Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 16, issue 7
Articles | Volume 16, issue 7
https://doi.org/10.5194/gmd-16-1997-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-1997-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 7
Development and technical paper
 | 
12 Apr 2023
Development and technical paper |  | 12 Apr 2023

A machine learning emulator for Lagrangian particle dispersion model footprints: a case study using NAME

Elena Fillola, Raul Santos-Rodriguez, Alistair Manning, Simon O'Doherty, and Matt Rigby

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Short summary
Lagrangian particle dispersion models are used extensively for the estimation of greenhouse gas (GHG) fluxes using atmospheric observations. However, these models do not scale well as data volumes increase. Here, we develop a proof-of-concept machine learning emulator that can produce outputs similar to those of the dispersion model, but 50 000 times faster, using only meteorological inputs. This works demonstrates the potential of machine learning to accelerate GHG estimations across the globe.
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