IonoGAN: An Enhanced Model for Forecasting Quiet and Disturbed Ionospheric Features From Predicted Ionograms
Abstract Ionograms are radar echo graphs that depict vertical ionospheric density profiles, structures, fluctuations, and irregularities, with the F region represented by F‐trace and Spread‐F features in the graphs. In this paper, IonoGAN, an enhanced neural network based on the Generative Adversari...
Saved in:
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-06-01
|
| Series: | Space Weather |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2025SW004463 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849414908834217984 |
|---|---|
| author | Chu Qiu Jinhui Cai Zheng Wang Pengdong Gao Guojun Wang Quan Qi Bo Wang Zhengwei Cheng Jiankui Shi Yajun Zhu Xiao Wang Kai Ding |
| author_facet | Chu Qiu Jinhui Cai Zheng Wang Pengdong Gao Guojun Wang Quan Qi Bo Wang Zhengwei Cheng Jiankui Shi Yajun Zhu Xiao Wang Kai Ding |
| author_sort | Chu Qiu |
| collection | DOAJ |
| description | Abstract Ionograms are radar echo graphs that depict vertical ionospheric density profiles, structures, fluctuations, and irregularities, with the F region represented by F‐trace and Spread‐F features in the graphs. In this paper, IonoGAN, an enhanced neural network based on the Generative Adversarial Network architecture, is proposed for direct prediction of ionograms and the variation of these ionospheric conditions. This estimation is based on the trends of density profiles and the waves/structures presented in the ionogram sequence. The IonoGAN extends the spatiotemporal information‐preserving and perception‐augmented (STIP) ability by incorporating a Local‐Global discriminator to focus on the F region in ionograms. In addition, two scientific characteristics of ionospheric natural phenomena are extracted and used as constraints in the modeling: Spread‐F Classification Accuracy (SFCA) and Absolute Value of the Correlation Coefficient for the F trace (AVCC‐F). For training, ionograms from Hainan Fuke station (19.5°N, 109.1°E, magnetic 11°N) during 2002–2015 were processed into 36,435 sequences with Spread‐F phenomena and 147,147 sequences without. To strengthen their features, Spread‐F phenomena were further classified into types of frequency, range, mix, and strong range. After the parameter training, the IonoGAN achieved SFCA and AVCC‐F converging to their optimal values: on the 2016 test set, SFCA = 90.92%, AVCC‐F = 0.6917. This modification enables the network to effectively capture the distinct features of the ionospheric F trace and the Spread‐F phenomenon during both quiet and disturbed periods. |
| format | Article |
| id | doaj-art-01a94ded067d4d31b045e9a5166d423a |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-01a94ded067d4d31b045e9a5166d423a2025-08-20T03:33:41ZengWileySpace Weather1542-73902025-06-01236n/an/a10.1029/2025SW004463IonoGAN: An Enhanced Model for Forecasting Quiet and Disturbed Ionospheric Features From Predicted IonogramsChu Qiu0Jinhui Cai1Zheng Wang2Pengdong Gao3Guojun Wang4Quan Qi5Bo Wang6Zhengwei Cheng7Jiankui Shi8Yajun Zhu9Xiao Wang10Kai Ding11Key Laboratory of Media Audio & Video (Communication University of China) Ministry of Education Beijing ChinaState Key Laboratory of Media Convergence and Communication Communication University of China Beijing ChinaState Key Laboratory of Solar Activity and Space Weather National Space Science Center Chinese Academy of Sciences Beijing ChinaKey Laboratory of Media Audio & Video (Communication University of China) Ministry of Education Beijing ChinaState Key Laboratory of Solar Activity and Space Weather National Space Science Center Chinese Academy of Sciences Beijing ChinaKey Laboratory of Media Audio & Video (Communication University of China) Ministry of Education Beijing ChinaKey Laboratory of Media Audio & Video (Communication University of China) Ministry of Education Beijing ChinaState Key Laboratory of Solar Activity and Space Weather National Space Science Center Chinese Academy of Sciences Beijing ChinaState Key Laboratory of Solar Activity and Space Weather National Space Science Center Chinese Academy of Sciences Beijing ChinaState Key Laboratory of Solar Activity and Space Weather National Space Science Center Chinese Academy of Sciences Beijing ChinaState Key Laboratory of Solar Activity and Space Weather National Space Science Center Chinese Academy of Sciences Beijing ChinaState