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

. Furthermore, we discuss spectral properties of the solution depending on different weighting functions of the observations. In section 2 we discuss domain localization in the context of ensemble-based Kalman filters and introduce a modification to the algorithm in order to include a Schur product with an isotropic matrix. Then, in section 3 we compare the different localization methods when applied to the Lorenz-40 system and show the beneficial effect of weighting of observations and the technique

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Thomas M. Hamill and Jeffrey S. Whitaker

1. Introduction The ensemble Kalman filter (EnKF; Evensen 1994 ; Houtekamer and Mitchell 1998 ) and its variants (e.g., Hamill and Snyder 2000 ; Anderson 2001 ; Whitaker and Hamill 2002 ; Hunt et al. 2006 ) are being explored for their use in improving the accuracy of initial conditions and for initializing ensemble weather predictions. The EnKF produces an ensemble of parallel short-term forecasts and analyses; background-error covariances from the ensemble are used in the

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

1. Introduction Heavy rainfalls are extreme meteorological phenomena and often cause disasters with loss of human life. Recent progress in numerical modeling and assimilation techniques has made it possible to predict to some extent the occurrence of heavy rainfalls induced by orographic or synoptic forcing. However, predicting small-scale convective rainfalls with weak forcing is still a numerical weather prediction (NWP) challenge. In Japan, such local heavy rainfalls are sometimes called

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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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Shu-Chih Yang, Eugenia Kalnay, and Brian Hunt

formulation of EnKF benefits from ad hoc modifications such as covariance inflation, EnKF–RIP benefits from modifications such as adaptive estimation of the number of iterations per analysis cycle and the addition of small perturbations. The RIP scheme was originally proposed to accelerate the spinup of the ensemble-based Kalman filter. This spinup is especially long in the absence of prior information (e.g., during a cold start) or when the background error statistics suddenly change. With RIP, the

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Takemasa Miyoshi, Yoshiaki Sato, and Takashi Kadowaki

1. Introduction The ensemble Kalman filter (EnKF), first proposed by Evensen (1994) , is now a feasible choice for use with operational numerical weather prediction (NWP). The Canadian Meteorological Centre (CMC) started to use an EnKF method with perturbed observations as an operational ensemble prediction system (EPS) in January 2005 ( Houtekamer and Mitchell 1998 , 2001 , 2005 ; Houtekamer et al. 2005 ). In the summer of 2005, the Met Office started to use the ensemble transform Kalman

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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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Jean-François Caron and Luc Fillion

1. Introduction For numerical weather prediction (NWP) forecasts at mesoscale and very-short-range time scales (e.g., nowcasting), the forecast of precipitation is of major interest but also poses the greatest challenge. A large part of the quality of the forecast relies on the quality of the initial conditions (the so-called analysis). The mesoscale analysis must contain the necessary information to allow the NWP model to start with precipitation areas at the right location and to correctly

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