EGU24-15144, updated on 09 Mar 2024
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

A Graph Neural Network emulator for greenhouse gas emissions inference

Elena Fillola1,2, Raul Santos-Rodriguez1, and Matt Rigby2
Elena Fillola et al.
  • 1School of Engineering Mathematics and Technology, University of Bristol, Bristol, United Kingdom of Great Britain – England, Scotland, Wales
  • 2Atmospheric Chemistry Research Group, University of Bristol, Bristol, United Kingdom of Great Britain – England, Scotland, Wales

Inverse modelling systems relying on Lagrangian Particle Dispersion Models (LPDMs) are a popular way to quantify greenhouse gas emissions using atmospheric observations, providing independent evaluation of countries' self-reported emissions. For each GHG measurement, the LPDM performs backward-running simulations of particle transport in the atmosphere, calculating source-receptor relationships (“footprints”). These reflect the upwind areas where emissions would contribute to the measurement. However, the increased volume of satellite measurements from high-resolution instruments like TROPOMI cause computational bottlenecks, limiting the amount of data that can be processed for inference. Previous approaches to speed up footprint generation revolve around interpolation, therefore still requiring expensive new runs. In this work, we present the first machine learning-driven LPDM emulator that once trained, can approximate satellite footprints using only meteorology and topography. The emulator uses Graph Neural Networks in an Encode-Process-Decode structure, similar to Google’s Graphcast [1], representing latitude-longitude coordinates as nodes in a graph. We apply the model for GOSAT measurements over Brazil to emulate footprints produced by the UK Met Office’s NAME LPDM, training on data for 2014 and 2015 on a domain of size approximately 1600x1200km at a resolution of 0.352x0.234 degrees. Once trained, the emulator can produce footprints for a domain of up to approximately 6500x5000km, leveraging the flexibility of GNNs. We evaluate the emulator for footprints produced across 2016 on the 6500x5000km domain size, achieving intersection-over-union scores of over 40% and normalised mean absolute errors of under 30% for simulated CH4 concentrations. As well as demonstrating the emulator as a standalone AI application, we show how to integrate it with the full GHG emissions pipeline to quantify Brazil’s emissions. This method demonstrates the potential of GNNs for atmospheric dispersion applications and paves the way for large-scale near-real time emissions emulation.

 [1] Remi Lam et al.,Learning skillful medium-range global weather forecasting. Science 382,1416-1421 (2023). DOI:10.1126/science.adi2336

How to cite: Fillola, E., Santos-Rodriguez, R., and Rigby, M.: A Graph Neural Network emulator for greenhouse gas emissions inference, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15144,, 2024.