Deep Learning-Based Vertical Decomposition of Ionospheric TEC into Layered Electron Density Profiles

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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Bibliographic Details
Main Authors: Jialiang Zhang, Jianxiang Zhang, Zhou Chen, Jingsong Wang, Cunqun Fan, Yan Guo
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Atmosphere
Subjects:
total electron content
electron density decomposition
deep learning
ionospheric modeling
Online Access:https://www.mdpi.com/2073-4433/16/5/598
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Summary: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.
ISSN:2073-4433

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