Articles | Volume 13, issue 4
https://doi.org/10.5194/gmd-13-2095-2020
https://doi.org/10.5194/gmd-13-2095-2020
Development and technical paper
 | 
28 Apr 2020
Development and technical paper |  | 28 Apr 2020

Bayesian spatio-temporal inference of trace gas emissions using an integrated nested Laplacian approximation and Gaussian Markov random fields

Luke M. Western, Zhe Sha, Matthew Rigby, Anita L. Ganesan, Alistair J. Manning, Kieran M. Stanley, Simon J. O'Doherty, Dickon Young, and Jonathan Rougier

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Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Luke Western on behalf of the Authors (16 Oct 2019)  Author's response   Manuscript 
ED: Referee Nomination & Report Request started (20 Nov 2019) by Ignacio Pisso
RR by Anonymous Referee #2 (02 Dec 2019)
RR by Alfredo Farjat (15 Jan 2020)
ED: Publish subject to minor revisions (review by editor) (04 Feb 2020) by Ignacio Pisso
AR by Luke Western on behalf of the Authors (13 Feb 2020)  Author's response   Manuscript 
ED: Publish as is (27 Mar 2020) by Ignacio Pisso
AR by Luke Western on behalf of the Authors (31 Mar 2020)  Manuscript 
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Short summary
Assessments of greenhouse gas emissions using atmospheric measurements and meteorological models, or top-down methods, are important to verify national inventories or produce a stand-alone estimate where no inventory exists. We present a novel top-down method to estimate emissions. This approach uses a fast method called an integrated nested Laplacian approximation to estimate how these emissions are correlated with other emissions in different locations and at different times.