Coupling ensemble Kalman filter with four-dimensional variational data assimilation

Zhang, Fuqing; Zhang, Meng; Hansen, James A.

doi:10.1007/s00376-009-0001-8

Cited by 102 publications

(83 citation statements)

References 29 publications

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“…Recently, several researchers have proposed a variety of hybrid methods (Lorenc, 2003;Hunt et al, 2004;Liu et al, 2008Liu et al, , 2009Zhang et al, 2009;Wang et al, 2010). The main thrust behind the hybrid methods is that the ensemble-based background error covariance statistics are provided to the variational data assimilation.…”

Section: Introductionmentioning

confidence: 99%

The structure of background-error covariance in a four-dimensional variational data assimilation system: Single-point experiment

Liu

Wang

2010

Adv. Atmos. Sci.

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A four dimensional variational data assimilation (4DVar) based on a dimension-reduced projection (DRP4DVar) has been developed as a hybrid of the 4DVar and Ensemble Kalman filter (EnKF) concepts. Its good flow-dependent features are demonstrated in single-point experiments through comparisons with adjointbased 4DVar and three-dimensional variational data (3DVar) assimilations using the fifth-generation Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model (MM5). The results reveal that DRP-4DVar can reasonably generate a background error covariance matrix (simply B-matrix) during the assimilation window from an initial estimation using a number of initial condition dependent historical forecast samples. In contrast, flow-dependence in the B-matrix of MM5 4DVar is barely detectable. It is argued that use of diagonal estimation in the B-matrix of the MM5 4DVar method at the initial time leads to this failure. The experiments also show that the increments produced by DRP-4DVar are anisotropic and no longer symmetric with respect to observation location due to the effects of the weather trends captured in its B-matrix. This differs from the MM5 3DVar which does not consider the influence of heterogeneous forcing on the correlation structure of the B-matrix, a condition that is realistic for many situations. Thus, the MM5 3DVar assimilation could only present an isotropic and homogeneous structure in its increments.Key words: DRP-4DVar, data assimilation, flow dependence, single-point experiment Citation: Liu, J. J., B. Wang, and S. D. Wang, 2010: The structure of background-error covariance in a four-dimensional variational data assimilation system: single-point experiment.

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Section: Introductionmentioning

confidence: 99%

The structure of background-error covariance in a four-dimensional variational data assimilation system: Single-point experiment

Liu

Wang

2010

Adv. Atmos. Sci.

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“…Research on coupling 4DVar with EnKF has been conducted to fully utilize their respective advantages [18][19][20][21]. Recently, a hybrid method known as the proper orthogonal decomposition (POD)-based ensemble 4D variational data assimilation method (PODEn4DVar), was proposed by Tian et al [21].…”

Section: Introductionmentioning

confidence: 99%

Assimilation of Remotely-Sensed Leaf Area Index into a Dynamic Vegetation Model for Gross Primary Productivity Estimation

Zhang

Tian

et al. 2017

Remote Sensing

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Quantitative estimation of the magnitude and variability of gross primary productivity (GPP) is required to study the carbon cycle of the terrestrial ecosystem. Using ecosystem models and remotely-sensed data is a practical method for accurately estimating GPP. This study presents a method for assimilating high-quality leaf area index (LAI) products retrieved from satellite data into a process-oriented Lund-Potsdam-Jena dynamic global vegetation model (LPJ-DGVM) to acquire accurate GPP. The assimilation methods, including the Ensemble Kalman Filter (EnKF) and a proper orthogonal decomposition (POD)-based ensemble four-dimensional (4D) variational assimilation method (PODEn4DVar), incorporate information provided by observations into the model to achieve a better agreement between the model-estimated and observed GPP. The LPJ-POD scheme performs better with a correlation coefficient of r = 0.923 and RMSD of 32.676 gC/m 2 /month compared with the LPJ-EnKF scheme (r = 0.887, RMSD = 38.531 gC/m 2 /month) and with no data assimilation (r = 0.840, RMSD = 45.410 gC/m 2 /month). Applying the PODEn4DVar method into LPJ-DGVM for simulating GPP in China shows that the annual amount of GPP in China varied between 5.92 PgC and 6.67 PgC during 2003-2012 with an annual mean of 6.35 PgC/yr. This study demonstrates that integrating remotely-sensed data with dynamic global vegetation models through data assimilation methods has potential in optimizing the simulation and that the LPJ-POD scheme shows better performance in improving GPP estimates, which can provide a favorable way for accurately estimating dynamics of ecosystems.

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“…It is now standard and became operational (Wang, 2010). Zhang et al (2009) use a two-way connection between EnKF and 4DVAR to obtain the covariance for 4DVAR, and 4DVAR to feed the mean analysis into EnKF. EnKF is operational at the National Centers for Environmental Prediction (NCEP) as part of its Global Forecast System Hybrid Variational Ensemble Data Assimilation System (GDAS), together with the Gridpoint Statistical Interpolation (GSI) variational data assimilation system (Developmental Testbed Center, 2015).…”

Section: Introductionmentioning

confidence: 99%

Hybrid Levenberg–Marquardt and weak-constraint ensemble Kalman smoother method

Mandel

Bergou

Gürol

et al. 2016

Nonlin. Processes Geophys.

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Abstract. The ensemble Kalman smoother (EnKS) is used as a linear least-squares solver in the Gauss-Newton method for the large nonlinear least-squares system in incremental 4DVAR. The ensemble approach is naturally parallel over the ensemble members and no tangent or adjoint operators are needed. Furthermore, adding a regularization term results in replacing the Gauss-Newton method, which may diverge, by the Levenberg-Marquardt method, which is known to be convergent. The regularization is implemented efficiently as an additional observation in the EnKS. The method is illustrated on the Lorenz 63 model and a two-level quasigeostrophic model.

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Coupling ensemble Kalman filter with four-dimensional variational data assimilation

Cited by 102 publications

References 29 publications

The structure of background-error covariance in a four-dimensional variational data assimilation system: Single-point experiment

The structure of background-error covariance in a four-dimensional variational data assimilation system: Single-point experiment

Assimilation of Remotely-Sensed Leaf Area Index into a Dynamic Vegetation Model for Gross Primary Productivity Estimation

Hybrid Levenberg–Marquardt and weak-constraint ensemble Kalman smoother method

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