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

Supplement

https://doi.org/10.5194/gmd-16-1997-2023-supplement

Data sets

Sample dataset for "A machine learning emulator for Lagrangian particle dispersion model footprints" Elena Fillola https://doi.org/10.5281/zenodo.7254330

Operational Numerical Weather Prediction (NWP) Output from the UK Variable (UKV) Resolution Part of the Met Office Unified Model (UM) Met Office http://catalogue.ceda.ac.uk/uuid/292da1ccfebd650f6d123e53270016a8

Operational Numerical Weather Prediction (NWP) Output from the North Atlantic European (NAE) Part of the Met Office Unified Model (UM) Met Office http://catalogue.ceda.ac.uk/uuid/220f1c04ffe39af29233b78c2cf2699a

NWP-UKV: Met Office UK Atmospheric High Resolution Model data Met Office https://catalogue.ceda.ac.uk/uuid/f47bc62786394626b665e23b658d385f

NWP-UKV: Met Office UK Atmospheric High Resolution Model data Met Office https://catalogue.ceda.ac.uk/uuid/86df725b793b4b4cb0ca0646686bd783

The ALE/GAGE/AGAGE Data Base R. G. Prinn, R. F. Weiss, J. Arduini, et al. http://agage.mit.edu/data

UK DECC (Deriving Emissions linked to Climate Change) Network S. O'Doherty, D. Say, K. Stanley, et al. http://catalogue.ceda.ac.uk/uuid/f5b38d1654d84b03ba79060746541e4f

Model code and software

elenafillo/LPDM-emulation-trees: LPDM-emulation-trees v1.0 Elena Fillola https://doi.org/10.5281/zenodo.7254667

ACRG-Bristol/acrg: ACRG v0.2.0 (v0.2.0) Matt Rigby, Rachel Tunnicliffe, Luke Western, Hannah Chawner, Anita Ganesan, Alice Ramsden, Gareth Jones, Dickon Young, Rebecca Ward, Angharad Stell, Alecia Nickless, and Joe Pitt https://doi.org/10.5281/zenodo.6834888

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