High‐Precision Prediction of Ionospheric TEC in the China Region Based on CMONOC High‐Resolution Data and an Auxiliary Attention Temporal Convolutional Network
Abstract Accurate prediction of Total Electron Content (TEC) in the ionosphere is crucial for navigation, communication, and space weather forecasting. However, the Global Ionosphere Maps provided by the International GNSS Service have limitations in resolution and adaptability in the China region,...
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| Format: | Article |
| Language: | English |
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Wiley
2025-06-01
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| Series: | Journal of Geophysical Research: Machine Learning and Computation |
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| Online Access: | https://doi.org/10.1029/2025JH000608 |
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| author | Jianghe Chen Pan Xiong Haochen Wu Xiaoran Zhang Xuemin Zhang Rongzi Chai Ting Zhang Kaixin Wang Chaoyu Wang |
| author_facet | Jianghe Chen Pan Xiong Haochen Wu Xiaoran Zhang Xuemin Zhang Rongzi Chai Ting Zhang Kaixin Wang Chaoyu Wang |
| author_sort | Jianghe Chen |
| collection | DOAJ |
| description | Abstract Accurate prediction of Total Electron Content (TEC) in the ionosphere is crucial for navigation, communication, and space weather forecasting. However, the Global Ionosphere Maps provided by the International GNSS Service have limitations in resolution and adaptability in the China region, making high‐precision predictions difficult. This study constructs a high‐precision regional TEC map with 1° × 1° spatial resolution, 2‐hr temporal resolution, and coverage from 2019 to 2023, based on data from 270 Crustal Movement Observation Network of China (CMONOC) GNSS stations. At the data level, a non‐integrated spherical harmonic model and Differential Code Bias correction method are employed to significantly reduce interpolation errors and improve model accuracy. At the algorithmic level, an Auxiliary Attention Temporal Convolutional Network (AuxATTCN) model is proposed, integrating an auxiliary attention mechanism with a Temporal Convolutional Network (TCN) to effectively capture long‐term dependencies and dynamically incorporate external driving factors such as geomagnetic activity and solar radiation. Comparative analysis with multiple experiments under varying geomagnetic and solar conditions shows that the AuxATTCN model significantly outperforms traditional time‐series methods (such as ARIMA, Prophet), mainstream deep learning models (including ConvLSTM, CONGRU, and TCN), and international ionospheric models (IRI2020, NeQuick2) in terms of overall error, seasonal and diurnal variations, and prediction accuracy during geomagnetic storms and solar activity peaks. The results demonstrate that the synergistic optimization of high‐quality CMONOC data sets and innovative algorithms achieves exceptional spatiotemporal accuracy and robustness in TEC prediction for the China region, providing new insights and technical support for fields such as navigation, communication, and space weather forecasting. |
| format | Article |
| id | doaj-art-e44702b450e040baab6bf639afdce85f |
| institution | Kabale University |
| issn | 2993-5210 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Geophysical Research: Machine Learning and Computation |
| spelling | doaj-art-e44702b450e040baab6bf639afdce85f2025-08-20T03:27:37ZengWileyJournal of Geophysical Research: Machine Learning and Computation2993-52102025-06-0122n/an/a10.1029/2025JH000608High‐Precision Prediction of Ionospheric TEC in the China Region Based on CMONOC High‐Resolution Data and an Auxiliary Attention Temporal Convolutional NetworkJianghe Chen0Pan Xiong1Haochen Wu2Xiaoran Zhang3Xuemin Zhang4Rongzi Chai5Ting Zhang6Kaixin Wang7Chaoyu Wang8Institute of Earthquake Forecasting China Earthquake Administration Beijing ChinaInstitute of Earthquake Forecasting China Earthquake Administration Beijing ChinaInstitute of Earthquake Forecasting China Earthquake Administration Beijing ChinaInstitute of Earthquake Forecasting China