Final report on "Carbon Data Assimilation with a Coupled Ensemble Kalman Filter"
We proposed (and accomplished) the development of an Ensemble Kalman Filter (EnKF) approach for the estimation of surface carbon fluxes as if they were parameters, augmenting the model with them. Our system is quite different from previous approaches, such as carbon flux inversions, 4D-Var, and EnKF with approximate background error covariance (Peters et al., 2008). We showed (using observing system simulation experiments, OSSEs) that these differences lead to a more accurate estimation of the evolving surface carbon fluxes at model grid-scale resolution. The main properties of the LETKF-C are: a) The carbon cycle LETKF is coupled with the simultaneous assimilation of the standard atmospheric variables, so that the ensemble wind transport of the CO2 provides an estimation of the carbon transport uncertainty. b) The use of an assimilation window (6hr) much shorter than the months-long windows used in other methods. This avoids the inevitable “blurring” of the signal that takes place in long windows due to turbulent mixing since the CO2 does not have time to mix before the next window. In this development we introduced new, advanced techniques that have since been adopted by the EnKF community (Kang, 2009, Kang et al., 2011, Kang et al. 2012). These advancesmore »
- Publication Date:
- OSTI Identifier:
- Report Number(s):
- DOE Contract Number:
- Resource Type:
- Technical Report
- Research Org:
- The Regents of The University of California, Berkeley
- Sponsoring Org:
- USDOE; USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
- Country of Publication:
- United States
- 58 GEOSCIENCES; 97 MATHEMATICS AND COMPUTING LETKF; ensemble Kalman filter; CAM; assimilation; carbon fluxes
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