Key Laboratory of Solar Activity and Space Weather National Space Science Center Chinese Academy of Sciences Beijing ChinaAbstract Ionograms are radar echo graphs that depict vertical ionospheric density profiles, structures, fluctuations, and irregularities, with the F region represented by F‐trace and Spread‐F features in the graphs. In this paper, IonoGAN, an enhanced neural network based on the Generative Adversarial Network architecture, is proposed for direct prediction of ionograms and the variation of these ionospheric conditions. This estimation is based on the trends of density profiles and the waves/structures presented in the ionogram sequence. The IonoGAN extends the spatiotemporal information‐preserving and perception‐augmented (STIP) ability by incorporating a Local‐Global discriminator to focus on the F region in ionograms. In addition, two scientific characteristics of ionospheric natural phenomena are extracted and used as constraints in the modeling: Spread‐F Classification Accuracy (SFCA) and Absolute Value of the Correlation Coefficient for the F trace (AVCC‐F). For training, ionograms from Hainan Fuke station (19.5°N, 109.1°E, magnetic 11°N) during 2002–2015 were processed into 36,435 sequences with Spread‐F phenomena and 147,147 sequences without. To strengthen their features, Spread‐F phenomena were further classified into types of frequency, range, mix, and strong range. After the parameter training, the IonoGAN achieved SFCA and AVCC‐F converging to their optimal values: on the 2016 test set, SFCA = 90.92%, AVCC‐F = 0.6917. This modification enables the network to effectively capture the distinct features of the ionospheric F trace and the Spread‐F phenomenon during both quiet and disturbed periods.https://doi.org/10.1029/2025SW004463ionoganinongram predictionspread‐fneural networkionospheric disturbanceSTIP |
| spellingShingle | Chu Qiu Jinhui Cai Zheng Wang Pengdong Gao Guojun Wang Quan Qi Bo Wang Zhengwei Cheng Jiankui Shi Yajun Zhu Xiao Wang Kai Ding IonoGAN: An Enhanced Model for Forecasting Quiet and Disturbed Ionospheric Features From Predicted Ionograms Space Weather ionogan inongram prediction spread‐f neural network ionospheric disturbance STIP |
| title | IonoGAN: An Enhanced Model for Forecasting Quiet and Disturbed Ionospheric Features From Predicted Ionograms |
| title_full | IonoGAN: An Enhanced Model for Forecasting Quiet and Disturbed Ionospheric Features From Predicted Ionograms |
| title_fullStr | IonoGAN: An Enhanced Model for Forecasting Quiet and Disturbed Ionospheric Features From Predicted Ionograms |
| title_full_unstemmed | IonoGAN: An Enhanced Model for Forecasting Quiet and Disturbed Ionospheric Features From Predicted Ionograms |
| title_short | IonoGAN: An Enhanced Model for Forecasting Quiet and Disturbed Ionospheric Features From Predicted Ionograms |
| title_sort | ionogan an enhanced model for forecasting quiet and disturbed ionospheric features from predicted ionograms |
| topic | ionogan inongram prediction spread‐f neural network ionospheric disturbance STIP |
| url | https://doi.org/10.1029/2025SW004463 |
| work_keys_str_mv | AT chuqiu ionogananenhancedmodelforforecastingquietanddisturbedionosphericfeaturesfrompredictedionograms AT jinhuicai ionogananenhancedmodelforforecastingquietanddisturbedionosphericfeaturesfrompredictedionograms AT zhengwang ionogananenhancedmodelforforecastingquietanddisturbedionosphericfeaturesfrompredictedionograms AT pengdonggao ionogananenhancedmodelforforecastingquietanddisturbedionosphericfeaturesfrompredictedionograms AT guojunwang ionogananenhancedmodelforforecastingquietanddisturbedionosphericfeaturesfrompredictedionograms AT quanqi ionogananenhancedmodelforforecastingquietanddisturbedionosphericfeaturesfrompredictedionograms AT bowang ionogananenhancedmodelforforecastingquietanddisturbedionosphericfeaturesfrompredictedionograms AT zhengweicheng ionogananenhancedmodelforforecastingquietanddisturbedionosphericfeaturesfrompredictedionograms AT jiankuishi ionogananenhancedmodelforforecastingquietanddisturbedionosphericfeaturesfrompredictedionograms AT yajunzhu ionogananenhancedmodelforforecastingquietanddisturbedionosphericfeaturesfrompredictedionograms AT xiaowang ionogananenhancedmodelforforecastingquietanddisturbedionosphericfeaturesfrompredictedionograms AT kaiding ionogananenhancedmodelforforecastingquietanddisturbedionosphericfeaturesfrompredictedionograms |