Carbon flux bias estimation employing Maximum Likelihood Ensemble Filter (MLEF)

Zupanski, D.; Denning, Scott; Uliasz, M.; Zupanski, Milija; Schuh, A. E.; Rayner, P. J.; Peters, Wouter; Corbin, K. D.

doi:10.1029/2006jd008371

Cited by 86 publications

(97 citation statements)

References 51 publications

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“…In a previous pseudo-data inversion using a very similar model (Zupanski et al, 2007), the errors on the observations were assumed to be 1 ppm for afternoon observations. Nevertheless, relative to the inversion techniques presented in the next section, the errors on these observations should include errors due to calibration error, mapping error, transport error, and representation error.…”

Section: Observational Datamentioning

confidence: 99%

“…While these biases could result from incorrectly modeled short term processes, such as errors in the daily development of the planetary boundary layer, or short-term processes not in the model such as seasonal fertilization and irrigation, the main purpose is to capture longer-term processes not explicitly modeled such as land use change (Robertson et al, 2000;Peterson et al, 1998), disturbances, anthropogenic fertilization effects (Oren et al, 2001), managed forestry (Tillman et al, 2000), and large scale carbon removal . This modeling is accomplished by convolving the influence functions generated from a lagrangian particle dispersion model, LPDM (Uliasz and Pielke, 1991;Uliasz, 1993Uliasz, , 1994Uliasz et al, 1996;Zupanski, 2007), with gridded Gross Primary Productivity (GPP) and total Ecosystem Respiration (ER) at each time step in SiB3-RAMS. The LPDM transport scheme reverses advection derived from RAMS at very fine time scales and parameterizes vertical turbulent diffusion according to a Gaussian process.…”

Section: Prior Flux Model and Transportmentioning

confidence: 99%

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A regional high-resolution carbon flux inversion of North America for 2004

et al. 2010

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Abstract.Resolving the discrepancies between NEE estimates based upon (1) ground studies and (2) atmospheric inversion results, demands increasingly sophisticated techniques. In this paper we present a high-resolution inversion based upon a regional meteorology model (RAMS) and an underlying biosphere (SiB3) model, both running on an identical 40 km grid over most of North America. Current operational systems like CarbonTracker as well as many previous global inversions including the Transcom suite of inversions have utilized inversion regions formed by collapsing biome-similar grid cells into larger aggregated regions. An extreme example of this might be where corrections to NEE imposed on forested regions on the east coast of the United States might be the same as that imposed on forests on the west coast of the United States while, in reality, there likely exist subtle differences in the two areas, both natural and anthropogenic. Our current inversion framework utilizes a combination of previously employed inversion techniques while allowing carbon flux corrections to be biome independent. Temporally and spatially high-resolution results utilizing biome-independent corrections provide insight into carbon dynamics in North America. In particular, we analyze hourly CO 2 mixing ratio data from a sparse network of eight towers in North America for 2004. A prior estimate of carbon fluxes due to Gross Primary Productivity (GPP) and Ecosystem Respiration (ER) is constructed from the SiB3 biosphere model on a 40 km grid. A combination of transport from Correspondence to: A. E. Schuh (aschuh@atmos.colostate.edu) the RAMS and the Parameterized Chemical Transport Model (PCTM) models is used to forge a connection between upwind biosphere fluxes and downwind observed CO 2 mixing ratio data. A Kalman filter procedure is used to estimate weekly corrections to biosphere fluxes based upon observed CO 2 . RMSE-weighted annual NEE estimates, over an ensemble of potential inversion parameter sets, show a mean estimate 0.57 Pg/yr sink in North America. We perform the inversion with two independently derived boundary inflow conditions and calculate jackknife-based statistics to test the robustness of the model results. We then compare final results to estimates obtained from the CarbonTracker inversion system and at the Southern Great Plains flux site. Results are promising, showing the ability to correct carbon fluxes from the biosphere models over annual and seasonal time scales, as well as over the different GPP and ER components. Additionally, the correlation of an estimated sink of carbon in the South Central United States with regional anomalously high precipitation in an area of managed agricultural and forest lands provides interesting hypotheses for future work.

