Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
ACP
Articles | Volume 23, issue 20
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

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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.
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