Articles | Volume 16, issue 17
https://doi.org/10.5194/gmd-16-5219-2023
https://doi.org/10.5194/gmd-16-5219-2023
Methods for assessment of models
 | 
08 Sep 2023
Methods for assessment of models |  | 08 Sep 2023

Metrics for evaluating the quality in linear atmospheric inverse problems: a case study of a trace gas inversion

Vineet Yadav, Subhomoy Ghosh, and Charles E. Miller

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2022-89', Peter Rayner, 03 Jul 2022
  • RC2: 'Comment on gmd-2022-89', Anonymous Referee #2, 15 Jul 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Vineet Yadav on behalf of the Authors (07 Sep 2022)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (02 Nov 2022) by Leena Järvi
RR by Anonymous Referee #3 (29 Nov 2022)
RR by Anonymous Referee #4 (03 Dec 2022)
ED: Publish subject to minor revisions (review by editor) (21 Dec 2022) by Leena Järvi
AR by Subhomoy Ghosh on behalf of the Authors (16 Mar 2023)  Author's response   Author's tracked changes 
EF by Lorena Grabowski (17 Mar 2023)  Supplement 
EF by Lorena Grabowski (20 Mar 2023)  Manuscript 
ED: Publish as is (15 May 2023) by Leena Järvi
AR by Subhomoy Ghosh on behalf of the Authors (01 Jun 2023)  Manuscript 
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Short summary
Measuring the performance of inversions in linear Bayesian problems is crucial in real-life applications. In this work, we provide analytical forms of the local and global sensitivities of the estimated fluxes with respect to various inputs. We provide methods to uniquely map the observational signal to spatiotemporal domains. Utilizing this, we also show techniques to assess correlations between the Jacobians that naturally translate to nonstationary covariance matrix components.