Modeling TEC Maps Over China Using Particle Swarm Optimization Neural Networks and Long‐Term Ground‐Based GPS, COSMIC, and Fengyun Data
Abstract This paper presents a new model for ionospheric total electron content (TEC) over China. The new model is developed using a hybrid method composed of the particle swarm optimization (PSO) and artificial neural network and long‐term observations from 257 ground‐based global navigation satell...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-04-01
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| Series: | Space Weather |
| Online Access: | https://doi.org/10.1029/2022SW003357 |
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| author | Shuangshuang Shi Kefei Zhang Jiaqi Shi Andong Hu Dongsheng Zhao Zhongchao Shi Peng Sun Huajing Wu Suqin Wu |
| author_facet | Shuangshuang Shi Kefei Zhang Jiaqi Shi Andong Hu Dongsheng Zhao Zhongchao Shi Peng Sun Huajing Wu Suqin Wu |
| author_sort | Shuangshuang Shi |
| collection | DOAJ |
| description | Abstract This paper presents a new model for ionospheric total electron content (TEC) over China. The new model is developed using a hybrid method composed of the particle swarm optimization (PSO) and artificial neural network and long‐term observations from 257 ground‐based global navigation satellite systems (GNSS) stations and space‐borne GNSS radio occultation systems (COSMIC and Fengyun) during the 14‐year period of 2008–2021. The PSO algorithm is used to optimize the traditional back‐propagation neural network (BP‐NN) model by reducing the effects of the local minimum problem. The new model is validated using out‐of‐sample data, and its results are compared to the BP‐NN, IRI‐2016 model, and global ionospheric maps provided by the International GNSS Service. Results show that TEC predicted from the new model agrees better with the reference TEC than the BP‐NN and IRI‐2016 models. The improvements made by the new model over the BP‐NN and IRI‐2016 models in the equinox, summer, and winter seasons of the solar maximum year (2015) are 4%–20%/20%–36%, 9%–21%/26%–42%, and 6%–22%/21%–43%, respectively, and their corresponding results in the solar minimum year (2019) are 12%–24%/41%–59%, 9%–24%/28%–56%, and 10%–26%/53%–72%. Furthermore, the new model well captures the diurnal evolution, seasonal variation, and variations in the ionospheric TEC under different solar activity levels. It also well captures the mid‐latitude summer nighttime anomaly over China, and the diurnal anomaly is more pronounced in the solar minimum year (2019) than in the solar maximum year (2015) in terms of the nighttime‐to‐noontime ratio and the range of months it lasts in a year. |
| format | Article |
| id | doaj-art-47c6d434b9644e7c8f4928815639cd99 |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2023-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-47c6d434b9644e7c8f4928815639cd992025-01-14T16:26:47ZengWileySpace Weather1542-73902023-04-01214n/an/a10.1029/2022SW003357Modeling TEC Maps Over China Using Particle Swarm Optimization Neural Networks and Long‐Term Ground‐Based GPS, COSMIC, and Fengyun DataShuangshuang Shi0Kefei Zhang1Jiaqi Shi2Andong Hu3Dongsheng Zhao4Zhongchao Shi5Peng Sun6Huajing Wu7Suqin Wu8Jiangsu 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 ChinaGNSS Research Center Wuhan University Wuhan ChinaCIRES University of Colorado Boulder Boulder CO USAJiangsu Key Laboratory of Resources and Environmental Information Engineering China University of Mining and Technology Xuzhou ChinaDepartment of Restoration Ecology and Built Environment Tokyo City University Yokohama JapanJiangsu 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 ChinaAbstract This paper presents a new model for ionospheric total electron content (TEC) over China. The new model is developed using a hybrid method composed of the particle swarm optimization (PSO) and artificial neural network and long‐term observations from 257 ground‐based global navigation satellite systems (GNSS) stations and space‐borne GNSS radio occultation systems (COSMIC and Fengyun) during the 14‐year period of 2008–2021. The PSO algorithm is used to optimize the traditional back‐propagation neural network (BP‐NN) model by reducing the effects of the local minimum problem. The new model is validated using out‐of‐sample data, and its results are compared to the BP‐NN, IRI‐2016 model, and global ionospheric maps provided by the International GNSS Service. Results show that TEC predicted from the new model agrees better with the reference TEC than the BP‐NN and IRI‐2016 models. The improvements made by the new model over the BP‐NN and IRI‐2016 models in the equinox, summer, and winter seasons of the solar maximum year (2015) are 4%–20%/20%–36%, 9%–21%/26%–42%, and 6%–22%/21%–43%, respectively, and their corresponding results in the solar minimum year (2019) are 12%–24%/41%–59%, 9%–24%/28%–56%, and 10%–26%/53%–72%. Furthermore, the new model well captures the diurnal evolution, seasonal variation, and variations in the ionospheric TEC under different solar activity levels. It also well captures the mid‐latitude summer nighttime anomaly over China, and the diurnal anomaly is more pronounced in the solar minimum year (2019) than in the solar maximum year (2015) in terms of the nighttime‐to‐noontime ratio and the range of months it lasts in a year.https://doi.org/10.1029/2022SW003357 |
| spellingShingle | Shuangshuang Shi Kefei Zhang Jiaqi Shi Andong Hu Dongsheng Zhao Zhongchao Shi Peng Sun Huajing Wu Suqin Wu Modeling TEC Maps Over China Using Particle Swarm Optimization Neural Networks and Long‐Term Ground‐Based GPS, COSMIC, and Fengyun Data Space Weather |
| title | Modeling TEC Maps Over China Using Particle Swarm Optimization Neural Networks and Long‐Term Ground‐Based GPS, COSMIC, and Fengyun Data |
| title_full | Modeling TEC Maps Over China Using Particle Swarm Optimization Neural Networks and Long‐Term Ground‐Based GPS, COSMIC, and Fengyun Data |
| title_fullStr | Modeling TEC Maps Over China Using Particle Swarm Optimization Neural Networks and Long‐Term Ground‐Based GPS, COSMIC, and Fengyun Data |
| title_full_unstemmed | Modeling TEC Maps Over China Using Particle Swarm Optimization Neural Networks and Long‐Term Ground‐Based GPS, COSMIC, and Fengyun Data |
| title_short | Modeling TEC Maps Over China Using Particle Swarm Optimization Neural Networks and Long‐Term Ground‐Based GPS, COSMIC, and Fengyun Data |
| title_sort | modeling tec maps over china using particle swarm optimization neural networks and long term ground based gps cosmic and fengyun data |
| url | https://doi.org/10.1029/2022SW003357 |
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