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.
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
9 citations as recorded by crossref.
- Editorial: Observations and simulations of layering phenomena in the middle/upper atmosphere and ionosphere B. Yu et al. 10.3389/fspas.2023.1361434
- Dynamics, chemistry, and modeling studies in the aviation and aerospace transition zone Z. Sheng et al. 10.1016/j.xinn.2025.101012
- Analysis and simulation of the relationship between Es layer and solar activity Y. Zhang et al. 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. 10.3390/atmos16080904
- Advancing Ionospheric Irregularity Reconstruction With ICON/MIGHTI Wind‐Driven Insights P. Tian et al. 10.1029/2025GL115666
- Mechanisms Underlying the Changes in Sporadic E Layers During Sudden Stratospheric Warming H. Zheng et al. 10.3390/atmos15101258
- 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. 10.1029/2024JA033356
- Global Empirical Model of Sporadic-E Occurrence Rates E. Parsch et al. 10.3389/fspas.2024.1434367
- Ionospheric irregularity reconstruction using multisource data fusion via deep learning P. Tian et al. 10.5194/acp-23-13413-2023
7 citations as recorded by crossref.
- Editorial: Observations and simulations of layering phenomena in the middle/upper atmosphere and ionosphere B. Yu et al. 10.3389/fspas.2023.1361434
- Dynamics, chemistry, and modeling studies in the aviation and aerospace transition zone Z. Sheng et al. 10.1016/j.xinn.2025.101012
- Analysis and simulation of the relationship between Es layer and solar activity Y. Zhang et al. 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. 10.3390/atmos16080904
- Advancing Ionospheric Irregularity Reconstruction With ICON/MIGHTI Wind‐Driven Insights P. Tian et al. 10.1029/2025GL115666
- Mechanisms Underlying the Changes in Sporadic E Layers During Sudden Stratospheric Warming H. Zheng et al. 10.3390/atmos15101258
- 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. 10.1029/2024JA033356
Latest update: 22 Aug 2025
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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