Articles | Volume 13, issue 4
Geosci. Model Dev., 13, 2095–2107, 2020
https://doi.org/10.5194/gmd-13-2095-2020
Geosci. Model Dev., 13, 2095–2107, 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 et al.

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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
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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.