Storm‐Time Characteristics of Ionospheric Model (MSAP) Based on Multi‐Algorithm Fusion
Abstract Geomagnetic storms induce ionospheric disturbances, affecting short‐wave radio communication systems. Accurate ionospheric total electron content (TEC) prediction is vital for accurately describing the short‐wave radio environment of the ionosphere. We use the Multi‐Step Auxiliary Predictio...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
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| Series: | Space Weather |
| Online Access: | https://doi.org/10.1029/2022SW003360 |
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| _version_ | 1841536466628378624 |
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| author | Zhou Chen Kang Wang Haimeng Li Wenti Liao Rongxin Tang Jing‐song Wang Xiaohua Deng |
| author_facet | Zhou Chen Kang Wang Haimeng Li Wenti Liao Rongxin Tang Jing‐song Wang Xiaohua Deng |
| author_sort | Zhou Chen |
| collection | DOAJ |
| description | Abstract Geomagnetic storms induce ionospheric disturbances, affecting short‐wave radio communication systems. Accurate ionospheric total electron content (TEC) prediction is vital for accurately describing the short‐wave radio environment of the ionosphere. We use the Multi‐Step Auxiliary Prediction (MSAP) model, a deep learning algorithm, to forecast TEC during geomagnetic storms. The MSAP model integrates Bi‐LSTM networks, an auxiliary model, and convolutional processes for spatiotemporal modeling. Our validation shows the MSAP model outperforms the IRI‐2016 model in predicting global TEC for the next 6 days in the test set. We assess its performance during 116 geomagnetic storm events, considering storm intensity, solar activity, month, and Universal Time (UT). The MSAP model exhibits a weak correlation with storm intensity and a strong correlation with solar activity. Monthly variation displays similar strong correlations in root mean square error (RMSE) and R2 for both models. For UT variation, the other metrics exhibit a weak correlation with the number of Global Navigation Satellite System stations, except for the RMSE of the MSAP and IRI‐2016 models. |
| format | Article |
| id | doaj-art-29519456d00143a9bab34633a092be9b |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-29519456d00143a9bab34633a092be9b2025-01-14T16:26:57ZengWileySpace Weather1542-73902024-01-01221n/an/a10.1029/2022SW003360Storm‐Time Characteristics of Ionospheric Model (MSAP) Based on Multi‐Algorithm FusionZhou Chen0Kang Wang1Haimeng Li2Wenti Liao3Rongxin Tang4Jing‐song Wang5Xiaohua Deng6Information Engineering School Nanchang University Nanchang ChinaInstitute of Space Science and Technology Nanchang University Nanchang ChinaInformation Engineering School Nanchang University Nanchang ChinaInformation Engineering School Nanchang University Nanchang ChinaInformation Engineering School Nanchang University Nanchang ChinaKey Laboratory of Space Weather National Center for Space Weather China Meteorological Administration Beijing ChinaInformation Engineering School Nanchang University Nanchang ChinaAbstract Geomagnetic storms induce ionospheric disturbances, affecting short‐wave radio communication systems. Accurate ionospheric total electron content (TEC) prediction is vital for accurately describing the short‐wave radio environment of the ionosphere. We use the Multi‐Step Auxiliary Prediction (MSAP) model, a deep learning algorithm, to forecast TEC during geomagnetic storms. The MSAP model integrates Bi‐LSTM networks, an auxiliary model, and convolutional processes for spatiotemporal modeling. Our validation shows the MSAP model outperforms the IRI‐2016 model in predicting global TEC for the next 6 days in the test set. We assess its performance during 116 geomagnetic storm events, considering storm intensity, solar activity, month, and Universal Time (UT). The MSAP model exhibits a weak correlation with storm intensity and a strong correlation with solar activity. Monthly variation displays similar strong correlations in root mean square error (RMSE) and R2 for both models. For UT variation, the other metrics exhibit a weak correlation with the number of Global Navigation Satellite System stations, except for the RMSE of the MSAP and IRI‐2016 models.https://doi.org/10.1029/2022SW003360 |
| spellingShingle | Zhou Chen Kang Wang Haimeng Li Wenti Liao Rongxin Tang Jing‐song Wang Xiaohua Deng Storm‐Time Characteristics of Ionospheric Model (MSAP) Based on Multi‐Algorithm Fusion Space Weather |
| title | Storm‐Time Characteristics of Ionospheric Model (MSAP) Based on Multi‐Algorithm Fusion |
| title_full | Storm‐Time Characteristics of Ionospheric Model (MSAP) Based on Multi‐Algorithm Fusion |
| title_fullStr | Storm‐Time Characteristics of Ionospheric Model (MSAP) Based on Multi‐Algorithm Fusion |
| title_full_unstemmed | Storm‐Time Characteristics of Ionospheric Model (MSAP) Based on Multi‐Algorithm Fusion |
| title_short | Storm‐Time Characteristics of Ionospheric Model (MSAP) Based on Multi‐Algorithm Fusion |
| title_sort | storm time characteristics of ionospheric model msap based on multi algorithm fusion |
| url | https://doi.org/10.1029/2022SW003360 |
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