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


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2023-47', Anonymous Referee #1, 28 Apr 2023
    • AC1: 'Reply on RC1', Nicholas Balasus, 12 Jul 2023
  • RC2: 'Comment on amt-2023-47', Anonymous Referee #2, 01 Jul 2023
    • AC2: 'Reply on RC2', Nicholas Balasus, 12 Jul 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Nicholas Balasus on behalf of the Authors (12 Jul 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (15 Jul 2023) by Sandip Dhomse
AR by Nicholas Balasus on behalf of the Authors (16 Jul 2023)
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.