From regional to global emulation: characterising regional differences to increase transfer learning performance

Surface methane emissions can be estimated from atmospheric observations using inverse modelling systems, which often rely on Lagrangian Particle Dispersion Models (LPDMs) to simulate how the gas is transported through the atmosphere using meteorological fields. However, LPDM-based techniques struggle to scale to the size of modern satellite datasets, as one LPDM run is needed for each observation, taking on the order of 10 CPU-minutes to complete. Previously, we introduced the Machine Learning model GATES (Graph-Neural-Network Atmospheric Transport Emulation System), which can replicate LPDM outputs 1000x faster than the physics-based model, and demonstrated its application to infer emissions over South America. Training GATES over other world regions and comparing cross-regional performance shows that the learnt transport is domain-specific, consistent with the strong heterogeneity in wind patterns and topography across continents. In this presentation, we discuss transfer learning techniques and characterisation of regional differences in wind patterns, topography, data availability and the shape and magnitude of LPDM outputs, to increase transfer learning performance. This work builds capabilities towards efficient, global methane emissions emulation.