Ionospheric irregularity reconstruction using multisource data fusion via deep learning

Tian, Penghao; Yu, Bingkun; Ye, Hailun; Xue, Xianghui; Wu, Jianfei; Chen, Tingdi

doi:https://doi.org/10.5194/acp-23-13413-2023

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.

https://doi.org/10.5194/acp-23-13413-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 23, issue 20

Research article

|

24 Oct 2023

Research article |

| 24 Oct 2023

Ionospheric irregularity reconstruction using multisource data fusion via deep learning

Penghao Tian, Bingkun Yu, Hailun Ye, Xianghui Xue, Jianfei Wu, and Tingdi Chen

Supplement

https://doi.org/10.5194/acp-23-13413-2023-supplement

Data sets

ERA5 hourly data on pressure levels from 1940 to present H. Hersbach, B. Bell, P. Berrisford, G. Biavati, A. Horányi, J. Muñoz Sabater, J. Nicolas, C. Peubey, R. Radu, I. Rozum, D. Schepers, A. Simmons, C. Soci, D. Dee, and J.-N. Thépaut https://doi.org/10.24381/cds.bd0915c6

COSMIC-1 Data Products UCAR COSMIC Program https://doi.org/10.5065/ZD80-KD74

Solar wind spatial scales in and comparisons of hourly Wind and ACE plasma and magnetic field data (https://omniweb.gsfc.nasa.gov/) J. H. King and N. E. Papitashvili https://doi.org/10.1029/2004JA010649

Model code and software

Ionospheric Irregularities Reconstruction Using Multi-Source Data Fusion via Deep Learning P. Tian, B. Yu, H. Ye, X. Xue, J. Wu, and T. Chen https://doi.org/10.5281/zenodo.10016010

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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.