Abstract. Atmospheric observation-based “inverse” greenhouse gas flux estimates are increasingly important to evaluate national inventories, with a dramatic improvement in “top-down” flux inference expected in the coming years due to the rapidly growing number of measurements from space. However, many well-established inverse modelling techniques face significant computational challenges scaling to modern satellite datasets, particularly those that rely on Lagrangian Particle Dispersion Models (LPDM) to simulate atmospheric transport. Here, we introduce GATES (Graph-Neural-Network Atmospheric Transport Emulation System), a data-driven LPDM emulator which outputs source-receptor relationships (“footprints”) using only meteorology and surface data as inputs, approximately 1000x times faster than an LPDM. We demonstrate GATES’s skill in estimating footprints over South America and integrate it into an emissions estimation pipeline, evaluating Brazil’s methane emissions using GOSAT (Greenhouse Gases Observing Satellite) observations for 2016 and 2018 and finding emissions that are consistent in space and time with the physics-driven estimate. This work highlights the potential of machine learning-based emulators like GATES to overcome a key bottleneck in large-scale, satellite-based inverse modeling, accelerating greenhouse gas emissions estimation and enabling timely, improved evaluations of national GHG inventories.
Fillola et al. (Wed,) studied this question.