A Predictive Model of the Position of Plasmapause Based on Lunar Phase and Deep Learning Framework
Abstract The plasmapause position is crucial for understanding magnetospheric dynamics and space weather forecasting. This study pioneers the integration of lunar phase (LP) into plasmapause modeling using two neural network architectures (BP and fully connected neural network) and a large database...
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-07-01
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| Series: | Geophysical Research Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2025GL116485 |
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| _version_ | 1849245672455274496 |
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| author | Yajun Li Chao Xiao Quanqi Shi Hongtao Huang Huizi Wang Anmin Tian Die Duan Ganming Ren Tao Tang Yang Lin Chenghao Li Jiajia Suo |
| author_facet | Yajun Li Chao Xiao Quanqi Shi Hongtao Huang Huizi Wang Anmin Tian Die Duan Ganming Ren Tao Tang Yang Lin Chenghao Li Jiajia Suo |
| author_sort | Yajun Li |
| collection | DOAJ |
| description | Abstract The plasmapause position is crucial for understanding magnetospheric dynamics and space weather forecasting. This study pioneers the integration of lunar phase (LP) into plasmapause modeling using two neural network architectures (BP and fully connected neural network) and a large database of 37,869 crossing events from 1977 to 2015. Our LP‐coupled models achieved a 15% reduction in root mean square error compared to prior artificial neural network models and outperforms empirical benchmarks (e.g., new solar wind‐driven global dynamic plasmapause model), with optimal performance in the dusk sector (18–24 magnetic local time) where lunar tidal effects peak. This work establishes LP as a critical modulator of plasmapause dynamics, challenging the conventional solar wind‐driven paradigm. The neural network framework combining LP modulation with solar wind/geomagnetic parameters yields significant improvements in global plasmapause prediction accuracy, providing a foundation for more precise space weather forecasting. Future research could further refine predictions by incorporating real‐time tilt angle data and coupling with first‐principles simulations of neutral atmosphere tides. |
| format | Article |
| id | doaj-art-8a7f542587a24b6291ffc88b3fcef6db |
| institution | Kabale University |
| issn | 0094-8276 1944-8007 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geophysical Research Letters |
| spelling | doaj-art-8a7f542587a24b6291ffc88b3fcef6db2025-08-20T03:58:44ZengWileyGeophysical Research Letters0094-82761944-80072025-07-015214n/an/a10.1029/2025GL116485A Predictive Model of the Position of Plasmapause Based on Lunar Phase and Deep Learning FrameworkYajun Li0Chao Xiao1Quanqi Shi2Hongtao Huang3Huizi Wang4Anmin Tian5Die Duan6Ganming Ren7Tao Tang8Yang Lin9Chenghao Li10Jiajia Suo11Institute of Space Sciences Shandong University Weihai ChinaInstitute of Space Sciences Shandong University Weihai ChinaInstitute of Space Sciences Shandong University Weihai ChinaCollege of Advanced Interdisciplinary Studies National University of Defense Technology Changsha ChinaInstitute of Space Sciences Shandong University Weihai ChinaInstitute of Space Sciences Shandong University Weihai ChinaCollege of Advanced Interdisciplinary Studies National University of Defense Technology Changsha ChinaCollege of Advanced Interdisciplinary Studies National University of Defense Technology Changsha ChinaInstitute of Space Sciences Shandong University Weihai ChinaCollege of Advanced Interdisciplinary Studies National University of Defense Technology Changsha ChinaCollege of Advanced Interdisciplinary Studies National University of Defense Technology Changsha ChinaInstitute of Space Sciences Shandong University Weihai ChinaAbstract The plasmapause position is crucial for understanding magnetospheric dynamics and space weather forecasting. This study pioneers the integration of lunar phase (LP) into plasmapause modeling using two neural network architectures (BP and fully connected neural network) and a large database of 37,869 crossing events from 1977 to 2015. Our LP‐coupled models achieved a 15% reduction in root mean square error compared to prior artificial neural network models and outperforms empirical benchmarks (e.g., new solar wind‐driven global dynamic plasmapause model), with optimal performance in the dusk sector (18–24 magnetic local time) where lunar tidal effects peak. This work establishes LP as a critical modulator of plasmapause dynamics, challenging the conventional solar wind‐driven paradigm. The neural network framework combining LP modulation with solar wind/geomagnetic parameters yields significant improvements in global plasmapause prediction accuracy, providing a foundation for more precise space weather forecasting. Future research could further refine predictions by incorporating real‐time tilt angle data and coupling with first‐principles simulations of neutral atmosphere tides.https://doi.org/10.1029/2025GL116485lunar phaseplasmapauseneural networkspace weather forecast |
| spellingShingle | Yajun Li Chao Xiao Quanqi Shi Hongtao Huang Huizi Wang Anmin Tian Die Duan Ganming Ren Tao Tang Yang Lin Chenghao Li Jiajia Suo A Predictive Model of the Position of Plasmapause Based on Lunar Phase and Deep Learning Framework Geophysical Research Letters lunar phase plasmapause neural network space weather forecast |
| title | A Predictive Model of the Position of Plasmapause Based on Lunar Phase and Deep Learning Framework |
| title_full | A Predictive Model of the Position of Plasmapause Based on Lunar Phase and Deep Learning Framework |
| title_fullStr | A Predictive Model of the Position of Plasmapause Based on Lunar Phase and Deep Learning Framework |
| title_full_unstemmed | A Predictive Model of the Position of Plasmapause Based on Lunar Phase and Deep Learning Framework |
| title_short | A Predictive Model of the Position of Plasmapause Based on Lunar Phase and Deep Learning Framework |
| title_sort | predictive model of the position of plasmapause based on lunar phase and deep learning framework |
| topic | lunar phase plasmapause neural network space weather forecast |
| url | https://doi.org/10.1029/2025GL116485 |
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