Articles | Volume 23, issue 20
https://doi.org/10.5194/acp-23-13413-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-23-13413-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Oct 2023
Research article |
| 24 Oct 2023
| 24 Oct 2023
Ionospheric irregularity reconstruction using multisource data fusion via deep learning
Penghao Tian
Deep Space Exploration Laboratory/School of Earth and Space Sciences, University of Science and Technology of China, Hefei, China
Institute of Deep Space Sciences, Deep Space Exploration Laboratory, Hefei, China
Deep Space Exploration Laboratory/School of Earth and Space Sciences, University of Science and Technology of China, Hefei, China
Anhui Mengcheng Geophysics National Observation and Research Station, University of Science and Technology of China, Hefei, China
Hailun Ye
Deep Space Exploration Laboratory/School of Earth and Space Sciences, University of Science and Technology of China, Hefei, China
Deep Space Exploration Laboratory/School of Earth and Space Sciences, University of Science and Technology of China, Hefei, China
Anhui Mengcheng Geophysics National Observation and Research Station, University of Science and Technology of China, Hefei, China
Hefei National Laboratory, University of Science and Technology of China, Hefei, China
Jianfei Wu
Deep Space Exploration Laboratory/School of Earth and Space Sciences, University of Science and Technology of China, Hefei, China
Tingdi Chen
Deep Space Exploration Laboratory/School of Earth and Space Sciences, University of Science and Technology of China, Hefei, China
Anhui Mengcheng Geophysics National Observation and Research Station, University of Science and Technology of China, Hefei, China
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Cited
15 citations as recorded by crossref.
- Sporadic E layer Enhancement Following Orionids Outbursts Y. Tian et al. https://doi.org/10.1016/j.asr.2026.10.071
- Editorial: Observations and simulations of layering phenomena in the middle/upper atmosphere and ionosphere B. Yu et al. https://doi.org/10.3389/fspas.2023.1361434
- Dynamics, chemistry, and modeling studies in the aviation and aerospace transition zone Z. Sheng et al. https://doi.org/10.1016/j.xinn.2025.101012
- Complexity, Interdisciplinarity, Big Data and AI in Ionosphere Research: Towards a Paradigm Shift S. Radicella https://doi.org/10.3390/atmos17030271
- Advancing Ionospheric Irregularity Reconstruction With ICON/MIGHTI Wind‐Driven Insights P. Tian et al. https://doi.org/10.1029/2025GL115666
- Near real-time F-region electron density prediction using a hybrid CNN-Transformer model: GNSS and space-weather applications R. Satheeskumar et al. https://doi.org/10.1016/j.asr.2026.08.101
- Mechanisms Underlying the Changes in Sporadic E Layers During Sudden Stratospheric Warming H. Zheng et al. https://doi.org/10.3390/atmos15101258
- MPC-based station-keeping control method for stratospheric airships under predicted wind conditions F. Bai et al. https://doi.org/10.1088/1742-6596/3308/1/012007
- Global ionospheric sporadic E intensity prediction from GNSS RO using a novel stacking machine learning method incorporated with physical observations T. Hu et al. https://doi.org/10.5194/acp-25-11517-2025
- Distinguishing Auroral-E and Sporadic-E in GNSS Radio Occultation Measurements S. Hyder et al. https://doi.org/10.3390/rs18203596
- Analysis and simulation of the relationship between Es layer and solar activity Y. Zhang et al. https://doi.org/10.1016/j.asr.2025.04.064
- Analysis of the Relation Between Solar Activity and Parameters of the Sporadic E Layer Y. Zhang et al. https://doi.org/10.3390/atmos16080904
- Short-term forecast of ionospheric sporadic E intensity fusing physical parameters via deep learning T. Hu et al. https://doi.org/10.1007/s10291-025-01947-0
- The Characteristics and Simulation of Sporadic E Layers in Ascending and Descending Phases of the Solar Cycle at Mid‐Latitude Stations Y. Zhang et al. https://doi.org/10.1029/2024JA033356
- A global model of the occurrence probability of sporadic E for the International Reference Ionosphere V. Truhlik et al. https://doi.org/10.1016/j.asr.2026.09.071
15 citations as recorded by crossref.
- Sporadic E layer Enhancement Following Orionids Outbursts Y. Tian et al. https://doi.org/10.1016/j.asr.2026.10.071
- Editorial: Observations and simulations of layering phenomena in the middle/upper atmosphere and ionosphere B. Yu et al. https://doi.org/10.3389/fspas.2023.1361434
- Dynamics, chemistry, and modeling studies in the aviation and aerospace transition zone Z. Sheng et al. https://doi.org/10.1016/j.xinn.2025.101012
- Complexity, Interdisciplinarity, Big Data and AI in Ionosphere Research: Towards a Paradigm Shift S. Radicella https://doi.org/10.3390/atmos17030271
- Advancing Ionospheric Irregularity Reconstruction With ICON/MIGHTI Wind‐Driven Insights P. Tian et al. https://doi.org/10.1029/2025GL115666
- Near real-time F-region electron density prediction using a hybrid CNN-Transformer model: GNSS and space-weather applications R. Satheeskumar et al. https://doi.org/10.1016/j.asr.2026.08.101
- Mechanisms Underlying the Changes in Sporadic E Layers During Sudden Stratospheric Warming H. Zheng et al. https://doi.org/10.3390/atmos15101258
- MPC-based station-keeping control method for stratospheric airships under predicted wind conditions F. Bai et al. https://doi.org/10.1088/1742-6596/3308/1/012007
- Global ionospheric sporadic E intensity prediction from GNSS RO using a novel stacking machine learning method incorporated with physical observations T. Hu et al. https://doi.org/10.5194/acp-25-11517-2025
- Distinguishing Auroral-E and Sporadic-E in GNSS Radio Occultation Measurements S. Hyder et al. https://doi.org/10.3390/rs18203596
- Analysis and simulation of the relationship between Es layer and solar activity Y. Zhang et al. https://doi.org/10.1016/j.asr.2025.04.064
- Analysis of the Relation Between Solar Activity and Parameters of the Sporadic E Layer Y. Zhang et al. https://doi.org/10.3390/atmos16080904
- Short-term forecast of ionospheric sporadic E intensity fusing physical parameters via deep learning T. Hu et al. https://doi.org/10.1007/s10291-025-01947-0
- The Characteristics and Simulation of Sporadic E Layers in Ascending and Descending Phases of the Solar Cycle at Mid‐Latitude Stations Y. Zhang et al. https://doi.org/10.1029/2024JA033356
- A global model of the occurrence probability of sporadic E for the International Reference Ionosphere V. Truhlik et al. https://doi.org/10.1016/j.asr.2026.09.071
Saved (final revised paper)
Latest update: 06 Nov 2026
Short summary
Modeling and prediction of ionospheric irregularities is an important topic in upper-atmospheric and upper-ionospheric physics. We proposed an artificial intelligence model to reconstruct the E-region ionospheric irregularities and first developed an open-source application for the community. The model reveals complex relationships between ionospheric irregularities and external driving factors. The findings suggest that spatiotemporal information plays an important role in the reconstruction.
Modeling and prediction of ionospheric irregularities is an important topic in upper-atmospheric...
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