Modeling Australian TEC Maps Using Long-Term Observations of Australian Regional GPS Network by Artificial Neural Network-Aided Spherical Cap Harmonic Analysis Approach
The global ionosphere map (GIM) is not capable of serving precise positioning and navigation for single frequency receivers in Australia due to sparse International GNSS Service (IGS) stations located in the vast land. This study proposes an approach to represent Australian total electron content (T...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/23/3851 |
| _version_ | 1797546902984589312 |
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| author | Wang Li Dongsheng Zhao Yi Shen Kefei Zhang |
| author_facet | Wang Li Dongsheng Zhao Yi Shen Kefei Zhang |
| author_sort | Wang Li |
| collection | DOAJ |
| description | The global ionosphere map (GIM) is not capable of serving precise positioning and navigation for single frequency receivers in Australia due to sparse International GNSS Service (IGS) stations located in the vast land. This study proposes an approach to represent Australian total electron content (TEC) using the spherical cap harmonic analysis (SCHA) and artificial neural network (ANN). The new Australian TEC maps are released with an interval of 15 min for longitude and latitude in 0.5° × 0.5°. The validation results show that the Australian Ionospheric Maps (AIMs) well represent the hourly and seasonally ionospheric electrodynamic features over the Australian continent; the accuracy of the AIMs improves remarkably compared to the GIM and the model built only by the SCHA. The residual of the AIM is inversely proportional to the level of solar radiation. During the equinoxes and solstices in a solar minimum year, the residuals are 2.16, 1.57, 1.68, and 1.98 total electron content units (TECUs, 1 TECU = 10<sup>16</sup> electron/m<sup>2</sup>), respectively. Furthermore, the AIM has a strong capability in capturing the adequate electrodynamic evolutions of the traveling ionospheric disturbances under severe geomagnetic storms. The results demonstrate that the ANN-aided SCHA method is an effective approach for mapping and investigating the TEC maps over Australia. |
| first_indexed | 2024-03-10T14:35:56Z |
| format | Article |
| id | doaj.art-6d6b9bc8b05c494bb6c6b42f10286a56 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T14:35:56Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-6d6b9bc8b05c494bb6c6b42f10286a562023-11-20T22:09:46ZengMDPI AGRemote Sensing2072-42922020-11-011223385110.3390/rs12233851Modeling Australian TEC Maps Using Long-Term Observations of Australian Regional GPS Network by Artificial Neural Network-Aided Spherical Cap Harmonic Analysis ApproachWang Li0Dongsheng Zhao1Yi Shen2Kefei Zhang3School of Environmental Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Environmental Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, ChinaKey Laboratory for Synergistic Prevention of Water and Soil Environmental Pollution, Xinyang Normal University, Xinyang 464000, ChinaSchool of Environmental Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, ChinaThe global ionosphere map (GIM) is not capable of serving precise positioning and navigation for single frequency receivers in Australia due to sparse International GNSS Service (IGS) stations located in the vast land. This study proposes an approach to represent Australian total electron content (TEC) using the spherical cap harmonic analysis (SCHA) and artificial neural network (ANN). The new Australian TEC maps are released with an interval of 15 min for longitude and latitude in 0.5° × 0.5°. The validation results show that the Australian Ionospheric Maps (AIMs) well represent the hourly and seasonally ionospheric electrodynamic features over the Australian continent; the accuracy of the AIMs improves remarkably compared to the GIM and the model built only by the SCHA. The residual of the AIM is inversely proportional to the level of solar radiation. During the equinoxes and solstices in a solar minimum year, the residuals are 2.16, 1.57, 1.68, and 1.98 total electron content units (TECUs, 1 TECU = 10<sup>16</sup> electron/m<sup>2</sup>), respectively. Furthermore, the AIM has a strong capability in capturing the adequate electrodynamic evolutions of the traveling ionospheric disturbances under severe geomagnetic storms. The results demonstrate that the ANN-aided SCHA method is an effective approach for mapping and investigating the TEC maps over Australia.https://www.mdpi.com/2072-4292/12/23/3851Australian ionospheric modelspherical cap harmonic analysisartificial neural networkAustralian regional GNSS networkgeomagnetic storm |
| spellingShingle | Wang Li Dongsheng Zhao Yi Shen Kefei Zhang Modeling Australian TEC Maps Using Long-Term Observations of Australian Regional GPS Network by Artificial Neural Network-Aided Spherical Cap Harmonic Analysis Approach Remote Sensing Australian ionospheric model spherical cap harmonic analysis artificial neural network Australian regional GNSS network geomagnetic storm |
| title | Modeling Australian TEC Maps Using Long-Term Observations of Australian Regional GPS Network by Artificial Neural Network-Aided Spherical Cap Harmonic Analysis Approach |
| title_full | Modeling Australian TEC Maps Using Long-Term Observations of Australian Regional GPS Network by Artificial Neural Network-Aided Spherical Cap Harmonic Analysis Approach |
| title_fullStr | Modeling Australian TEC Maps Using Long-Term Observations of Australian Regional GPS Network by Artificial Neural Network-Aided Spherical Cap Harmonic Analysis Approach |
| title_full_unstemmed | Modeling Australian TEC Maps Using Long-Term Observations of Australian Regional GPS Network by Artificial Neural Network-Aided Spherical Cap Harmonic Analysis Approach |
| title_short | Modeling Australian TEC Maps Using Long-Term Observations of Australian Regional GPS Network by Artificial Neural Network-Aided Spherical Cap Harmonic Analysis Approach |
| title_sort | modeling australian tec maps using long term observations of australian regional gps network by artificial neural network aided spherical cap harmonic analysis approach |
| topic | Australian ionospheric model spherical cap harmonic analysis artificial neural network Australian regional GNSS network geomagnetic storm |
| url | https://www.mdpi.com/2072-4292/12/23/3851 |
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