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

Viewed

Total article views: 1,921 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,535 330 56 1,921 100 33 30
  • HTML: 1,535
  • PDF: 330
  • XML: 56
  • Total: 1,921
  • Supplement: 100
  • BibTeX: 33
  • EndNote: 30
Views and downloads (calculated since 19 Jun 2023)
Cumulative views and downloads (calculated since 19 Jun 2023)

Viewed (geographical distribution)

Total article views: 1,921 (including HTML, PDF, and XML) Thereof 1,966 with geography defined and -45 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 30 Dec 2024
Download
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.
Read more
Altmetrics
Final-revised paper
Preprint