Articles | Volume 15, issue 14
https://doi.org/10.5194/gmd-15-5547-2022
https://doi.org/10.5194/gmd-15-5547-2022
Development and technical paper
 | 
20 Jul 2022
Development and technical paper |  | 20 Jul 2022

Computationally efficient methods for large-scale atmospheric inverse modeling

Taewon Cho, Julianne Chung, Scot M. Miller, and Arvind K. Saibaba

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Julianne Chung on behalf of the Authors (02 Jun 2022)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (06 Jun 2022) by Dan Lu
RR by Anonymous Referee #1 (20 Jun 2022)
RR by Anonymous Referee #2 (24 Jun 2022)
ED: Publish as is (24 Jun 2022) by Dan Lu
AR by Julianne Chung on behalf of the Authors (30 Jun 2022)  Manuscript 
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Short summary
Atmospheric inverse modeling describes the process of estimating greenhouse gas fluxes or air pollution emissions at the Earth's surface using observations of these gases collected in the atmosphere. The launch of new satellites, the expansion of surface observation networks, and a desire for more detailed maps of surface fluxes have yielded numerous computational and statistical challenges. This article describes computationally efficient methods for large-scale atmospheric inverse modeling.