Towards emulated Lagrangian particle dispersion model footprints for satellite observations

Lagrangian particle dispersion models (LPDMs) have been used extensively to calculate source-receptor relationships (“footprints”) for use in greenhouse gas (GHG) flux inversions. However, because a backward-running model simulation is required for each data point, LPDMs do not scale well to very large datasets, which makes them unsuitable for use in GHG inversions using high-resolution satellite instruments such as TROPOMI. In this work, we demonstrate how Machine Learning (ML) can be used to accelerate footprint production, by first presenting a proof-of-concept emulator for ground-based site observations, and then discussing work in progress to create an emulator suitable to satellite observations. In Fillola et al (2023), we presented a ML emulator for NAME, the Met Office’s LPDM, which outputs footprints for a small region around an observation point using purely meteorological variables as inputs. The footprint magnitude at each grid cell in the domain is modelled independently using gradient-boosted regression trees. The model is evaluated for seven sites, producing a footprint in 10ms, compared to around 10 minutes for the 3D simulator, and achieving R2 values between 0.6 and 0.8 for CH4 concentrations simulated at the sites when compared to the timeseries generated by NAME. Following on from this work, we demonstrate how this same emulator can be applied to satellite data to reproduce footprints immediately around any measurement point in the domain, evaluating this application with data for Brazil and North Africa and obtaining R2 values of around 0.5 for simulated CH4 concentrations. Furthermore, we propose new emulator architectures for LPDMs applied to satellite observations. These new architectures should tackle some of the weaknesses in the existing approach, for example, by propagating information more flexibly in space and time, potentially improving accuracy of the derived footprints and extending the prediction capabilities to bigger domains.