Articles | Volume 12, issue 7
Geosci. Model Dev., 12, 2899–2914, 2019
Geosci. Model Dev., 12, 2899–2914, 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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Estimating Surface Carbon Fluxes Based on a Local Ensemble Transform Kalman Filter with a Short Assimilation Window and a Long Observation Window
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Cited articles

Anderson, J. L.: An ensemble adjustment Kalman filter for data assimilation, Mon. Weather Rev., 129, 2884–2903, 2001. 
Anderson, J. L.: A local least squares framework for ensemble filtering, Mon. Weather Rev., 131, 634–642, 2003. 
Anderson, J. L. and Anderson, S. L.: A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts, Mon. Weather Rev., 127, 2741–2758,<2741:AMCIOT>2.0.CO;2, 1999. 
Asefi-Najafabady, S., Rayner, P. J., Gurney, K. R., McRobert, A., Song, Y., Coltin, K., Huang, J., Elvidge, C., and Baugh, K.: A multiyear, global gridded fossil fuel CO2 emission data product: Evaluation and analysis of results, J. Geophys. Res.-Atmos., 119, 10213–10231,, 2014. 
Baker, D. F., Doney, S. C., and Schimel, D. S.: Variational data assimilation for atmospheric CO2, Tellus, Ser. B, 58, 359–365,, 2006. 
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.