Articles | Volume 25, issue 10
https://doi.org/10.5194/acp-25-5159-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-25-5159-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 May 2025
Research article |
| 21 May 2025
| 21 May 2025
High-resolution greenhouse gas flux inversions using a machine learning surrogate model for atmospheric transport
Nikhil Dadheech
Department of Atmospheric and Climate Science, University of Washington, Seattle, WA, USA
Tai-Long He
Department of Atmospheric and Climate Science, University of Washington, Seattle, WA, USA
now at: School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
Department of Atmospheric and Climate Science, University of Washington, Seattle, WA, USA
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Short summary
We developed an efficient GHG (greenhouse gas) flux inversion framework using a machine-learning emulator (FootNet) as a surrogate for an atmospheric transport model, resulting in a 650 × speedup. Paradoxically, the flux inversion using the ML (machine-learning) model outperforms the full-physics model in our case study. We attribute this to the ML model mitigating transport errors in the GHG flux inversion.
We developed an efficient GHG (greenhouse gas) flux inversion framework using a machine-learning...
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