Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 16, issue 16
Articles | Volume 16, issue 16
https://doi.org/10.5194/amt-16-3787-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-16-3787-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 16
Research article
 | 
18 Aug 2023
Research article |  | 18 Aug 2023

A blended TROPOMI+GOSAT satellite data product for atmospheric methane using machine learning to correct retrieval biases

Nicholas Balasus, Daniel J. Jacob, Alba Lorente, Joannes D. Maasakkers, Robert J. Parker, Hartmut Boesch, Zichong Chen, Makoto M. Kelp, Hannah Nesser, and Daniel J. Varon

Viewed

Total article views: 4,764 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
3,490 1,185 89 4,764 92 98
  • HTML: 3,490
  • PDF: 1,185
  • XML: 89
  • Total: 4,764
  • BibTeX: 92
  • EndNote: 98
Views and downloads (calculated since 10 Mar 2023)
Cumulative views and downloads (calculated since 10 Mar 2023)

Viewed (geographical distribution)

Total article views: 4,764 (including HTML, PDF, and XML) Thereof 4,653 with geography defined and 111 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 20 Nov 2024
Download
Short summary
We use machine learning to remove biases in TROPOMI satellite observations of atmospheric methane, with GOSAT observations serving as a reference. We find that the TROPOMI biases relative to GOSAT are related to the presence of aerosols and clouds, the surface brightness, and the specific detector that makes the observation aboard TROPOMI. The resulting blended TROPOMI+GOSAT product is more reliable for quantifying methane emissions.
Read more