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Zhiyong Meng and Fuqing Zhang

-area models (LAMs), which is the focus of the current review. 1 The first LAM application of the EnKF was found in Snyder and Zhang (2003) and Zhang et al. (2004) , where synthetic radar data was assimilated into a cloud model. Those two studies demonstrated that the EnKF analysis can faithfully approximate the truth in terms of both dynamic and thermodynamic variables of a supercell storm ( Fig. 1 ). Fig . 1. The performance of a convective-scale EnKF in assimilating radar radial velocity for

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

and lateral boundary conditions at the beginning time of the assimilation window, x lbc is the lateral boundary conditions at the end time, and and are first-guess fields of x 0 and x lbc , respectively. Here represents the background covariance matrix associated with the initial (lateral) boundary conditions. Lateral boundary conditions, except at the initial and end times, are obtained by linear interpolation. Here H is the observation operator, y o comprises the observations, and

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Mark Buehner and Ahmed Mahidjiba

deterministic forecasts and also the EnKF background ensembles were used to specify flow-dependent background-error covariances within the variational data assimilation system. These alternative approaches were evaluated against the currently operational approach of using 4D-Var with a static and relatively simple estimate of the background-error covariances. Results from that study demonstrate the potential of a significant positive impact on analysis and forecast accuracy in the deterministic forecast

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Takemasa Miyoshi, Yoshiaki Sato, and Takashi Kadowaki

only to generate ensemble perturbations; the ensemble mean is provided by a separate data assimilation system. The current mainstream procedure in operational NWP is to use a variational data assimilation method to obtain an accurate analysis. Separately, bred vectors (BVs) or singular vectors (SVs) are computed to obtain initial ensemble perturbations. It is important to investigate if EnKF provides as accurate an analysis as variational methods, as well as to investigate if EnKF initial

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Marc Bocquet, Carlos A. Pires, and Lin Wu

usual Gaussian analysis of current data assimilation orthodoxy. There are reviews, or relevant reports, that focus more on the nonlinear aspects but less on modeling the non-Gaussian statistics ( Miller et al. 1994 ; Evensen 1997 ; Verlaan and Heemink 2001 ; Andersson et al. 2005 ). The intended scope of the article is broad: meteorology, oceanography, and atmospheric chemistry. Non-Gaussianity may take many forms there, and does not necessarily always come from the dynamics. However, a

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

the localization methods discussed in the previous sections with a small dynamical model, data assimilation experiments with the Lorenz-40 dynamical system of Lorenz and Emanuel (1998) were performed. This nonlinear model has been used to assess ensemble-based assimilation schemes in a number of studies (e.g., Whitaker and Hamill 2002 ; Ott et al. 2004 ; Sakov and Oke 2008 ). The model is governed by 40 coupled ordinary differential equations in a domain with cyclic boundary conditions. The

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Shu-Chih Yang, Eugenia Kalnay, and Brian Hunt

improved and stays on the correct regime, following the true trajectory very well. This suggests that the quasi outer loops have a clear advantage during unstable periods. When getting close to the boundary between regimes ( Evans et al. 2004 ), the nonlinear perturbation growth quickly degrades the forecast trajectory for the standard LETKF and for QOL before the second iteration. Particularly for the assimilation window between time = 17.25 and 17.5 (between points C and D), both forecasts quickly go

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