Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 14, issue 1
Articles | Volume 14, issue 1
https://doi.org/10.5194/amt-14-117-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-14-117-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 14, issue 1
Research article
 | 
07 Jan 2021
Research article |  | 07 Jan 2021

XCO2 estimates from the OCO-2 measurements using a neural network approach

Leslie David, François-Marie Bréon, and Frédéric Chevallier

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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 François-Marie Bréon on behalf of the Authors (28 Aug 2020)  Author's response   Manuscript 
ED: Referee Nomination & Report Request started (01 Oct 2020) by Piet Stammes
ED: Publish subject to minor revisions (review by editor) (02 Nov 2020) by Piet Stammes
AR by François-Marie Bréon on behalf of the Authors (14 Nov 2020)  Author's response   Manuscript 
ED: Publish as is (16 Nov 2020) by Piet Stammes
AR by François-Marie Bréon on behalf of the Authors (18 Nov 2020)  Manuscript 
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Short summary
This paper shows that a neural network (NN) approach can be used to process spaceborne observations from the OCO-2 satellite and retrieve both surface pressure and atmospheric CO2 content. The accuracy evaluation indicates that the retrievals have an accuracy that is at least as good as those of the operational approach, which relies on complex algorithms and is computer intensive. The NN approach is therefore a promising alternative for the processing of CO2-monitoring missions.
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