Articles | Volume 13, issue 4
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


Interactive discussion

Status: closed
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
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.