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

Viewed

Total article views: 3,013 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,388 557 68 3,013 112 85 110
  • HTML: 2,388
  • PDF: 557
  • XML: 68
  • Total: 3,013
  • Supplement: 112
  • BibTeX: 85
  • EndNote: 110
Views and downloads (calculated since 07 Nov 2022)
Cumulative views and downloads (calculated since 07 Nov 2022)

Viewed (geographical distribution)

Total article views: 3,013 (including HTML, PDF, and XML) Thereof 2,866 with geography defined and 147 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 07 Nov 2025
Download
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.
Read more
Share