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Zhiyong Meng and Fuqing Zhang

, 2009a ; Aksoy et al. 2005 , 2006a , b ; Houtekamer et al. 2005 , 2009 ; Tong and Xue 2005 ; Dirren et al. 2007 ; Meng and Zhang 2007 , 2008a , b ; Whitaker et al. 2008 ; Torn and Hakim 2008a , 2009a ; Buehner et al. 2010a , b ). There are several recent review articles on the EnKF including, Evensen (2003 , 2007) , Hamill (2006) , and Ehrendorfer (2007) . However, none of these is dedicated to EnKF applications ranging from regional to meso- and convective scales in limited

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Chiara Piccolo

is compared with the growth from stationary ensemble-based forecast error covariances generated using the Met Office Global and Regional Ensemble Prediction System (MOGREPS). These covariances are estimated by propagating model states with a full nonlinear model rather than under the assumption of linearity. MOGREPS initial conditions are generated using an ETKF, which includes model error through a variable inflation factor to compensate for the underspread ensemble and by a stochastic physics

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Thomas M. Hamill and Jeffrey S. Whitaker

growth is large). The analysis process naturally whitens the analysis-error spectrum relative to the background-error spectrum ( Daley 1991 , his Fig. 5.9; Hamill et al. 2002 , their Fig. 9), decreasing the projection onto the growing modes. In this manuscript we seek to understand some of the mechanisms for slow spread growth in ensemble Kalman filters. In particular, we examine the effects of covariance localization, additive error noise, and model error. We perform simulation experiments with

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

distance. This is not the case in the ensemble Kalman filter (EnKF) because of sampling error. Furthermore, these methods are used with weighting of the observations, or localization of the matrix, as described in section 2 ( Penduff et al. 2002 ; Hunt et al. 2007 ; Nerger and Gregg 2007 ), which has not been done with OI. In this work we investigate the impact of this weighting on the analysis and focus on explaining the effects of domain localization in ensemble-based Kalman filter algorithms

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Takuya Kawabata, Tohru Kuroda, Hiromu Seko, and Kazuo Saito

(Meso-4DVAR; Koizumi et al. 2005 ) in 2002, making it the first operational regional 4DVAR system in the world. By assimilating 1-h accumulated rainfall amounts derived from radar-reflectivity data, they improved the accuracy of the JMA operational mesoscale forecasts. However, the horizontal grid spacing of Meso-4DVAR was 20 km, and the precipitation scheme in the adjoint model adopted only large-scale condensation and convective adjustment. In 2009, Meso-4DVAR was replaced with a different 4DVAR

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Shu-Chih Yang, Eugenia Kalnay, and Brian Hunt

filter failure, despite the fact that the filter divergence can also take place in a linear system. In the Kalman filter formulas derived using the best linear unbiased estimation (BLUE), the error covariance is evolved with the tangent linear model. Evensen (1992) pointed out that unbounded error growth could be caused by the use of the tangent linear model because of the lack of nonlinear saturation effects. EnKFs are less vulnerable to this error growth than either the Kalman filter or the

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

inflation (e.g., Satterfield and Szunyogh 2011 ), retuning the variance inflation factor, ρ , for the configurations of the data assimilation system, which assimilate the AMSU-A observations, would likely lead to a further increase of the accuracy of the analyses and the ensuing forecasts. Notwithstanding the potential positive effects of retuning ρ on the accuracy of the analyses in the experiment that assimilates the AMSU-A observations, for the sake of a conservative comparison to the results of

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Marc Bocquet, Carlos A. Pires, and Lin Wu

assimilation should consider non-Gaussian effects as corrections to a Gaussian analysis-based strategy. Variational approaches (4D-Var essentially) and ensemble-based Kalman filters include different approaches to account for model nonlinearities. Sources of non-Gaussianity can be initially categorized into two families: nonlinearities in models and non-Gaussianity of priors. The latter will be emphasized here since it is the main focus of this review, but also because several recently developed

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