Earthquake Administration Beijing ChinaInstitute of Earthquake Forecasting China Earthquake Administration Beijing ChinaInstitute of Earthquake Forecasting China Earthquake Administration Beijing ChinaInnovation Academy for Precision Measurement Science and Technology Chinese Academy of Sciences Wuhan ChinaSchool of Civil Engineering and Geomatics Shandong University of Technology Zibo ChinaTianjin Key Lab for Advanced Signal Processing Civil Aviation University of China Tianjin ChinaAbstract Accurate prediction of Total Electron Content (TEC) in the ionosphere is crucial for navigation, communication, and space weather forecasting. However, the Global Ionosphere Maps provided by the International GNSS Service have limitations in resolution and adaptability in the China region, making high‐precision predictions difficult. This study constructs a high‐precision regional TEC map with 1° × 1° spatial resolution, 2‐hr temporal resolution, and coverage from 2019 to 2023, based on data from 270 Crustal Movement Observation Network of China (CMONOC) GNSS stations. At the data level, a non‐integrated spherical harmonic model and Differential Code Bias correction method are employed to significantly reduce interpolation errors and improve model accuracy. At the algorithmic level, an Auxiliary Attention Temporal Convolutional Network (AuxATTCN) model is proposed, integrating an auxiliary attention mechanism with a Temporal Convolutional Network (TCN) to effectively capture long‐term dependencies and dynamically incorporate external driving factors such as geomagnetic activity and solar radiation. Comparative analysis with multiple experiments under varying geomagnetic and solar conditions shows that the AuxATTCN model significantly outperforms traditional time‐series methods (such as ARIMA, Prophet), mainstream deep learning models (including ConvLSTM, CONGRU, and TCN), and international ionospheric models (IRI2020, NeQuick2) in terms of overall error, seasonal and diurnal variations, and prediction accuracy during geomagnetic storms and solar activity peaks. The results demonstrate that the synergistic optimization of high‐quality CMONOC data sets and innovative algorithms achieves exceptional spatiotemporal accuracy and robustness in TEC prediction for the China region, providing new insights and technical support for fields such as navigation, communication, and space weather forecasting.https://doi.org/10.1029/2025JH000608ionosphericTECdeep learningAuxATTCNCMONOCforecasting |
| spellingShingle | Jianghe Chen Pan Xiong Haochen Wu Xiaoran Zhang Xuemin Zhang Rongzi Chai Ting Zhang Kaixin Wang Chaoyu Wang High‐Precision Prediction of Ionospheric TEC in the China Region Based on CMONOC High‐Resolution Data and an Auxiliary Attention Temporal Convolutional Network Journal of Geophysical Research: Machine Learning and Computation ionospheric TEC deep learning AuxATTCN CMONOC forecasting |
| title | High‐Precision Prediction of Ionospheric TEC in the China Region Based on CMONOC High‐Resolution Data and an Auxiliary Attention Temporal Convolutional Network |
| title_full | High‐Precision Prediction of Ionospheric TEC in the China Region Based on CMONOC High‐Resolution Data and an Auxiliary Attention Temporal Convolutional Network |
| title_fullStr | High‐Precision Prediction of Ionospheric TEC in the China Region Based on CMONOC High‐Resolution Data and an Auxiliary Attention Temporal Convolutional Network |
| title_full_unstemmed | High‐Precision Prediction of Ionospheric TEC in the China Region Based on CMONOC High‐Resolution Data and an Auxiliary Attention Temporal Convolutional Network |
| title_short | High‐Precision Prediction of Ionospheric TEC in the China Region Based on CMONOC High‐Resolution Data and an Auxiliary Attention Temporal Convolutional Network |
| title_sort | high precision prediction of ionospheric tec in the china region based on cmonoc high resolution data and an auxiliary attention temporal convolutional network |
| topic | ionospheric TEC deep learning AuxATTCN CMONOC forecasting |
| url | https://doi.org/10.1029/2025JH000608 |
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