Estimation of trace gas fluxes with objectively determined basis functions using reversible-jump Markov chain Monte Carlo

Lunt, Mark F.; Rigby, Matt; Ganesan, Anita L.; Manning, Alistair J.

doi:https://doi.org/10.5194/gmd-9-3213-2016

Articles | Volume 9, issue 9

https://doi.org/10.5194/gmd-9-3213-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/gmd-9-3213-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 9, issue 9

Development and technical paper

|

19 Sep 2016

Development and technical paper |

| 19 Sep 2016

Estimation of trace gas fluxes with objectively determined basis functions using reversible-jump Markov chain Monte Carlo

Mark F. Lunt, Matt Rigby, Anita L. Ganesan, and Alistair J. Manning

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Final revised paper (published on 19 Sep 2016)
Supplement to the final revised paper
Preprint (discussion started on 27 Apr 2016)
Supplement to the preprint

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Review', Anonymous Referee #1, 22 Jun 2016
- AC1: 'Response to referee #1', Mark Lunt, 20 Jul 2016
RC2: 'Review of ``Estimation of trace gas fluxes with objectively determined basis functions using reversible jump Markov Chain Monte Carlo'' by Mark F. Lunt, Matt Rigby, Anita L. Ganesan, and Alistair J. Manning', Anonymous Referee #2, 22 Jun 2016
- AC2: 'Response to referee #2', Mark Lunt, 20 Jul 2016

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Mark Lunt on behalf of the Authors (20 Jul 2016) Author's response Manuscript

ED: Publish subject to technical corrections (25 Aug 2016) by Ignacio Pisso

AR by Mark Lunt on behalf of the Authors (31 Aug 2016) Manuscript

Short summary

Bayesian inversions can be used to estimate emissions of gases from atmospheric data. We present an inversion framework that objectively defines the basis functions, which describe regions of emissions. The framework allows for the uncertainty in the choice of basis functions to be propagated through to the posterior emissions distribution in a single-step process, and provides an alternative to using a single set of basis functions.