Accelerating GHG Emissions Inference: A Lagrangian Particle Dispersion Model Emulator Using Graph Neural Networks (Papers Track)

Elena Fillola (University of Bristol); Raul Santos Rodriguez (University of Bristol); Matt Rigby (University of Bristol)

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Climate Science & Modeling


Inverse modelling systems relying on Lagrangian Particle Dispersion Models (LPDMs) are a popular way to quantify greenhouse gas (GHG) emissions using atmospheric observations, providing independent validation to countries' self-reported emissions. However, the increased volume of satellite measurements cannot be fully leveraged due to computational bottlenecks. Here, we propose a data-driven architecture with Graph Neural Networks that emulates the outputs of LPDMs using only meteorological inputs, and demonstrate it in application with preliminary results for satellite measurements over Brazil.