Deep Learning-Based Vertical Decomposition of Ionospheric TEC into Layered Electron Density Profiles
This study proposes a deep learning-based vertical decomposition model for ionospheric Total Electron Content (TEC), which establishes a nonlinear mapping from macroscale TEC data to vertically layered electron density (Ne) spanning 60–800 km by integrating geomagnetic indices (AE, SYM-H) and solar...
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MDPI AG
2025-05-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/16/5/598 |
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| author | Jialiang Zhang Jianxiang Zhang Zhou Chen Jingsong Wang Cunqun Fan Yan Guo |
| author_facet | Jialiang Zhang Jianxiang Zhang Zhou Chen Jingsong Wang Cunqun Fan Yan Guo |
| author_sort | Jialiang Zhang |
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| description | This study proposes a deep learning-based vertical decomposition model for ionospheric Total Electron Content (TEC), which establishes a nonlinear mapping from macroscale TEC data to vertically layered electron density (Ne) spanning 60–800 km by integrating geomagnetic indices (AE, SYM-H) and solar activity parameters (F10.7). Utilizing global TEC grid data (spatiotemporal resolution: 1 h/5.625° × 2.8125°) provided by the International GNSS Service (IGS), a Multilayer Perceptron (MLP) model was developed, taking spatiotemporal coordinates, altitude, and space environment parameters as inputs to predict logarithmic electron density ln(Ne). Experimental validation against COSMIC-2 radio occultation observations in 2019 demonstrates the model’s capability to capture ionospheric vertical structures, with a prediction performance significantly outperforming the International Reference Ionosphere model IRI-2020: root mean square error (RMSE) decreased by 34.16%, and the coefficient of determination (R<sup>2</sup>) increased by 28.45%. This method overcomes the reliance of traditional electron density inversion on costly radar or satellite observations, enabling high-spatiotemporal-resolution global ionospheric profile reconstruction using widely available GNSS-TEC data. It provides a novel tool for space weather warning and shortwave communication optimization. Current limitations include insufficient physical interpretability and prediction uncertainty in GNSS-sparse regions, which could be mitigated in future work through the integration of physical constraints and multi-source data assimilation. |
| format | Article |
| id | doaj-art-86a6e13837ab442da12ef67c72270cdb |
| institution | DOAJ |
| issn | 2073-4433 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Atmosphere |
| spelling | doaj-art-86a6e13837ab442da12ef67c72270cdb2025-08-20T03:14:35ZengMDPI AGAtmosphere2073-44332025-05-0116559810.3390/atmos16050598Deep Learning-Based Vertical Decomposition of Ionospheric TEC into Layered Electron Density ProfilesJialiang Zhang0Jianxiang Zhang1Zhou Chen2Jingsong Wang3Cunqun Fan4Yan Guo5School of Mathematics and Computer Sciences, Nanchang University, Nanchang 330031, ChinaInstitute of Space Science and Technology, Nanchang University, Nanchang 330031, ChinaInstitute of Space Science and Technology, Nanchang University, Nanchang 330031, ChinaKey Laboratory of Space Weather, National Center for Space Weather, China, Meteorological Administration, Beijing 100081, ChinaKey Laboratory of Space Weather, National Center for Space Weather, China, Meteorological Administration, Beijing 100081, ChinaKey Laboratory of Space Weather, National Center for Space Weather, China, Meteorological Administration, Beijing 100081, ChinaThis study proposes a deep learning-based vertical decomposition model for ionospheric Total Electron Content (TEC), which establishes a nonlinear mapping from macroscale TEC data to vertically layered electron density (Ne) spanning 60–800 km by integrating geomagnetic indices (AE, SYM-H) and solar activity parameters (F10.7). Utilizing global TEC grid data (spatiotemporal resolution: 1 h/5.625° × 2.8125°) provided by the International GNSS Service (IGS), a Multilayer Perceptron (MLP) model was developed, taking spatiotemporal coordinates, altitude, and space environment parameters as inputs to predict logarithmic electron density ln(Ne). Experimental validation against COSMIC-2 radio occultation observations in 2019 demonstrates the model’s capability to capture ionospheric vertical structures, with a prediction performance significantly outperforming the International Reference Ionosphere model IRI-2020: root mean square error (RMSE) decreased by 34.16%, and the coefficient of determination (R<sup>2</sup>) increased by 28.45%. This method overcomes the reliance of traditional electron density inversion on costly radar or satellite observations, enabling high-spatiotemporal-resolution global ionospheric profile reconstruction using widely available GNSS-TEC data. It provides a novel tool for space weather warning and shortwave communication optimization. Current limitations include insufficient physical interpretability and prediction uncertainty in GNSS-sparse regions, which could be mitigated in future work through the integration of physical constraints and multi-source data assimilation.https://www.mdpi.com/2073-4433/16/5/598total electron contentelectron density decompositiondeep learningionospheric modeling |
| spellingShingle | Jialiang Zhang Jianxiang Zhang Zhou Chen Jingsong Wang Cunqun Fan Yan Guo Deep Learning-Based Vertical Decomposition of Ionospheric TEC into Layered Electron Density Profiles Atmosphere total electron content electron density decomposition deep learning ionospheric modeling |
| title | Deep Learning-Based Vertical Decomposition of Ionospheric TEC into Layered Electron Density Profiles |
| title_full | Deep Learning-Based Vertical Decomposition of Ionospheric TEC into Layered Electron Density Profiles |
| title_fullStr | Deep Learning-Based Vertical Decomposition of Ionospheric TEC into Layered Electron Density Profiles |
| title_full_unstemmed | Deep Learning-Based Vertical Decomposition of Ionospheric TEC into Layered Electron Density Profiles |
| title_short | Deep Learning-Based Vertical Decomposition of Ionospheric TEC into Layered Electron Density Profiles |
| title_sort | deep learning based vertical decomposition of ionospheric tec into layered electron density profiles |
| topic | total electron content electron density decomposition deep learning ionospheric modeling |
| url | https://www.mdpi.com/2073-4433/16/5/598 |
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