An Investigation of Ionospheric TEC Prediction Maps Over China Using Bidirectional Long Short‐Term Memory Method
Abstract The ionospheric total electron content (TEC) is an important ionospheric parameter, and it is widely utilized in research such as space weather prediction and precise positioning. However, it is still challenging to develop an ionospheric TEC prediction model with high accuracy. In this stu...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-06-01
|
| Series: | Space Weather |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2022SW003103 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841536462295662592 |
|---|---|
| author | Shuangshuang Shi Kefei Zhang Suqin Wu Jiaqi Shi Andong Hu Huajing Wu Yu Li |
| author_facet | Shuangshuang Shi Kefei Zhang Suqin Wu Jiaqi Shi Andong Hu Huajing Wu Yu Li |
| author_sort | Shuangshuang Shi |
| collection | DOAJ |
| description | Abstract The ionospheric total electron content (TEC) is an important ionospheric parameter, and it is widely utilized in research such as space weather prediction and precise positioning. However, it is still challenging to develop an ionospheric TEC prediction model with high accuracy. In this study, a new ionospheric TEC model over China was developed using the bidirectional long short‐term memory (bi‐LSTM) method and observations from 257 ground‐based global navigation satellite system (GNSS) stations in the Crustal Movement Observation Network of China from January 2018 to December 2021. The root mean square errors of the bi‐LSTM‐based model’s 1 and 2 hr ahead predictions on the test data set (from June 2021 to December 2021) are 1.12 and 1.68 TECU, respectively, which are 75/50/32% and 72/48/22% smaller than those of the IRI‐2016, artificial neural network and LSTM‐based models, correspondingly. The bi‐LSTM‐based model shows the best performance, which is most likely due to the fact that the sequence information in both forward and backward directions is taken into consideration in the new model. In addition, the diurnal variation, seasonal variation of the ionospheric TEC, and variations under geomagnetic storm conditions are successfully captured by the bi‐LSTM‐based model. Moreover, the TEC maps resulting from the bi‐LSTM model agree well with those obtained from the final ionospheric product from the Chinese Academy of Sciences. Hence, the new model can be a good choice for the investigation of the spatiotemporal variation trend in the ionosphere and GNSS navigation. |
| format | Article |
| id | doaj-art-176a06661ea74ff2b480d5bdfec77019 |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2022-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-176a06661ea74ff2b480d5bdfec770192025-01-14T16:27:09ZengWileySpace Weather1542-73902022-06-01206n/an/a10.1029/2022SW003103An Investigation of Ionospheric TEC Prediction Maps Over China Using Bidirectional Long Short‐Term Memory MethodShuangshuang Shi0Kefei Zhang1Suqin Wu2Jiaqi Shi3Andong Hu4Huajing Wu5Yu Li6Jiangsu Key Laboratory of Resources and Environmental Information Engineering China University of Mining and Technology Xuzhou ChinaJiangsu Key Laboratory of Resources and Environmental Information Engineering China University of Mining and Technology Xuzhou ChinaJiangsu Key Laboratory of Resources and Environmental Information Engineering China University of Mining and Technology Xuzhou ChinaJiangsu Key Laboratory of Resources and Environmental Information Engineering China University of Mining and Technology Xuzhou ChinaCIRES University of Colorado Boulder CO USAJiangsu Key Laboratory of Resources and Environmental Information Engineering China University of Mining and Technology Xuzhou ChinaChina Earthquake Networks Center China Earthquake Administration Beijing ChinaAbstract The ionospheric total electron content (TEC) is an important ionospheric parameter, and it is widely utilized in research such as space weather prediction and precise positioning. However, it is still challenging to develop an ionospheric TEC prediction model with high accuracy. In this study, a new ionospheric TEC model over China was developed using the bidirectional long short‐term memory (bi‐LSTM) method and observations from 257 