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

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

doi:https://doi.org/10.5194/amt-16-3787-2023

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

Data sets

Blended TROPOMI+GOSAT Satellite Data Product for Atmospheric Methane Nicholas Balasus https://dataverse.harvard.edu/dataverse/blended-tropomi-gosat-methane

TROPOMI Methane Product ESA https://s5phub.copernicus.eu/dhus/#/home

University of Leicester GOSAT Proxy XCH4 v9.0 Robert Parker and Hartmut Boesch https://doi.org/10.5285/18ef8247f52a4cb6a14013f8235cc1eb

Model code and software

nicholasbalasus/blended_tropomi_gosat_methane: 65 AMT Nicholas Balasus https://doi.org/10.5281/zenodo.8136738

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.