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Zhiyong Meng and Fuqing Zhang

EnKF in comparison to the variational data assimilation techniques. These advantages include the following: 1) the background error covariance is flow dependent, which reflects the error of the day; 2) the model and observation operator can be nonlinear; 3) it provides not only the best estimation of the state, but also the associated flow-dependent uncertainty; therefore, it can be seamlessly coupled with ensemble forecasting; 4) there is no need to code a tangent linear or adjoint model; 5) it is

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Tijana Janjić, Lars Nerger, Alberta Albertella, Jens Schröter, and Sergey Skachko

. Logarithm of the spectral difference between (left) analysis and the data and (right) forecast and the data depending on spherical harmonic degree. 5. Conclusions Ensemble Kalman filter methods are typically used with one of two localization techniques: domain localization or direct forecast error localization. For domain localization, the assimilation is split into local domains in which the assimilation updates are performed independently using observations within a chosen distance. Weighting of the

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Loïk Berre and Gérald Desroziers

1. Introduction Usual data assimilation systems for numerical weather prediction (NWP), using Kalman filter or variational techniques, are based on a statistical combination of observations and a background, which is usually a short-term forecast. This statistical estimation requires the specification of spatial covariances of errors in these two kinds of information. As presented in Hollingsworth (1987) and Daley (1991 , p. 125), the role of background error covariances is to spatially

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Craig H. Bishop and Daniel Hodyss

framework is as follows. Local observation volumes are inappropriate for long-time window data assimilation because errors are liable to propagate out of observation volumes. Local approaches limit the effectiveness of some variational techniques for bias correction and the estimation of forecast and observation error variances. There is a growing interest in the use of localized ensemble covariances in three-dimensional variational data assimilation (3D-VAR) and 4D-VAR schemes evident in papers by

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José A. Aravéquia, Istvan Szunyogh, Elana J. Fertig, Eugenia Kalnay, David Kuhl, and Eric J. Kostelich

al. 2008 ), evidence has emerged only recently that EnKF schemes may be viable alternatives to the variational techniques in operational numerical weather prediction (e.g., Buehner et al. 2010a , b ; Miyoshi et al. 2010 ). In the present paper, we focus on the performance of one particular EnKF scheme, the local ensemble transform Kalman filter (LETKF), for assimilating satellite radiance observations. The LETKF algorithm was developed by Ott et al. (2004) and Hunt et al. (2004 , 2007) and

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Mark Buehner and Ahmed Mahidjiba

using the same forecast model for each. As mentioned below, however, other studies have been performed to compare other ensemble data assimilation techniques (e.g., ensemble of 4D-Var analyses) to the SV approach. Several previous studies have compared the use of different strategies for computing the initial conditions for ensemble forecasts. Hamill et al. (2000) used a quasigeostrophic channel model without model error to compare SVs with a system simulation approach ( Houtekamer et al. 1996

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Mark Buehner, P. L. Houtekamer, Cecilien Charette, Herschel L. Mitchell, and Bin He

standard four-dimensional variational data assimilation (4D-Var) and EnKF approaches differ in several important respects: the use of a deterministic versus an ensemble data assimilation approach, the use of an iterative variational algorithm versus a sequential (with respect to batches of observations) solution algorithm, differences in the approach for applying spatial covariance localization, the use of static versus flow-dependent background-error covariances, and different techniques for

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Chiara Piccolo

specification is crucial for the performance of the system. The ETKF is one of the many different ensemble Kalman filter (EnKF) formulations. For a detailed description of differences between EnKF and 4DVAR approaches, we refer to Kalnay et al. (2007) and Lorenc (2003b) . The present paper is organized as follows. In section 2 we recall the various assumptions and limitations of the different methods and describe the relevant techniques to estimate the forecast error statistics. In section 3 we

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Steven J. Greybush, Eugenia Kalnay, Takemasa Miyoshi, Kayo Ide, and Brian R. Hunt

1. Introduction The ensemble Kalman filter (EnKF; Evensen 1994 ) is a Monte Carlo approximation to the traditional filter of Kalman (1960) that is suitable for high-dimensional problems such as numerical weather prediction (NWP). One of the strengths of ensemble Kalman filters is the ability to evolve in time estimates of forecast error covariance, using the flow-dependent information inherent in an ensemble of model runs. Localization is a technique by which the impact of

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Thomas M. Hamill and Jeffrey S. Whitaker

ensemble forecasts. There are now a variety of techniques for addressing model uncertainty, such as stochastically perturbed parameterization tendencies ( Buizza et al. 1999 ; Palmer et al. 2009 ), stochastic backscatter ( Shutts 2005 ; Berner et al. 2009 ), and the use of multimodel or multicenter ensembles (e.g., Bougeault et al. 2010 and references therein). While the methods for dealing with model uncertainty are certainly relevant for the growth of ensemble forecast spread, here we are

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