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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Aug 2023
Research article |
| 18 Aug 2023
| 18 Aug 2023
A blended TROPOMI+GOSAT satellite data product for atmospheric methane using machine learning to correct retrieval biases
Nicholas Balasus
CORRESPONDING AUTHOR
School of Engineering and Applied Sciences, Harvard University, Cambridge, USA
Daniel J. Jacob
School of Engineering and Applied Sciences, Harvard University, Cambridge, USA
Department of Earth and Planetary Sciences, Harvard University, Cambridge, USA
Alba Lorente
SRON Netherlands Institute for Space Research, Leiden, the Netherlands
Joannes D. Maasakkers
SRON Netherlands Institute for Space Research, Leiden, the Netherlands
Robert J. Parker
National Centre for Earth Observation, University of Leicester, Leicester, UK
Earth Observation Science, School of Physics and Astronomy, University of Leicester, Leicester, UK
Hartmut Boesch
National Centre for Earth Observation, University of Leicester, Leicester, UK
Earth Observation Science, School of Physics and Astronomy, University of Leicester, Leicester, UK
now at: Institute of Environmental Physics (IUP), University of Bremen FB1, Bremen, Germany
Zichong Chen
School of Engineering and Applied Sciences, Harvard University, Cambridge, USA
Makoto M. Kelp
Department of Earth and Planetary Sciences, Harvard University, Cambridge, USA
Hannah Nesser
School of Engineering and Applied Sciences, Harvard University, Cambridge, USA
Daniel J. Varon
School of Engineering and Applied Sciences, Harvard University, Cambridge, USA
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Cited
8 citations as recorded by crossref.
- Short-term trend and temporal variations in atmospheric methane at an Atlantic coastal site in Southwestern Europe R. Padilla et al. 10.1016/j.atmosenv.2024.120665
- Satellite Data and Machine Learning for Benchmarking Methane Concentrations in the Canadian Dairy Industry H. Bi & S. Neethirajan 10.3390/su162310400
- High-resolution US methane emissions inferred from an inversion of 2019 TROPOMI satellite data: contributions from individual states, urban areas, and landfills H. Nesser et al. 10.5194/acp-24-5069-2024
- A bias-corrected GEMS geostationary satellite product for nitrogen dioxide using machine learning to enforce consistency with the TROPOMI satellite instrument Y. Oak et al. 10.5194/amt-17-5147-2024
- Developing unbiased estimation of atmospheric methane via machine learning and multiobjective programming based on TROPOMI and GOSAT data K. Li et al. 10.1016/j.rse.2024.114039
- Comparative Analysis and High−Precision Modeling of Tropospheric CH4 in the Yangtze River Delta of China Obtained from the TROPOMI and GOSAT T. Cai & C. Xiang 10.3390/atmos15030266
- Interpreting the Seasonality of Atmospheric Methane J. East et al. 10.1029/2024GL108494
- Urban methane emission monitoring across North America using TROPOMI data: an analytical inversion approach M. Hemati et al. 10.1038/s41598-024-58995-8
6 citations as recorded by crossref.
- Short-term trend and temporal variations in atmospheric methane at an Atlantic coastal site in Southwestern Europe R. Padilla et al. 10.1016/j.atmosenv.2024.120665
- Satellite Data and Machine Learning for Benchmarking Methane Concentrations in the Canadian Dairy Industry H. Bi & S. Neethirajan 10.3390/su162310400
- High-resolution US methane emissions inferred from an inversion of 2019 TROPOMI satellite data: contributions from individual states, urban areas, and landfills H. Nesser et al. 10.5194/acp-24-5069-2024
- A bias-corrected GEMS geostationary satellite product for nitrogen dioxide using machine learning to enforce consistency with the TROPOMI satellite instrument Y. Oak et al. 10.5194/amt-17-5147-2024
- Developing unbiased estimation of atmospheric methane via machine learning and multiobjective programming based on TROPOMI and GOSAT data K. Li et al. 10.1016/j.rse.2024.114039
- Comparative Analysis and High−Precision Modeling of Tropospheric CH4 in the Yangtze River Delta of China Obtained from the TROPOMI and GOSAT T. Cai & C. Xiang 10.3390/atmos15030266
Latest update: 13 Dec 2024
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.
We use machine learning to remove biases in TROPOMI satellite observations of atmospheric...