ground‐based global navigation satellite system (GNSS) stations in the Crustal Movement Observation Network of China from January 2018 to December 2021. The root mean square errors of the bi‐LSTM‐based model’s 1 and 2 hr ahead predictions on the test data set (from June 2021 to December 2021) are 1.12 and 1.68 TECU, respectively, which are 75/50/32% and 72/48/22% smaller than those of the IRI‐2016, artificial neural network and LSTM‐based models, correspondingly. The bi‐LSTM‐based model shows the best performance, which is most likely due to the fact that the sequence information in both forward and backward directions is taken into consideration in the new model. In addition, the diurnal variation, seasonal variation of the ionospheric TEC, and variations under geomagnetic storm conditions are successfully captured by the bi‐LSTM‐based model. Moreover, the TEC maps resulting from the bi‐LSTM model agree well with those obtained from the final ionospheric product from the Chinese Academy of Sciences. Hence, the new model can be a good choice for the investigation of the spatiotemporal variation trend in the ionosphere and GNSS navigation.https://doi.org/10.1029/2022SW003103ionospheric TEC predictionbidirectional long short‐term memory methodregional model over Chinaground‐based GPS dataionospheric disturbance |
| spellingShingle | Shuangshuang Shi Kefei Zhang Suqin Wu Jiaqi Shi Andong Hu Huajing Wu Yu Li An Investigation of Ionospheric TEC Prediction Maps Over China Using Bidirectional Long Short‐Term Memory Method Space Weather ionospheric TEC prediction bidirectional long short‐term memory method regional model over China ground‐based GPS data ionospheric disturbance |
| title | An Investigation of Ionospheric TEC Prediction Maps Over China Using Bidirectional Long Short‐Term Memory Method |
| title_full | An Investigation of Ionospheric TEC Prediction Maps Over China Using Bidirectional Long Short‐Term Memory Method |
| title_fullStr | An Investigation of Ionospheric TEC Prediction Maps Over China Using Bidirectional Long Short‐Term Memory Method |
| title_full_unstemmed | An Investigation of Ionospheric TEC Prediction Maps Over China Using Bidirectional Long Short‐Term Memory Method |
| title_short | An Investigation of Ionospheric TEC Prediction Maps Over China Using Bidirectional Long Short‐Term Memory Method |
| title_sort | investigation of ionospheric tec prediction maps over china using bidirectional long short term memory method |
| topic | ionospheric TEC prediction bidirectional long short‐term memory method regional model over China ground‐based GPS data ionospheric disturbance |
| url | https://doi.org/10.1029/2022SW003103 |
| work_keys_str_mv | AT shuangshuangshi aninvestigationofionospherictecpredictionmapsoverchinausingbidirectionallongshorttermmemorymethod AT kefeizhang aninvestigationofionospherictecpredictionmapsoverchinausingbidirectionallongshorttermmemorymethod AT suqinwu aninvestigationofionospherictecpredictionmapsoverchinausingbidirectionallongshorttermmemorymethod AT jiaqishi aninvestigationofionospherictecpredictionmapsoverchinausingbidirectionallongshorttermmemorymethod AT andonghu aninvestigationofionospherictecpredictionmapsoverchinausingbidirectionallongshorttermmemorymethod AT huajingwu aninvestigationofionospherictecpredictionmapsoverchinausingbidirectionallongshorttermmemorymethod AT yuli aninvestigationofionospherictecpredictionmapsoverchinausingbidirectionallongshorttermmemorymethod AT shuangshuangshi investigationofionospherictecpredictionmapsoverchinausingbidirectionallongshorttermmemorymethod AT kefeizhang investigationofionospherictecpredictionmapsoverchinausingbidirectionallongshorttermmemorymethod AT suqinwu investigationofionospherictecpredictionmapsoverchinausingbidirectionallongshorttermmemorymethod AT jiaqishi investigationofionospherictecpredictionmapsoverchinausingbidirectionallongshorttermmemorymethod AT andonghu investigationofionospherictecpredictionmapsoverchinausingbidirectionallongshorttermmemorymethod AT huajingwu investigationofionospherictecpredictionmapsoverchinausingbidirectionallongshorttermmemorymethod AT yuli investigationofionospherictecpredictionmapsoverchinausingbidirectionallongshorttermmemorymethod |