Application of a Multi‐Layer Artificial Neural Network in a 3‐D Global Electron Density Model Using the Long‐Term Observations of COSMIC, Fengyun‐3C, and Digisonde
Abstract The ionosphere plays an important role in satellite navigation, radio communication, and space weather prediction. However, it is still a challenging mission to develop a model with high predictability that captures the horizontal‐vertical features of ionospheric electrodynamics. In this st...
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2021-03-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2020SW002605 |
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| author | Wang Li Dongsheng Zhao Changyong He Yi Shen Andong Hu Kefei Zhang |
| author_facet | Wang Li Dongsheng Zhao Changyong He Yi Shen Andong Hu Kefei Zhang |
| author_sort | Wang Li |
| collection | DOAJ |
| description | Abstract The ionosphere plays an important role in satellite navigation, radio communication, and space weather prediction. However, it is still a challenging mission to develop a model with high predictability that captures the horizontal‐vertical features of ionospheric electrodynamics. In this study, multiple observations during 2005–2019 from space‐borne global navigation satellite system (GNSS) radio occultation (RO) systems (COSMIC and FY‐3C) and the Digisonde Global Ionosphere Radio Observatory are utilized to develop a completely global ionospheric three‐dimensional electron density model based on an artificial neural network, namely ANN‐TDD. The correlation coefficients of the predicted profiles all exceed 0.96 for the training, validation and test datasets, and the minimum root‐mean‐square error of the predicted residuals is 7.8 × 104 el/cm3. Under quiet space weather, the predicted accuracy of the ANN‐TDD is 30%–60% higher than the IRI‐2016 at the Millstone Hill and Jicamarca incoherent scatter radars. However, the ANN‐TDD is less capable of predicting ionospheric dynamic evolution under severe geomagnetic storms compared to the IRI‐2016 with the STORM option activated. Additionally, the ANN‐TDD successfully reproduces the large‐scale horizontal‐vertical ionospheric electrodynamic features, including seasonal variation and hemispheric asymmetries. These features agree well with the structure revealed by the RO profiles derived from the FORMOSAT/COSMIC‐2 mission. Furthermore, the ANN‐TDD successfully captures the prominent regional ionospheric patterns, including the equatorial ionization anomaly, Weddell Sea anomaly and mid‐latitude summer nighttime anomaly. The new model is expected to play an important role in the application of GNSS navigation and in the explanation of the physical mechanisms involved. |
| format | Article |
| id | doaj-art-72e41438961f41cdbf5d017e5025d693 |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2021-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-72e41438961f41cdbf5d017e5025d6932025-01-14T16:30:38ZengWileySpace Weather1542-73902021-03-01193n/an/a10.1029/2020SW002605Application of a Multi‐Layer Artificial Neural Network in a 3‐D Global Electron Density Model Using the Long‐Term Observations of COSMIC, Fengyun‐3C, and DigisondeWang Li0Dongsheng Zhao1Changyong He2Yi Shen3Andong Hu4Kefei Zhang5School of Environmental Science and Spatial Informatics China University of Mining and Technology Xuzhou ChinaSchool of Environmental Science and Spatial Informatics China University of Mining and Technology Xuzhou ChinaSPACE Research Center School of Science RMIT University Melbourne VIC AustraliaKey Laboratory for Synergistic Prevention of Water and Soil Environmental Pollution Xinyang Normal University Xinyang ChinaSPACE Research Center School of Science RMIT University Melbourne VIC AustraliaSchool of Environmental Science and Spatial Informatics China University of Mining and Technology Xuzhou ChinaAbstract The ionosphere plays an important role in satellite navigation, radio communication, and space weather prediction. However, it is still a challenging mission to develop a model with high predictability that captures the horizontal‐vertical features of ionospheric electrodynamics. In this study, multiple observations during 2005–2019 from space‐borne global navigation satellite system (GNSS) radio occultation (RO) systems (COSMIC and FY‐3C) and the Digisonde Global Ionosphere Radio Observatory are utilized to develop a completely global ionospheric three‐dimensional electron density model based on an artificial neural network, namely ANN‐TDD. The correlation coefficients of the predicted profiles all exceed 0.96 for the training, validation and test datasets, and the minimum root‐mean‐square error of the predicted residuals is 7.8 × 104 el/cm3. Under quiet space weather, the predicted accuracy of the ANN‐TDD is 30%–60% higher than the IRI‐2016 at the Millstone Hill and Jicamarca incoherent scatter radars. However, the ANN‐TDD is less capable of predicting ionospheric dynamic evolution under severe geomagnetic storms compared to the IRI‐2016 with the STORM option activated. Additionally, the ANN‐TDD successfully reproduces the large‐scale horizontal‐vertical ionospheric electrodynamic features, including seasonal variation and hemispheric asymmetries. These features agree well with the structure revealed by the RO profiles derived from the FORMOSAT/COSMIC‐2 mission. Furthermore, the ANN‐TDD successfully captures the prominent regional ionospheric patterns, including the equatorial ionization anomaly, Weddell Sea anomaly and mid‐latitude summer nighttime anomaly. The new model is expected to play an important role in the application of GNSS navigation and in the explanation of the physical mechanisms involved.https://doi.org/10.1029/2020SW002605COSMIC missionequatorial ionization anomalyFY‐3Cionospheric modelneural network |
| spellingShingle | Wang Li Dongsheng Zhao Changyong He Yi Shen Andong Hu Kefei Zhang Application of a Multi‐Layer Artificial Neural Network in a 3‐D Global Electron Density Model Using the Long‐Term Observations of COSMIC, Fengyun‐3C, and Digisonde Space Weather COSMIC mission equatorial ionization anomaly FY‐3C ionospheric model neural network |
| title | Application of a Multi‐Layer Artificial Neural Network in a 3‐D Global Electron Density Model Using the Long‐Term Observations of COSMIC, Fengyun‐3C, and Digisonde |
| title_full | Application of a Multi‐Layer Artificial Neural Network in a 3‐D Global Electron Density Model Using the Long‐Term Observations of COSMIC, Fengyun‐3C, and Digisonde |
| title_fullStr | Application of a Multi‐Layer Artificial Neural Network in a 3‐D Global Electron Density Model Using the Long‐Term Observations of COSMIC, Fengyun‐3C, and Digisonde |
| title_full_unstemmed | Application of a Multi‐Layer Artificial Neural Network in a 3‐D Global Electron Density Model Using the Long‐Term Observations of COSMIC, Fengyun‐3C, and Digisonde |
| title_short | Application of a Multi‐Layer Artificial Neural Network in a 3‐D Global Electron Density Model Using the Long‐Term Observations of COSMIC, Fengyun‐3C, and Digisonde |
| title_sort | application of a multi layer artificial neural network in a 3 d global electron density model using the long term observations of cosmic fengyun 3c and digisonde |
| topic | COSMIC mission equatorial ionization anomaly FY‐3C ionospheric model neural network |
| url | https://doi.org/10.1029/2020SW002605 |
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