Bayesian inverse modelling and data assimilation of atmospheric emissions.

气体排放的指数增长是地球全球变暖的部分原因。今天，我们每年排放约500亿吨温室气体，其中大部分是由燃烧化石燃料、工业生产和土地使用变化产生的。甲烷可以在石油和天然气开采过程中释放，这通常被称为“逃逸排放”。甲烷的短寿命意味着其排放量的减少迅速导致其在大气中的浓度降低。因此，解决甲烷排放问题可能是缓解气候变化某些影响的有效和快速方法。随机性是这项研究中的一个首要问题，因为有许多随机性的来源。我的研究重点是定位人为温室气体的来源和量化排放率;重点是甲烷。为此，我正在模拟大气中的气体扩散，并对源特性进行概率反演。我预测时空气体扩散使用高斯羽流和其他模型从计算流体动力学的基础上Navier-Stokes方程和评估其计算成本和精度在不同的大气条件下。此外，我正在开发新的方法，包括基于梯度的MCMC算法和高斯过程，以执行有效的概率反演，根据气体浓度测量确定源位置。由于该问题的高维性质，MCMC反演计算昂贵。因此，开展这项研究的目的是创建计算速度快且适用的模型，包括用于实时跟踪无人机或卫星的排放。与壳牌合作。

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.