Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 12, issue 9
Articles | Volume 12, issue 9
https://doi.org/10.5194/amt-12-4659-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/amt-12-4659-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 12, issue 9
Research article
 | 
02 Sep 2019
Research article |  | 02 Sep 2019

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

Laura Cartwright, Andrew Zammit-Mangion, Sangeeta Bhatia, Ivan Schroder, Frances Phillips, Trevor Coates, Karita Negandhi, Travis Naylor, Martin Kennedy, Steve Zegelin, Nick Wokker, Nicholas M. Deutscher, and Andrew Feitz

Data sets

The 2015 Ginninderra CH4 and CO2 release experiment: Fixed and scanning sensor dataset A. Feitz, I. Schroder, F. Phillips, T. Coates, K. Negandhi, 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

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Short summary
Despite extensive research, emission detection and quantification of greenhouse gases (GHGs) remain 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.
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The 10th International Carbon Dioxide Conference (ICDC10)...