Articles | Volume 12, issue 7
Geosci. Model Dev., 12, 2899–2914, 2019
https://doi.org/10.5194/gmd-12-2899-2019
Geosci. Model Dev., 12, 2899–2914, 2019
https://doi.org/10.5194/gmd-12-2899-2019

Development and technical paper 12 Jul 2019

Development and technical paper | 12 Jul 2019

Estimating surface carbon fluxes based on a local ensemble transform Kalman filter with a short assimilation window and a long observation window: an observing system simulation experiment test in GEOS-Chem 10.1

Yun Liu et al.

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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 Yun Liu on behalf of the Authors (15 Oct 2018)  Author's response
ED: Referee Nomination & Report Request started (19 Nov 2018) by Adrian Sandu
RR by Anonymous Referee #2 (26 Dec 2018)
ED: Reconsider after major revisions (06 Feb 2019) by Adrian Sandu
AR by Anna Mirena Feist-Polner on behalf of the Authors (22 Mar 2019)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (11 Apr 2019) by Adrian Sandu
RR by Anonymous Referee #1 (18 Apr 2019)
RR by Anonymous Referee #3 (05 May 2019)
ED: Publish as is (16 May 2019) by Adrian Sandu
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Short summary
We developed a new carbon data assimilation system to estimate the surface carbon fluxes using the LETKF and GEOS-Chem model, which uses a new scheme with a short assimilation window and a long observation window. The analysis is more accurate using the short assimilation window and is exposed to the future observations that accelerate the spin-up. In OSSE, the system reduces the analysis error significantly, suggesting that this method could be used for other data assimilation problems.