Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 16, issue 1
Articles | Volume 16, issue 1
https://doi.org/10.5194/amt-16-153-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/amt-16-153-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 1
Research article
 | 
13 Jan 2023
Research article |  | 13 Jan 2023

Spectral replacement using machine learning methods for continuous mapping of the Geostationary Environment Monitoring Spectrometer (GEMS)

Yeeun Lee, Myoung-Hwan Ahn, Mina Kang, and Mijin Eo

Viewed

Total article views: 2,730 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,779 862 89 2,730 81 91
  • HTML: 1,779
  • PDF: 862
  • XML: 89
  • Total: 2,730
  • BibTeX: 81
  • EndNote: 91
Views and downloads (calculated since 17 Feb 2022)
Cumulative views and downloads (calculated since 17 Feb 2022)

Viewed (geographical distribution)

Total article views: 2,730 (including HTML, PDF, and XML) Thereof 2,721 with geography defined and 9 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 25 Oct 2025
Download
Short summary
This study aims to verify that a partly defective hyperspectral measurement can be successfully reproduced with concise machine learning models coupled with principal component analysis. Evaluation of the approach is performed with radiances and retrieval results of ozone and cloud properties. Considering that GEMS is the first geostationary UV–VIS hyperspectral spectrometer, we expect our findings can be introduced further to similar geostationary environmental instruments to be launched soon.
Read more
Share
Special issue
GEMS: first year in operation (AMT/ACP inter-journal SI)