Estimation Model of Global Ionospheric Irregularities: An Artificial Intelligence Approach
Abstract The ionospheric sporadic E layer, the ionospheric irregularities of enhanced electron density, appears in the Earth's ionosphere at altitudes between 90 and 120 km, which supports the real‐world radio communication needs of many sectors reliant on ionosphere‐dependent decision‐making....
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| Format: | Article |
| Language: | English |
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Wiley
2022-09-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2022SW003160 |
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| author | Penghao Tian Bingkun Yu Hailun Ye Xianghui Xue Jianfei Wu Tingdi Chen |
| author_facet | Penghao Tian Bingkun Yu Hailun Ye Xianghui Xue Jianfei Wu Tingdi Chen |
| author_sort | Penghao Tian |
| collection | DOAJ |
| description | Abstract The ionospheric sporadic E layer, the ionospheric irregularities of enhanced electron density, appears in the Earth's ionosphere at altitudes between 90 and 120 km, which supports the real‐world radio communication needs of many sectors reliant on ionosphere‐dependent decision‐making. The prediction of the occurrence of sporadic E layers has been extremely difficult due to the highly complex behavior. Conventional numerical methods are limited because of the inability to extract high‐level information from data. Deep learning is a powerful tool for mining latent features from data, which can theoretically avoid assumptions constraining physical methods. Inspired by feature extraction, we applied deep learning to explore latent relationships between mapping observable lower atmospheric data and ionospheric data from limited observations. The proposed model was trained with high‐resolution ERA5 data during 1 January 2007–30 August 2018 as input and corresponding ionospheric sporadic E data collected from COSMIC RO measurements as output. The results show that the model can learn complex relevance as bridges connecting the input and the desired output and obtain excellent performance and generalization capability by applying multiple evaluation criteria. Additionally, we established several model architecture training methods to explore the performance of the model with different input data. The statistic results show that model inference performance is proportional to the abundance of input information and is impacted by intraseasonal variability. The inference capability of the model achieves the best performance in the June–August (JJA) and December–February (DJF) seasons, which is the exact period of sporadic E layer significant occurrence, although different models are evaluated. |
| format | Article |
| id | doaj-art-f831eb1fd8ae4b95a41f3be3b4095fe4 |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2022-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-f831eb1fd8ae4b95a41f3be3b4095fe42025-01-14T16:31:13ZengWileySpace Weather1542-73902022-09-01209n/an/a10.1029/2022SW003160Estimation Model of Global Ionospheric Irregularities: An Artificial Intelligence ApproachPenghao Tian0Bingkun Yu1Hailun Ye2Xianghui Xue3Jianfei Wu4Tingdi Chen5CAS Key Laboratory of Geospace Environment School of Earth and Space Sciences University of Science and Technology of China Hefei ChinaCAS Key Laboratory of Geospace Environment School of Earth and Space Sciences University of Science and Technology of China Hefei ChinaCAS Key Laboratory of Geospace Environment School of Earth and Space Sciences University of Science and Technology of China Hefei ChinaCAS Key Laboratory of Geospace Environment School of Earth and Space Sciences University of Science and Technology of China Hefei ChinaCAS Key Laboratory of Geospace Environment School of Earth and Space Sciences University of Science and Technology of China Hefei ChinaCAS Key Laboratory of Geospace Environment School of Earth and Space Sciences University of Science and Technology of China Hefei ChinaAbstract The ionospheric sporadic E layer, the ionospheric irregularities of enhanced electron density, appears in the Earth's ionosphere at altitudes between 90 and 120 km, which supports the real‐world radio communication needs of many sectors reliant on ionosphere‐dependent decision‐making. The prediction of the occurrence of sporadic E layers has been extremely difficult due to the highly complex behavior. Conventional numerical methods are limited because of the inability to extract high‐level information from data. Deep learning is a powerful tool for mining latent features from data, which can theoretically avoid assumptions constraining physical methods. Inspired by feature extraction, we applied deep learning to explore latent relationships between mapping observable lower atmospheric data and ionospheric data from limited observations. The proposed model was trained with high‐resolution ERA5 data during 1 January 2007–30 August 2018 as input and corresponding ionospheric sporadic E data collected from COSMIC RO measurements as output. The results show that the model can learn complex relevance as bridges connecting the input and the desired output and obtain excellent performance and generalization capability by applying multiple evaluation criteria. Additionally, we established several model architecture training methods to explore the performance of the model with different input data. The statistic results show that model inference performance is proportional to the abundance of input information and is impacted by intraseasonal variability. The inference capability of the model achieves the best performance in the June–August (JJA) and December–February (DJF) seasons, which is the exact period of sporadic E layer significant occurrence, although different models are evaluated.https://doi.org/10.1029/2022SW003160deep learningionospheric irregularitiesS4maxionospheresporadic Eradio occultation |
| spellingShingle | Penghao Tian Bingkun Yu Hailun Ye Xianghui Xue Jianfei Wu Tingdi Chen Estimation Model of Global Ionospheric Irregularities: An Artificial Intelligence Approach Space Weather deep learning ionospheric irregularities S4max ionosphere sporadic E radio occultation |
| title | Estimation Model of Global Ionospheric Irregularities: An Artificial Intelligence Approach |
| title_full | Estimation Model of Global Ionospheric Irregularities: An Artificial Intelligence Approach |
| title_fullStr | Estimation Model of Global Ionospheric Irregularities: An Artificial Intelligence Approach |
| title_full_unstemmed | Estimation Model of Global Ionospheric Irregularities: An Artificial Intelligence Approach |
| title_short | Estimation Model of Global Ionospheric Irregularities: An Artificial Intelligence Approach |
| title_sort | estimation model of global ionospheric irregularities an artificial intelligence approach |
| topic | deep learning ionospheric irregularities S4max ionosphere sporadic E radio occultation |
| url | https://doi.org/10.1029/2022SW003160 |
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