of Earth Sciences and Spatial Information Engineering Hunan University of Science and Technology Xiangtan ChinaSchool of Earth Sciences and Spatial Information Engineering Hunan University of Science and Technology Xiangtan ChinaBeiDou High‐Precision Satellite Navigation and Location Service Hunan Engineering Research Center Hunan Institute of Geomatics Sciences and Technology Changsha ChinaSchool of Earth Sciences and Spatial Information Engineering Hunan University of Science and Technology Xiangtan ChinaSchool of Geodesy and Geomatics Wuhan University Wuhan ChinaSchool of Earth Sciences and Spatial Information Engineering Hunan University of Science and Technology Xiangtan ChinaSchool of Earth Sciences and Spatial Information Engineering Hunan University of Science and Technology Xiangtan ChinaAbstract Two solar flares accompanied by coronal mass ejections (CMEs) occurred on 24–25 February (DOY 055–056), 2023, resulting in a large magnetic storm on DOY 058. We reconstructed the ionospheric electron density (IED) in Europe to analyze the spatial distribution of ionosphere and its temporal evolution during this period. Computerized ionospheric tomography based on machine learning (CIT‐ML) was used to predict the IEDs of unobserved voxels. The IEDs were examined using observation data from the Swarm satellite. The CIT‐ML accuracy was 28.3% higher than the improved algebraic reconstruction technique with relaxation factor automatic search technology (IART‐AS), which effectively improved the typical ill‐posed problem of CIT. The first flare generated the Bz component of the interplanetary magnetic field (IMF), which continued southward for 13 hr, causing a small magnetic storm before the second flare occurred, resulting in an increased nighttime IED and nighttime medium‐scale traveling ionospheric disturbance (MSTID). The vertical total electron content (VTEC) and IED declined in the early stages of the main phase of the large magnetic storm, but later increased, indicating that negative‐positive biphasic storms were occurring in the ionosphere that altered the ionospheric daily cycle, resulting in the peak of the ionosphere being advanced by approximately 1.5 hr. The storms also caused nighttime MSTIDs during the main phase (DOY 057 at night) and the recovery phase (DOY 058 at night). To investigate the mechanisms of these results, we conducted a term analysis of the ion continuity equation using the Thermosphere Ionosphere Electrodynamics General Circulation Model (TIEGCM). The analysis showed that ambipolar diffusion driver nighttime MSTIDs during flares, while increased geomagnetic disturbances amplify the effects of neutral wind transport, E × B drift and chemical reactions during magnetic storms. These combined effects offset the alternating positive and negative structures induced by ambipolar diffusion, becoming the main cause of electron density variations during ionospheric storms.https://doi.org/10.1029/2024SW004146computerized ionospheric tomographymachine learningflaresmagnetic stormsionospheric stormsmedium‐scale traveling ionospheric disturbance |
| spellingShingle | Ting Li Dunyong Zheng Changyong He Fei Ye Pengfei Yuan Yibin Yao Mengguang Liao Jian Xie Ionospheric Response to the 24–27 February 2023 Solar Flare and Geomagnetic Storms Over the European Region Using a Machine Learning–Based Tomographic Technique Space Weather computerized ionospheric tomography machine learning flares magnetic storms ionospheric storms medium‐scale traveling ionospheric disturbance |
| title | Ionospheric Response to the 24–27 February 2023 Solar Flare and Geomagnetic Storms Over the European Region Using a Machine Learning–Based Tomographic Technique |
| title_full | Ionospheric Response to the 24–27 February 2023 Solar Flare and Geomagnetic Storms Over the European Region Using a Machine Learning–Based Tomographic Technique |
| title_fullStr | Ionospheric Response to the 24–27 February 2023 Solar Flare and Geomagnetic Storms Over the European Region Using a Machine Learning–Based Tomographic Technique |
| title_full_unstemmed | Ionospheric Response to the 24–27 February 2023 Solar Flare and Geomagnetic Storms Over the European Region Using a Machine Learning–Based Tomographic Technique |
| title_short | Ionospheric Response to the 24–27 February 2023 Solar Flare and Geomagnetic Storms Over the European Region Using a Machine Learning–Based Tomographic Technique |
| title_sort | ionospheric response to the 24 27 february 2023 solar flare and geomagnetic storms over the european region using a machine learning based tomographic technique |
| topic | computerized ionospheric tomography machine learning flares magnetic storms ionospheric storms medium‐scale traveling ionospheric disturbance |
| url | https://doi.org/10.1029/2024SW004146 |
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