Prediction of Global Ionospheric TEC Based on Deep Learning
Abstract The accurate prediction of ionospheric Total Electron Content (TEC) is important for global navigation satellite systems (GNSS), satellite communications and other space communications applications. In this study, a prediction model of global IGS‐TEC maps are established based on testing se...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-04-01
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| Series: | Space Weather |
| Online Access: | https://doi.org/10.1029/2021SW002854 |
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| _version_ | 1841536437878521856 |
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| author | Zhou Chen Wenti Liao Haimeng Li Jinsong Wang Xiaohua Deng Sheng Hong |
| author_facet | Zhou Chen Wenti Liao Haimeng Li Jinsong Wang Xiaohua Deng Sheng Hong |
| author_sort | Zhou Chen |
| collection | DOAJ |
| description | Abstract The accurate prediction of ionospheric Total Electron Content (TEC) is important for global navigation satellite systems (GNSS), satellite communications and other space communications applications. In this study, a prediction model of global IGS‐TEC maps are established based on testing several different long short‐term memory (LSTM) network (LSTM)‐based algorithms to explore a direction that can effectively alleviate the increasing error with prediction time. We find that a Multi‐step auxiliary algorithm based prediction model performs best. It can effectively predict the global ionospheric IGS‐TEC in the next 6 days (the mean absolute deviation (MAD) and root mean square error (RMSE) are 2.485 and 3.511 TECU, respectively) compared to the IRI (the MAD and RMSE are 4.248 and 5.593 TECU). The analyses of four geomagnetic storm events are completely separate from the time range of the training set, so as to further validate the performance of the model. The International Reference Ionosphere model is used as a reference for the performance of our predictive model, and a rotated persistence is estimated by time‐shift algorithm of IGS‐TEC. The result suggests that the Multi‐step auxiliary prediction model has a good generalization performance and can have a relatively good stability and low error during a geomagnetic storm and quiet time. |
| format | Article |
| id | doaj-art-c85617fb4d4541e482c6c796d6036ae9 |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2022-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-c85617fb4d4541e482c6c796d6036ae92025-01-14T16:27:25ZengWileySpace Weather1542-73902022-04-01204n/an/a10.1029/2021SW002854Prediction of Global Ionospheric TEC Based on Deep LearningZhou Chen0Wenti Liao1Haimeng Li2Jinsong Wang3Xiaohua Deng4Sheng Hong5Institute of Space Science and Technology Nanchang University Nanchang ChinaInstitute of Space Science and Technology Nanchang University Nanchang ChinaInstitute of Space Science and Technology Nanchang University Nanchang ChinaKey Laboratory of Space Weather National Center for Space Weather Meteorological Administration Beijing ChinaInstitute of Space Science and Technology Nanchang University Nanchang ChinaInformation Engineering School Nanchang University Nanchang ChinaAbstract The accurate prediction of ionospheric Total Electron Content (TEC) is important for global navigation satellite systems (GNSS), satellite communications and other space communications applications. In this study, a prediction model of global IGS‐TEC maps are established based on testing several different long short‐term memory (LSTM) network (LSTM)‐based algorithms to explore a direction that can effectively alleviate the increasing error with prediction time. We find that a Multi‐step auxiliary algorithm based prediction model performs best. It can effectively predict the global ionospheric IGS‐TEC in the next 6 days (the mean absolute deviation (MAD) and root mean square error (RMSE) are 2.485 and 3.511 TECU, respectively) compared to the IRI (the MAD and RMSE are 4.248 and 5.593 TECU). The analyses of four geomagnetic storm events are completely separate from the time range of the training set, so as to further validate the performance of the model. The International Reference Ionosphere model is used as a reference for the performance of our predictive model, and a rotated persistence is estimated by time‐shift algorithm of IGS‐TEC. The result suggests that the Multi‐step auxiliary prediction model has a good generalization performance and can have a relatively good stability and low error during a geomagnetic storm and quiet time.https://doi.org/10.1029/2021SW002854 |
| spellingShingle | Zhou Chen Wenti Liao Haimeng Li Jinsong Wang Xiaohua Deng Sheng Hong Prediction of Global Ionospheric TEC Based on Deep Learning Space Weather |
| title | Prediction of Global Ionospheric TEC Based on Deep Learning |
| title_full | Prediction of Global Ionospheric TEC Based on Deep Learning |
| title_fullStr | Prediction of Global Ionospheric TEC Based on Deep Learning |
| title_full_unstemmed | Prediction of Global Ionospheric TEC Based on Deep Learning |
| title_short | Prediction of Global Ionospheric TEC Based on Deep Learning |
| title_sort | prediction of global ionospheric tec based on deep learning |
| url | https://doi.org/10.1029/2021SW002854 |
| work_keys_str_mv | AT zhouchen predictionofglobalionospherictecbasedondeeplearning AT wentiliao predictionofglobalionospherictecbasedondeeplearning AT haimengli predictionofglobalionospherictecbasedondeeplearning AT jinsongwang predictionofglobalionospherictecbasedondeeplearning AT xiaohuadeng predictionofglobalionospherictecbasedondeeplearning AT shenghong predictionofglobalionospherictecbasedondeeplearning |