ED‐Autoformer: A New Model for Precise Global TEC Forecast
Abstract Total electron content (TEC) is a key parameter for characterizing ionospheric morphology and significantly impacts the Global Navigation Satellite System. The ionosphere responds dramatically to solar and geomagnetic activity, leading to substantial TEC fluctuations and disturbances. To im...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2025-06-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2025SW004356 |
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| _version_ | 1849415811651862528 |
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| author | Jiawei Zhou Hongtao Cai Xu Yan Hong‐wen Xu Kun Hu Chao Xiong |
| author_facet | Jiawei Zhou Hongtao Cai Xu Yan Hong‐wen Xu Kun Hu Chao Xiong |
| author_sort | Jiawei Zhou |
| collection | DOAJ |
| description | Abstract Total electron content (TEC) is a key parameter for characterizing ionospheric morphology and significantly impacts the Global Navigation Satellite System. The ionosphere responds dramatically to solar and geomagnetic activity, leading to substantial TEC fluctuations and disturbances. To improve TEC prediction accuracy, we propose Encoder‐Decoder (ED)‐Autoformer, a novel model combining ED structure with the Autoformer model. The model integrates time series data with TEC to enable 24‐hr forecasts of both disturbance storm time index (Dst) and TEC. Evaluated on global ionospheric maps TEC, our model achieves a 12.0% improvement (0.51 TECu) in the root mean squared error (RMSE) during solar maximum and an 8.9% improvement (0.14 TECu) in RMSE during solar minimum compared to the Convolutional Long‐Short‐Term Memory (ConvLSTM) method. Furthermore, ED‐Autoformer shows superior computational efficiency with 45.4% faster inference speed compared to ED‐ConvLSTM. We further analyzed TEC disturbances during geomagnetic storm periods. For the geomagnetic storm on 20 September 2015, the RMSE remained below 3.50 TECu for most periods, peaking at 5.50 TECu during the main phase. These results demonstrate the robustness of the model in accurately predicting TEC disturbances during geomagnetic storm periods. |
| format | Article |
| id | doaj-art-744ebff09a1c4a79ba33d134b3e7077e |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-744ebff09a1c4a79ba33d134b3e7077e2025-08-20T03:33:25ZengWileySpace Weather1542-73902025-06-01236n/an/a10.1029/2025SW004356ED‐Autoformer: A New Model for Precise Global TEC ForecastJiawei Zhou0Hongtao Cai1Xu Yan2Hong‐wen Xu3Kun Hu4Chao Xiong5School of Earth and Space Science and Technology Wuhan University Wuhan ChinaSchool of Earth and Space Science and Technology Wuhan University Wuhan ChinaSchool of Earth and Space Science and Technology Wuhan University Wuhan ChinaSchool of Earth and Space Science and Technology Wuhan University Wuhan ChinaSchool of Earth and Space Science and Technology Wuhan University Wuhan ChinaSchool of Earth and Space Science and Technology Wuhan University Wuhan ChinaAbstract Total electron content (TEC) is a key parameter for characterizing ionospheric morphology and significantly impacts the Global Navigation Satellite System. The ionosphere responds dramatically to solar and geomagnetic activity, leading to substantial TEC fluctuations and disturbances. To improve TEC prediction accuracy, we propose Encoder‐Decoder (ED)‐Autoformer, a novel model combining ED structure with the Autoformer model. The model integrates time series data with TEC to enable 24‐hr forecasts of both disturbance storm time index (Dst) and TEC. Evaluated on global ionospheric maps TEC, our model achieves a 12.0% improvement (0.51 TECu) in the root mean squared error (RMSE) during solar maximum and an 8.9% improvement (0.14 TECu) in RMSE during solar minimum compared to the Convolutional Long‐Short‐Term Memory (ConvLSTM) method. Furthermore, ED‐Autoformer shows superior computational efficiency with 45.4% faster inference speed compared to ED‐ConvLSTM. We further analyzed TEC disturbances during geomagnetic storm periods. For the geomagnetic storm on 20 September 2015, the RMSE remained below 3.50 TECu for most periods, peaking at 5.50 TECu during the main phase. These results demonstrate the robustness of the model in accurately predicting TEC disturbances during geomagnetic storm periods.https://doi.org/10.1029/2025SW004356ionospheric stormTEC predictionmachine learning |
| spellingShingle | Jiawei Zhou Hongtao Cai Xu Yan Hong‐wen Xu Kun Hu Chao Xiong ED‐Autoformer: A New Model for Precise Global TEC Forecast Space Weather ionospheric storm TEC prediction machine learning |
| title | ED‐Autoformer: A New Model for Precise Global TEC Forecast |
| title_full | ED‐Autoformer: A New Model for Precise Global TEC Forecast |
| title_fullStr | ED‐Autoformer: A New Model for Precise Global TEC Forecast |
| title_full_unstemmed | ED‐Autoformer: A New Model for Precise Global TEC Forecast |
| title_short | ED‐Autoformer: A New Model for Precise Global TEC Forecast |
| title_sort | ed autoformer a new model for precise global tec forecast |
| topic | ionospheric storm TEC prediction machine learning |
| url | https://doi.org/10.1029/2025SW004356 |
| work_keys_str_mv | AT jiaweizhou edautoformeranewmodelforpreciseglobaltecforecast AT hongtaocai edautoformeranewmodelforpreciseglobaltecforecast AT xuyan edautoformeranewmodelforpreciseglobaltecforecast AT hongwenxu edautoformeranewmodelforpreciseglobaltecforecast AT kunhu edautoformeranewmodelforpreciseglobaltecforecast AT chaoxiong edautoformeranewmodelforpreciseglobaltecforecast |