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Section: Observational Datamentioning

confidence: 99%

Section: Prior Flux Model and Transportmentioning

confidence: 99%

A regional high-resolution carbon flux inversion of North America for 2004

et al. 2010

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“…This framework can be used for comparing information measures of different data assimilation approaches. Additionally, as demonstrated in Zupanski et al (2007), the information measures in ensemble subspace can be employed to define a flow-dependent 'distance' function for covariance localization. We evaluate this framework within an ensemble-based data assimilation method, using a single-column precipitation model and simulated observations.…”

Section: Introductionmentioning

confidence: 99%

Applications of information theory in ensemble data assimilation

Zupanski

Hou

Zhang

et al. 2007

Quart J Royal Meteoro Soc

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ABSTRACT:We apply information theory within an ensemble-based data assimilation approach and define information matrix in ensemble subspace. The information matrix in ensemble subspace employs a flow-dependent forecast error covariance and it is of relatively small dimensions (equal to the ensemble size). The information matrix in ensemble subspace can be directly linked to the information matrix typically used in non-ensemble-based data assimilation methods, such as the Kalman Filter (KF) and the three-dimensional variational (3D-Var) methods, which provides a framework for consistent comparisons of information measures between different data assimilation methods.We evaluate information measures, such as degrees of freedom for signal, within the Maximum Likelihood Ensemble Filter (MLEF) data assimilation approach and compare them with those obtained using the KF approach and the 3D-Var approach. We assimilate model-simulated observations and use the Goddard Earth Observing System Single Column Model (GEOS-5 SCM) as a dynamical forecast model.The experimental results demonstrate that the proposed framework is useful for comparing information measures obtained in different data assimilation approaches. These comparisons indicate that using a flow-dependent forecast error covariance matrix (e.g. as in the KF and the MLEF experiments) is fundamentally important for adequately describing prior knowledge about the true model state when calculating information measures of assimilated observations. We also demonstrate that data assimilation results obtained using the KF and the MLEF approach (when ensemble size is larger than 10 ensemble members) are superior to the results of the 3D-Var approach.

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“…Formal conditions for an observing network and data assimilation to provide reliable estimate of the model parameters have been studied in connection with the concept of observability (Cohn and Dee, 1988;Navon, 1997). Successful parameter estimation has been also obtained in the context of simple prototypes of nonlinear dynamics with the EnKF (Anderson, 2001;Aksoy et al, 2006;Yang and Delsole, 2009;Koyama and Watanabe, 2010) and with the maximum likelihood ensemble filter (Zupanski and Zupanski, 2006;Zupanski et al, 2007;Orescanin et al, 2009). Annan and Hargraves (2004) have proposed a method, in the framework of the EnKF, to estimate the parameters of the Lorenz (1963) model and have then successfully applied the same approach to an intermediate complexity Earth system model (Annan et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

State and parameter estimation with the extended Kalman filter: an alternative formulation of the model error dynamics

Carrassi

Vannitsem

2011

Quart J Royal Meteoro Soc

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An alternative formulation of the extended Kalman filter for state and parameter estimation is presented, referred to as Short-Time Augmented Extended Kalman Filter (ST-AEKF). In this algorithm, the evolution of the model error generated by the uncertain parameters is described using a truncated short-time Taylor expansion within the assimilation interval. This allows for a simplification of the forward propagation of the augmented error covariance matrix with respect to the classical state augmented approach. The algorithm is illustrated in the case of a scalar unstable dynamics and is then more extensively analyzed in the context of the Lorenz 36-variable model. The results demonstrate the ability of the ST-AEKF to provide accurate estimate of both the system's state and parameters with a skill comparable to that of the full state augmented approach and in some cases close to the EKF in a perfect model scenario. The performance of the filter is analyzed for different initial parametric errors and assimilation intervals, and for the estimates of one or more model parameters. The filter accuracy is sensitive to the nature of the estimated parameter but more importantly to the assimilation interval, a feature connected to the short-time approximation on which the filter formulation relies. The conditions and the context of applications of the present approach are also discussed.

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Carbon flux bias estimation employing Maximum Likelihood Ensemble Filter (MLEF)

Cited by 86 publications

References 51 publications

A regional high-resolution carbon flux inversion of North America for 2004

A regional high-resolution carbon flux inversion of North America for 2004

Applications of information theory in ensemble data assimilation

State and parameter estimation with the extended Kalman filter: an alternative formulation of the model error dynamics

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