Estimating greenhouse gas emissions using the next generation of satellite observations

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Our team has shown that GHG emissions can be inferred at national scales from the previous generation of satellite observations, based on simulations of atmospheric gas transport using the Met Office NAME model and Bayesian methods (e.g. Ganesan, et al., 2017). However, the extension of current approaches to the new generation of satellite data are challenging due to the size of datasets involved. Here, we propose to use novel data science approaches to explore how to infer GHG fluxes from large atmospheric concentration datasets and use this information to tackle a range of pressing challenges, such as: a) what is driving the current rapid growth in atmospheric methane? b) how is the terrestrial carbon sink changing with time? c) are national GHG emissions reports reliable?