Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 17, issue 14
Articles | Volume 17, issue 14
https://doi.org/10.5194/amt-17-4317-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/amt-17-4317-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 14
Research article
 | 
19 Jul 2024
Research article |  | 19 Jul 2024

Aerosol optical depth data fusion with Geostationary Korea Multi-Purpose Satellite (GEO-KOMPSAT-2) instruments GEMS, AMI, and GOCI-II: statistical and deep neural network methods

Minseok Kim, Jhoon Kim, Hyunkwang Lim, Seoyoung Lee, Yeseul Cho, Yun-Gon Lee, Sujung Go, and Kyunghwa Lee

Viewed

Total article views: 754 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
538 173 43 754 34 32
  • HTML: 538
  • PDF: 173
  • XML: 43
  • Total: 754
  • BibTeX: 34
  • EndNote: 32
Views and downloads (calculated since 06 Dec 2023)
Cumulative views and downloads (calculated since 06 Dec 2023)

Viewed (geographical distribution)

Total article views: 754 (including HTML, PDF, and XML) Thereof 748 with geography defined and 6 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 26 Jul 2024
Download
Short summary
Information about aerosol loading in the atmosphere can be collected from various satellite instruments. Aerosol products from various satellite instruments have their own error characteristics. This study statistically merged aerosol optical depth datasets from multiple instruments aboard geostationary satellites considering uncertainties. Also, a deep neural network technique is adopted for aerosol data merging.
Read more
Special issue
GEMS: first year in operation (AMT/ACP inter-journal SI)