Articles | Volume 18, issue 5
https://doi.org/10.5194/gmd-18-1661-2025
© 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/gmd-18-1661-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
12 Mar 2025
Development and technical paper |
| 12 Mar 2025
| 12 Mar 2025
FootNet v1.0: development of a machine learning emulator of atmospheric transport
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
Nikhil Dadheech
Department of Atmospheric and Climate Science, University of Washington, Seattle, WA, USA
Tammy M. Thompson
Environmental Defense Fund, Boulder, CO, USA
Department of Atmospheric and Climate Science, University of Washington, Seattle, WA, USA
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Short summary
It is computationally expensive to infer greenhouse gas (GHG) emissions using atmospheric observations. This is partly due to the detailed model used to represent atmospheric transport. We demonstrate how a machine learning (ML) model can be used to simulate high-resolution atmospheric transport. This type of ML model will help estimate GHG emissions using dense observations, which are becoming increasingly common with the proliferation of urban monitoring networks and geostationary satellites.
It is computationally expensive to infer greenhouse gas (GHG) emissions using atmospheric...
