Articles | Volume 14, issue 3
Geosci. Model Dev., 14, 1237–1252, 2021
https://doi.org/10.5194/gmd-14-1237-2021
Geosci. Model Dev., 14, 1237–1252, 2021
https://doi.org/10.5194/gmd-14-1237-2021

Development and technical paper 08 Mar 2021

Development and technical paper | 08 Mar 2021

On the model uncertainties in Bayesian source reconstruction using an ensemble of weather predictions, the emission inverse modelling system FREAR v1.0, and the Lagrangian transport and dispersion model Flexpart v9.0.2

Pieter De Meutter 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 Pieter De Meutter on behalf of the Authors (01 Dec 2020)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (10 Dec 2020) by Slimane Bekki
RR by P. Armand (19 Dec 2020)
RR by Anonymous Referee #1 (22 Dec 2020)
RR by Anonymous Referee #3 (25 Dec 2020)
ED: Publish subject to minor revisions (review by editor) (28 Dec 2020) by Slimane Bekki
AR by Pieter De Meutter on behalf of the Authors (06 Jan 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (10 Jan 2021) by Slimane Bekki
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Short summary
Inverse atmospheric transport modelling is an important tool in several disciplines. However, the specification of atmospheric transport model error remains challenging. In this paper, we employ a state-of-the-art ensemble technique combined with a state-of-the-art Bayesian inference algorithm to infer point sources. Our research helps to fill the gap in our understanding of model error in the context of inverse atmospheric transport modelling.