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

). Successful NWP depends upon well-balanced initial conditions to avoid the generation of spurious inertial gravity waves such as those that ruined the 1922 Richardson forecast. By balanced, we mean an atmospheric state in the slow manifold that approximately follows physical balance equations appropriate to the scale and location, such as the geostrophic relationship. In practice, there are initialization techniques for improving the balance of an analysis, such as nonlinear normal mode initialization and

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Craig H. Bishop and Daniel Hodyss

error correlation functions associated with modern numerical weather prediction (NWP) models exhibit enough variability for AECL to be significantly superior to NECL. This study begins to address this question by comparing AECL and NECL performance in experiments using the Navy Operational Global Atmospheric Prediction System (NOGAPS; Hogan and Rosmond, 1991 ). To illustrate AECL methods within the context of a global numerical weather prediction model, Bishop and Hodyss (2007 , 2009b

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Thomas M. Hamill and Jeffrey S. Whitaker

approximately 2.4 days. This model is obviously much simpler than the operational numerical weather prediction models currently in use; the resolution is lower, there is no terrain, no land or water, and no atmospheric moisture. In fact, while this model is capable of supporting internal gravity waves, it does not produce an external mode. These simplifications should be kept in mind while interpreting the results and their implications for operational numerical weather prediction. b

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Takemasa Miyoshi, Yoshiaki Sato, and Takashi Kadowaki

al. (2008) also applied the LETKF to the NCEP GFS and assimilated real observations successfully, with the exception of satellite radiances. J. Aravequia and I. Szunyogh (2009, personal communication) upgraded this system to include the assimilation of satellite radiances. As for Japanese developments, Miyoshi and Yamane (2007) reported the successful application of the LETKF to an atmospheric general circulation model (AGCM) for the Earth Simulator (AFES; Ohfuchi et al. 2004 ). Miyoshi and

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Zhiyong Meng and Fuqing Zhang

ensemble Kalman filter (EnKF; Evensen 1994 ), which estimates the background error covariance with a short-term ensemble forecast, is drawing increasing attention. Since its first application in atmospheric sciences ( Houtekamer and Mitchell 1998 ), the EnKF has been widely examined with different models at different scales (e.g., Hamill and Snyder 2000 ; Anderson 2001 ; Whitaker and Hamill 2002 ; Mitchell et al. 2002 ; Snyder and Zhang 2003 ; Zhang and Anderson 2003 ; Zhang et al. 2004 , 2006

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

1. Introduction Variational data assimilation approaches are used at many numerical weather prediction (NWP) centers for operationally assimilating meteorological observations to provide a single “best” estimate of the current atmospheric state (e.g., Parrish and Derber, 1992 ; Rabier et al. 2000 ; Gauthier et al. 2007 ; Rawlins et al. 2007 ). The resulting analysis is used to initialize deterministic forecast models to produce short- and medium-range forecasts. Observations

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Jean-François Caron and Luc Fillion

spurious fast gravity waves in the early stage of the forecast. The latter being particularly important for optimal assimilation of precipitation-related data ( Errico et al. 2007 ). To improve the representation of the divergent part of the wind over precipitation areas in variational data assimilation (Var) systems, Fillion et al. (2005) extended the idea of Fisher (2003) to use the quasigeostrophic (QG) omega equation to relate mass and divergent wind increments by introducing a coupling between

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Loïk Berre and Gérald Desroziers

representation of effects from data density variations and atmospheric spatial variabilities was noted, and positive impacts were observed on the forecast quality of the Météo-France NWP system. Deriving flow-dependent local variances from an ensemble variational assimilation was then attempted by Kucukkaraca and Fisher (2006) , with encouraging results on cases of midlatitude storms and tropical cyclones. Another possible step is to relax the application of the homogeneity assumption with respect to

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