Journal cover Journal topic
Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 3.248 IF 3.248
  • IF 5-year value: 3.650 IF 5-year
    3.650
  • CiteScore value: 3.37 CiteScore
    3.37
  • SNIP value: 1.253 SNIP 1.253
  • SJR value: 1.869 SJR 1.869
  • IPP value: 3.29 IPP 3.29
  • h5-index value: 47 h5-index 47
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 60 Scimago H
    index 60
Discussion papers
https://doi.org/10.5194/amt-2019-124
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-2019-124
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 23 Apr 2019

Research article | 23 Apr 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Measurement Techniques (AMT).

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

Laura Cartwright1, Andrew Zammit-Mangion1, Sangeeta Bhatia2,a, Ivan Schroder3, Frances Phillips4, Trevor Coates5, Karita Neghandhi6,b, Travis Naylor4, Martin Kennedy6, Steve Zegelin7, Nick Wokker8, Nicholas M. Deutscher4, and Andrew Feitz3 Laura Cartwright et al.
  • 1School of Mathematics and Applied Statistics, University of Wollongong, Wollongong, Australia
  • 2Centre for Research in Mathematics, Western Sydney University, Parramatta, Australia
  • 3Geoscience Australia, Canberra, Australia
  • 4Centre for Atmospheric Chemistry, School of Earth, Atmosphere and Life Sciences, University of Wollongong, Wollongong, Australia
  • 5School of Agriculture and Food, University of Melbourne, Melbourne, Australia
  • 6Department of Earth and Planetary Sciences, Macquarie University, Sydney, Australia
  • 7CSIRO Oceans and Atmosphere, Canberra, Australia
  • 8Department of Industry, Innovation and Science, Canberra, Australia
  • acurrently at: School of Public Health, Imperial College London, London, UK
  • bcurrently at: Office of Environment and Heritage, Parramatta, Australia

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 misspecifications 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 parametrisations of measurements, the meteorological data, and the atmospheric model itself when doing 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. The worst median emission-rate estimate we obtain from all the inversions is within 36 % of the true value, while the worst posterior 95 % credible interval has a limit within 11 % of the true value.

Laura Cartwright et al.
Interactive discussion
Status: open (until 20 Jun 2019)
Status: open (until 20 Jun 2019)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
[Subscribe to comment alert] Printer-friendly Version - Printer-friendly version Supplement - Supplement
Laura Cartwright et al.
Data sets

The 2015 Ginninderra CH4 and CO2 release experiment: Fixed and scanning sensor dataset A. Feitz, I. Schroder, F. Phillips, T. Coates, K. Neghandhi, S. Bhatia, T. Naylor, M. Kennedy, S. Zegelin, N. Wokker, N. M. Deutscher, L. Cartwright, and A. Zammit-Mangion https://doi.org/10.26186/5cb7f14abd710

Model code and software

Bayesian atmospheric tomography with application to data from the 2015 Ginninderra release experiment L. Cartwright https://doi.org/10.5281/zenodo.2645929

Laura Cartwright et al.
Viewed  
Total article views: 202 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
153 48 1 202 0 2
  • HTML: 153
  • PDF: 48
  • XML: 1
  • Total: 202
  • BibTeX: 0
  • EndNote: 2
Views and downloads (calculated since 23 Apr 2019)
Cumulative views and downloads (calculated since 23 Apr 2019)
Viewed (geographical distribution)  
Total article views: 115 (including HTML, PDF, and XML) Thereof 114 with geography defined and 1 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Cited  
Saved  
No saved metrics found.
Discussed  
No discussed metrics found.
Latest update: 26 May 2019
Publications Copernicus
Download
Short summary
Despite extensive research, emission detection and quantification of greenhouse gases (GHG) remains an open problem. This article presents a novel statistical framework for detecting and quantifying methane emissions, and showcases its efficacy on data collected from different instruments in the 2015 Ginninderra controlled-release experiment. The developed techniques can be used to aid GHG emission reduction schemes by, for example, detecting and quantifying leaks from carbon storage facilities.
Despite extensive research, emission detection and quantification of greenhouse gases (GHG)...
Citation