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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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AMT | Articles | Volume 12, issue 5
Atmos. Meas. Tech., 12, 2611-2629, 2019
https://doi.org/10.5194/amt-12-2611-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue: The 10th International Carbon Dioxide Conference (ICDC10)...

Atmos. Meas. Tech., 12, 2611-2629, 2019
https://doi.org/10.5194/amt-12-2611-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 07 May 2019

Research article | 07 May 2019

A segmentation algorithm for characterizing rise and fall segments in seasonal cycles: an application to XCO2 to estimate benchmarks and assess model bias

Leonardo Calle 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 Leonardo Calle on behalf of the Authors (05 Feb 2019)  Author's response    Manuscript
ED: Publish subject to minor revisions (review by editor) (22 Feb 2019) by Brigitte Buchmann
AR by Leonardo Calle on behalf of the Authors (03 Mar 2019)  Author's response    Manuscript
ED: Publish as is (23 Mar 2019) by Brigitte Buchmann
AR by Leonardo Calle on behalf of the Authors (02 Apr 2019)  Author's response    Manuscript
Publications Copernicus
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Short summary
Satellite observations of atmospheric carbon dioxide offer extraordinary insights into terrestrial ecosystem activity on Earth. The algorithm we present provides researchers with a great deal more information from these satellite data than has been available in the past. We hope the application of this algorithm and analyses tools provides insight into atmospheric dynamics of carbon dioxide and helps inform the development of global ecosystem models in the future.
Satellite observations of atmospheric carbon dioxide offer extraordinary insights into...
Citation