Bayesian atmospheric tomography for detection and quantification of methane emissions: application to data from the 2015 Ginninderra release experiment

Abstract

Abstract. Detection and quantification of greenhouse-gas emissions is important for both compliance and environment conservation. However, despite several decades of active research, it remains predominantly an open problem, largely due to model errors and assumptions that appear at each stage of the inversion processing chain. In 2015, a controlled-release experiment headed by Geoscience Australia was carried out at the Ginninderra Controlled Release Facility, and a variety of instruments and methods were employed for quantifying the release rates of methane and carbon dioxide from a point source. This paper proposes a fully Bayesian approach to atmospheric tomography for inferring the methane emission rate of this point source using data collected during the experiment from both point- and path-sampling instruments. The Bayesian framework is designed to account for uncertainty in the parameterisations of measurements, the meteorological data, and the atmospheric model itself when performing inversion using Markov chain Monte Carlo (MCMC). We apply our framework to all instrument groups using measurements from two release-rate periods. We show that the inversion framework is robust to instrument type and meteorological conditions. From all the inversions we conducted across the different instrument groups and release-rate periods, our worst-case median emission rate estimate was within 36 % of the true emission rate. Further, in the worst case, the closest limit of the 95 % credible interval to the true emission rate was within 11 % of this true value.

Mobile atmospheric measurements and local-scale inverse estimation of the location and rates of brief CH<sub>4</sub> and CO<sub>2</sub> releases from point sources

“…Some of these emissions could be localized through periodical LDAR (leak detection and repair) campaigns. Such CH 4 emissions are often accompanied by CO 2 emissions, for example when considering diesel engines powering large compressors or flaring activities to reduce natural gas (NG) venting (Caulton et al, 2014). Therefore, the monitoring of CO 2 emissions whose budget can be significant and which can help detect and characterize the processes underlying the CH 4 emissions is important too.…”

Performance of an open-path near-infrared measurement system for measurements of CO<sub>2</sub> and CH<sub>4</sub> during extended field trials

“…During the campaign, a Picarro G2301 Cavity RingDown Spectrometer (CRDS) (Crosson, 2008) measuring CO 2 , CH 4 and H 2 O was deployed. The CRDS measured continuously and was co-located with the OP-NIR spectrometer from 30 November to 12 December 2018.…”

Emulation of greenhouse‐gas sensitivities using variational autoencoders

“…Flux inversion is at the core of many methods for quantifying GHG emissions (recent examples include Alexe et al, 2015; Bergamaschi et al, 2018; Cartwright et al, 2019; Cressie, 2018; Feitz et al, 2018; Ganesan et al, 2015; Houweling et al, 2017; Humphries et al, 2012; Lucas et al, 2017; Luhar et al, 2014; Zammit‐Mangion et al, 2015). It can be summarized as the process of comparing observed GHG mole fractions to those simulated using an atmospheric transport model (ATM), in order to estimate the fluxes that are contributing to the observed mole fractions.…”