Reconstruction of the Regional Total Electron Content Maps Over the Korean Peninsula Using Deep Convolutional Generative Adversarial Network and Poisson Blending
Abstract This study reconstructs total electron content (TEC) maps in the vicinity of the Korean Peninsula by employing a deep convolutional generative adversarial network and Poisson blending (DCGAN‐PB). Our interest is to rebuild small‐scale ionosphere structures on the TEC map in a local region w...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-08-01
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| Series: | Space Weather |
| Online Access: | https://doi.org/10.1029/2022SW003131 |
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| author | Se‐Heon Jeong Woo Kyoung Lee Soojeong Jang Hyosub Kil Jeong‐Heon Kim Young‐Sil Kwak Yong Ha Kim Junseok Hong Byung‐Kyu Choi |
| author_facet | Se‐Heon Jeong Woo Kyoung Lee Soojeong Jang Hyosub Kil Jeong‐Heon Kim Young‐Sil Kwak Yong Ha Kim Junseok Hong Byung‐Kyu Choi |
| author_sort | Se‐Heon Jeong |
| collection | DOAJ |
| description | Abstract This study reconstructs total electron content (TEC) maps in the vicinity of the Korean Peninsula by employing a deep convolutional generative adversarial network and Poisson blending (DCGAN‐PB). Our interest is to rebuild small‐scale ionosphere structures on the TEC map in a local region where pronounced ionospheric structures, such as the equatorial ionization anomaly, are absent. The reconstructed regional TEC maps have a domain of 120°–135.5°E longitude and 25.5°–41°N latitude with 0.5° resolution. To achieve this, we first train a DCGAN model by using the International Reference Ionosphere‐based TEC maps from 2002 to 2019 (except for 2010 and 2014) as a training data set. Next, the trained DCGAN model generates synthetic complete TEC maps from observation‐based incomplete TEC maps. Final TEC maps are produced by blending of synthetic TEC maps with observed TEC data by PB. The performance of the DCGAN‐PB model is evaluated by testing the regeneration of the masked TEC observations in 2010 (solar minimum) and 2014 (solar maximum). Our results show that a good correlation between the masked and model‐generated TEC values is maintained even with a large percentage (∼80%) of masking. The performance of the DCGAN‐PB model is not sensitive to local time, solar activity, and magnetic activity. Thus, the DCGAN‐PB model can reconstruct fine ionospheric structures in regions where observations are sparse and distinguishing ionospheric structures are absent. This model can contribute to near real‐time monitoring of the ionosphere by immediately providing complete TEC maps. |
| format | Article |
| id | doaj-art-fb5ca482140940a296bb96d5f17ea28c |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2022-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-fb5ca482140940a296bb96d5f17ea28c2025-01-14T16:27:07ZengWileySpace Weather1542-73902022-08-01208n/an/a10.1029/2022SW003131Reconstruction of the Regional Total Electron Content Maps Over the Korean Peninsula Using Deep Convolutional Generative Adversarial Network and Poisson BlendingSe‐Heon Jeong0Woo Kyoung Lee1Soojeong Jang2Hyosub Kil3Jeong‐Heon Kim4Young‐Sil Kwak5Yong Ha Kim6Junseok Hong7Byung‐Kyu Choi8Chungnam National University Daejeon South KoreaKorea Astronomy and Space Science Institute Daejeon South KoreaKyung Hee University Yongin South KoreaJohns Hopkins University Applied Physics Laboratory Laurel MD USAKorea Astronomy and Space Science Institute Daejeon South KoreaKorea Astronomy and Space Science Institute Daejeon South KoreaChungnam National University Daejeon South KoreaKorea Astronomy and Space Science Institute Daejeon South KoreaKorea Astronomy and Space Science Institute Daejeon South KoreaAbstract This study reconstructs total electron content (TEC) maps in the vicinity of the Korean Peninsula by employing a deep convolutional generative adversarial network and Poisson blending (DCGAN‐PB). Our interest is to rebuild small‐scale ionosphere structures on the TEC map in a local region where pronounced ionospheric structures, such as the equatorial ionization anomaly, are absent. The reconstructed regional TEC maps have a domain of 120°–135.5°E longitude and 25.5°–41°N latitude with 0.5° resolution. To achieve this, we first train a DCGAN model by using the International Reference Ionosphere‐based TEC maps from 2002 to 2019 (except for 2010 and 2014) as a training data set. Next, the trained DCGAN model generates synthetic complete TEC maps from observation‐based incomplete TEC maps. Final TEC maps are produced by blending of synthetic TEC maps with observed TEC data by PB. The performance of the DCGAN‐PB model is evaluated by testing the regeneration of the masked TEC observations in 2010 (solar minimum) and 2014 (solar maximum). Our results show that a good correlation between the masked and model‐generated TEC values is maintained even with a large percentage (∼80%) of masking. The performance of the DCGAN‐PB model is not sensitive to local time, solar activity, and magnetic activity. Thus, the DCGAN‐PB model can reconstruct fine ionospheric structures in regions where observations are sparse and distinguishing ionospheric structures are absent. This model can contribute to near real‐time monitoring of the ionosphere by immediately providing complete TEC maps.https://doi.org/10.1029/2022SW003131 |
| spellingShingle | Se‐Heon Jeong Woo Kyoung Lee Soojeong Jang Hyosub Kil Jeong‐Heon Kim Young‐Sil Kwak Yong Ha Kim Junseok Hong Byung‐Kyu Choi Reconstruction of the Regional Total Electron Content Maps Over the Korean Peninsula Using Deep Convolutional Generative Adversarial Network and Poisson Blending Space Weather |
| title | Reconstruction of the Regional Total Electron Content Maps Over the Korean Peninsula Using Deep Convolutional Generative Adversarial Network and Poisson Blending |
| title_full | Reconstruction of the Regional Total Electron Content Maps Over the Korean Peninsula Using Deep Convolutional Generative Adversarial Network and Poisson Blending |
| title_fullStr | Reconstruction of the Regional Total Electron Content Maps Over the Korean Peninsula Using Deep Convolutional Generative Adversarial Network and Poisson Blending |
| title_full_unstemmed | Reconstruction of the Regional Total Electron Content Maps Over the Korean Peninsula Using Deep Convolutional Generative Adversarial Network and Poisson Blending |
| title_short | Reconstruction of the Regional Total Electron Content Maps Over the Korean Peninsula Using Deep Convolutional Generative Adversarial Network and Poisson Blending |
| title_sort | reconstruction of the regional total electron content maps over the korean peninsula using deep convolutional generative adversarial network and poisson blending |
| url | https://doi.org/10.1029/2022SW003131 |
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