Bayesian inverse modelling and data assimilation of atmospheric emissions.

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The exponential increase of gas emissions is in part responsible for Earth's global warming. Today, we emit around 50 billion tonnes of greenhouse gases each year, with the majority produced by the burning of fossil fuels, industrial production, and land use change. Methane can be released during oil and gas extraction, this is often referred as "fugitive emissions". The short lifetime of methane implies that reductions in its emissions rapidly results in lowering its concentration in the atmosphere. Hence, tackling methane emissions could be an effective and rapid way to mitigate some of the impacts of climate change. Stochasticity is an overarching problem in this research as there are many sources of randomness.My research focuses on locating source(s) and quantifying emission rate(s) of anthropogenic greenhouse gases; with a focus on methane. To do so, I am modelling gas dispersion in the atmosphere and implementing probabilistic inversion for source characterisation. I am predicting spatio-temporal gas dispersion using Gaussian plume and other models from computational fluid dynamics based on Navier-Stokes equations and assessing their computational cost and accuracy under different atmospheric conditions. Additionally, I am developing novel methodologies involving gradient-based MCMC algorithms and Gaussian Processes to perform efficient probabilistic inversion, which identifies source(s) location based on gas concentration measurements. Due to the high-dimensional nature of the problem, MCMC inversion is computationally expensive. Hence, this research is undertaken with the aim to create models which are computationally fast and applicable, including for live tracking of emissions by drones or satellites. In partnership with Shell.