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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/gmd-2017-269
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2017-269
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

Development and technical paper 13 Dec 2017

Development and technical paper | 13 Dec 2017

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This discussion paper is a preprint. A revision of the manuscript is under review for the journal Geoscientific Model Development (GMD).

Estimating Surface Carbon Fluxes Based on a Local Ensemble Transform Kalman Filter with a Short Assimilation Window and a Long Observation Window

Yun Liu1, Eugenia Kalnay1, Ning Zeng1, Ghassem Asrar2, Zhaohui Chen3, and Binghao Jia4 Yun Liu et al.
  • 1Dept. of Atmospheric and Oceanic Science, University of Maryland – College Park, USA
  • 2Joint Global Change Research Institute/PNNL, College Park, MD, USA
  • 3School of Environmental Science, University of East Anglia, Norwich, UK
  • 4State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

Abstract. We developed a Carbon data assimilation system to estimate the surface carbon fluxes using the Local Ensemble Transform Kalman Filter and atmospheric transfer model of GEOS-Chem driven by the MERRA-1 reanalysis of the meteorological fields based on the Goddard Earth Observing System Model, Version 5 (GEOS-5). This assimilation system is inspired by the method of Kang et al. (2011, 2012), who estimated the surface carbon fluxes in an Observing System Simulation Experiment (OSSE) mode, as evolving parameters in the assimilation of the atmospheric CO2, using a short assimilation window of 6hours. They included the assimilation of the standard meteorological variables, so that the ensemble provided a measure of the uncertainty in the CO2 transport. After introducing new techniques such as “variable localization”, and increased observation weights near the surface, they obtained accurate carbon fluxes at grid point resolution. We developed a new version of the LETKF related to the “Running-in-Place” (RIP) method used to accelerate the spin-up of EnKF data assimilation (Kalnay and Yang, 2010; Wang et al., 2013, Yang et al., 2014). Like RIP, the new assimilation system uses the “no-cost smoothing” algorithm for the LETKF (Kalnay et al., 2007b), which allows shifting at no cost the Kalman Filter solution forward or backward within an assimilation window. In the new scheme a long “observation window” (e.g., 7-days or longer) is used to create an LETKF ensemble at 7-days. Then, the RIP smoother is used to obtain an accurate final analysis at 1-day. This analysis has the advantage of being based on a short assimilation window, which makes it more accurate, and of having been exposed to the future 7-days observations, which accelerates the spin up. The assimilation and observation windows are then shifted forward by one day, and the process is repeated. This reduces significantly the analysis error, suggesting that this method could be used in other data assimilation problems. The newly developed assimilation method can be used with other Earth system models, especially for greater use of observations in conjunction with models.

Yun Liu et al.
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Yun Liu et al.
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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 with the short assimilation window and is exposed to the future observations accelerating the spin up. In OSSE, the system reduces significantly the analysis error, suggesting that this method could be used in other data assimilation problems.
We developed a new Carbon data assimilation system to estimate the surface carbon fluxes using...
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