Articles | Volume 18, issue 5
https://doi.org/10.5194/gmd-18-1661-2025
https://doi.org/10.5194/gmd-18-1661-2025
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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Cited articles

Baker, D. F., Doney, S. C., and Schimel, D. S.: Variational data assimilation for atmospheric CO2, Tellus B, 58, 359–365, https://doi.org/10.1111/j.1600-0889.2006.00218.x, 2006. a
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Dadheech, N., He, T.-L., and Turner, A. J.: High-resolution greenhouse gas flux inversions using a machine learning surrogate model for atmospheric transport, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-2918, 2024. a, b
Dobbins, R.: Atmospheric Motion and Air Pollution: An Introduction for Students of Engineering and Science, A Wiley-interscience publication, Wiley, ISBN 9780471216759, https://books.google.com/books?id=kDhSAAAAMAAJ (last access: 6 March 2025), 1979. a
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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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