Towards a Fully Machine Learning–Driven Methane Emissions Inference Pipeline at Global Scale

The growing availability of satellite-based methane observations provides new opportunities to improve estimates of surface emissions. Inverse modelling frameworks commonly rely on Lagrangian Particle Dispersion Models (LPDMs) to simulate atmospheric transport and derive source–receptor relationships (“footprints”), but these approaches are computationally expensive and struggle to scale to the rapidly increasing volume of satellite data.
Previously, we introduced GATES (Graph-Neural-Network Atmospheric Transport Emulation System), a machine learning (ML) based emulator capable of reproducing LPDM footprint sensitivities three orders of magnitude faster than the underlying physics-based model, and demonstrated its application to infer methane emissions over South America. While such footprints capture the local contribution from surface fluxes, observed methane concentrations are often dominated by the background mole fraction associated with large-scale atmospheric transport entering the domain. Despite its importance, this background component has received comparatively little attention in ML-based transport emulation.
Here, we present a machine learning emulator for background methane mole fractions, designed to reproduce the contribution from outside the modelled domain to observed concentrations using meteorological and atmospheric state information. By combining this background emulator with the existing GATES footprint emulator, we construct a fully ML-driven pipeline capable of predicting total methane concentrations without requiring explicit LPDM simulations. We demonstrate that this framework reproduces key spatial and temporal characteristics of LPDM-based background estimates over South America, including seasonal structure, daily variability, and regional patterns, as well as its performance within inversions to estimate Brazil’s methane emissions.
We further assess the scalability of the approach by applying the footprint emulator to regions outside the original training domain. While the model performs well when trained and evaluated within the same region, performance degrades when applied to unseen domains with different meteorological regimes. These results indicate that atmospheric transport learning is strongly domain-specific, highlighting both the potential and the limitations of transfer learning, and underscoring the need for region-specific training data when extending the approach to global emulation.
This work demonstrates the feasibility of a fully ML-driven atmospheric transport and background modelling framework for methane inversion, offering the next steps towards computationally efficient, satellite-based emissions monitoring.