Prediction of Global Ionospheric Total Electron Content (TEC) Based on SAM‐ConvLSTM Model
Abstract This paper first applies a prediction model based on self‐attention memory ConvLSTM (SAM‐ConvLSTM) to predict the global ionospheric total electron content (TEC) maps with up to 1 day of lead time. We choose the global ionospheric TEC maps released by the Center for Orbit Determination in E...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-12-01
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| Series: | Space Weather |
| Online Access: | https://doi.org/10.1029/2023SW003707 |
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| _version_ | 1841536384993591296 |
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| author | Hanze Luo Yingkui Gong Si Chen Cheng Yu Guang Yang Fengzheng Yu Ziyue Hu Xiangwei Tian |
| author_facet | Hanze Luo Yingkui Gong Si Chen Cheng Yu Guang Yang Fengzheng Yu Ziyue Hu Xiangwei Tian |
| author_sort | Hanze Luo |
| collection | DOAJ |
| description | Abstract This paper first applies a prediction model based on self‐attention memory ConvLSTM (SAM‐ConvLSTM) to predict the global ionospheric total electron content (TEC) maps with up to 1 day of lead time. We choose the global ionospheric TEC maps released by the Center for Orbit Determination in Europe (CODE) as the training data set covering the period from 1999 to 2022. Besides that, we put several space environment data as additional multivariate‐features into the framework of the prediction model to enhance its forecasting ability. In order to confirm the efficiency of the proposed model, the other two prediction models based on convolutional long short‐term memory (LSTM) are used for comparison. The three models are trained and evaluated on the same data set. Results show that the proposed SAM‐ConvLSTM prediction model performs more accurately than the other two models, and more stably under space weather events. In order to assess the generalization capabilities of the proposed model amidst severe space weather occurrences, we selected the period of 22–25 April 2023, characterized by a potent geomagnetic storm, for experimental validation. Subsequently, we employed the 1‐day predicted global TEC products from the Center for Operational Products and Services (COPG) and the SAM‐ConvLSTM model to evaluate their respective forecasting prowess. The results show that the SAM‐ConvLSTM prediction model achieves lower prediction error. In one word, the ionospheric TEC prediction model proposed in this paper can establish the ionosphere TEC of spatio‐temporal data association for a long time, and realize high precision of prediction performance. |
| format | Article |
| id | doaj-art-16790516a4bd4121a4fa7bb47a71515b |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-16790516a4bd4121a4fa7bb47a71515b2025-01-14T16:30:45ZengWileySpace Weather1542-73902023-12-012112n/an/a10.1029/2023SW003707Prediction of Global Ionospheric Total Electron Content (TEC) Based on SAM‐ConvLSTM ModelHanze Luo0Yingkui Gong1Si Chen2Cheng Yu3Guang Yang4Fengzheng Yu5Ziyue Hu6Xiangwei Tian7Aerospace Information Research Institute Chinese Academy of Sciences Beijing ChinaAerospace Information Research Institute Chinese Academy of Sciences Beijing ChinaAerospace Information Research Institute Chinese Academy of Sciences Beijing ChinaAerospace Information Research Institute Chinese Academy of Sciences Beijing ChinaAerospace Information Research Institute Chinese Academy of Sciences Beijing ChinaAerospace Information Research Institute Chinese Academy of Sciences Beijing ChinaAerospace Information Research Institute Chinese Academy of Sciences Beijing ChinaAerospace Information Research Institute Chinese Academy of Sciences Beijing ChinaAbstract This paper first applies a prediction model based on self‐attention memory ConvLSTM (SAM‐ConvLSTM) to predict the global ionospheric total electron content (TEC) maps with up to 1 day of lead time. We choose the global ionospheric TEC maps released by the Center for Orbit Determination in Europe (CODE) as the training data set covering the period from 1999 to 2022. Besides that, we put several space environment data as additional multivariate‐features into the framework of the prediction model to enhance its forecasting ability. In order to confirm the efficiency of the proposed model, the other two prediction models based on convolutional long short‐term memory (LSTM) are used for comparison. The three models are trained and evaluated on the same data set. Results show that the proposed SAM‐ConvLSTM prediction model performs more accurately than the other two models, and more stably under space weather events. In order to assess the generalization capabilities of the proposed model amidst severe space weather occurrences, we selected the period of 22–25 April 2023, characterized by a potent geomagnetic storm, for experimental validation. Subsequently, we employed the 1‐day predicted global TEC products from the Center for Operational Products and Services (COPG) and the SAM‐ConvLSTM model to evaluate their respective forecasting prowess. The results show that the SAM‐ConvLSTM prediction model achieves lower prediction error. In one word, the ionospheric TEC prediction model proposed in this paper can establish the ionosphere TEC of spatio‐temporal data association for a long time, and realize high precision of prediction performance.https://doi.org/10.1029/2023SW003707 |
| spellingShingle | Hanze Luo Yingkui Gong Si Chen Cheng Yu Guang Yang Fengzheng Yu Ziyue Hu Xiangwei Tian Prediction of Global Ionospheric Total Electron Content (TEC) Based on SAM‐ConvLSTM Model Space Weather |
| title | Prediction of Global Ionospheric Total Electron Content (TEC) Based on SAM‐ConvLSTM Model |
| title_full | Prediction of Global Ionospheric Total Electron Content (TEC) Based on SAM‐ConvLSTM Model |
| title_fullStr | Prediction of Global Ionospheric Total Electron Content (TEC) Based on SAM‐ConvLSTM Model |
| title_full_unstemmed | Prediction of Global Ionospheric Total Electron Content (TEC) Based on SAM‐ConvLSTM Model |
| title_short | Prediction of Global Ionospheric Total Electron Content (TEC) Based on SAM‐ConvLSTM Model |
| title_sort | prediction of global ionospheric total electron content tec based on sam convlstm model |
| url | https://doi.org/10.1029/2023SW003707 |
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