Final report on "Carbon Data Assimilation with a Coupled Ensemble Kalman Filter"

doi:10.2172/1144723

GSTDTAP > 地球科学

DOI	10.2172/1144723
报告编号	DOE-UCB-64436
来源ID	OSTI ID: 1144723
	Final report on "Carbon Data Assimilation with a Coupled Ensemble Kalman Filter"
	Kalnay, Eugenia; Kang, Ji-Sun; Fung, Inez
	2014-07-23
出版年	2014
语种	英语
国家	美国
领域	地球科学
英文摘要	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 âblurringâ 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 advances include âvariable localizationâ that reduces sampling errors in the estimation of the forecast error covariance, more advanced adaptive multiplicative and additive inflations, and vertical localization based on the time scale of the processes. The main result has been obtained using the LETKF-C with all these advances, and assimilating simulated atmospheric CO2 observations from different observing systems (surface flask observations of CO2 but no surface carbon fluxes observations, total column CO2 from GoSAT/OCO-2, and upper troposphere AIRS retrievals). After a spin-up of about one month, the LETKF-C succeeded in reconstructing the true evolving surface fluxes of carbon at a model grid resolution. When applied to the CAM3.5 model, the LETKF gave very promising results as well, although only one month is available.
英文关键词	LETKF ensemble Kalman filter CAM assimilation carbon fluxes
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来源平台	US Department of Energy (DOE)
引用统计
文献类型	科技报告
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/6540
专题	地球科学
推荐引用方式 GB/T 7714	Kalnay, Eugenia,Kang, Ji-Sun,Fung, Inez. Final report on "Carbon Data Assimilation with a Coupled Ensemble Kalman Filter",2014.