Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 18, issue 5
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.
https://doi.org/10.5194/gmd-18-1661-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 5
Development and technical paper
 | 
12 Mar 2025
Development and technical paper |  | 12 Mar 2025

FootNet v1.0: development of a machine learning emulator of atmospheric transport

Tai-Long He, Nikhil Dadheech, Tammy M. Thompson, and Alexander J. Turner

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Latest update: 13 Mar 2025
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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.
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