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

Environmental Multiscale (GEM) model ( Côté et al. 1998 ), which is very similar to the model used operationally for global deterministic forecasts at the Canadian Meteorological Centre (CMC) between 28 May 2008 and 22 June 2009 ( Bélair et al. 2009 ). Consequently, this intercomparison uses configurations of the forecast model and a network of assimilated observations that make the results entirely relevant for operational NWP. In the next section, a brief description of the configurations of the

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Mark Buehner and Ahmed Mahidjiba

1. Introduction As is common at many NWP centers, both deterministic and ensemble forecasts are produced operationally by the Meteorological Service of Canada (MSC). The systems used to produce the initial conditions for these two forecast systems operate almost completely independently. Since 2005, the global deterministic analysis is produced using a four-dimensional variational data assimilation (4D-Var) system ( Gauthier et al. 2007 ). An ensemble Kalman filter (EnKF) approach ( Houtekamer

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Takemasa Miyoshi, Yoshiaki Sato, and Takashi Kadowaki

described first, followed by detailed descriptions of the LETKF system including the error covariance localization and inflation, parallel algorithm, and adaptive bias correction. a. Quasi-operational experimental system Miyoshi and Sato (2007) developed a quasi-operational experimental system of the LETKF with the JMA global model, based on the operational 4D-Var system with incremental formulation ( JMA 2007 , as of March 2007). The forecast–analysis cycle is constructed as shown in Fig. 1 . A 9-h

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Chiara Piccolo

1. Introduction Estimates of forecast error covariances are at the heart of any data assimilation system and yet the way they are modeled in any operational assimilation scheme is limited by the compromises made for practical implementation and the available knowledge of the statistical properties of the forecast error. In most operational assimilation schemes the forecast error covariance is assumed stationary, homogeneous, and isotropic to overcome the difficulty of estimating the full

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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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Craig H. Bishop and Daniel Hodyss

because at the authors’ research laboratory, a major effort has been underway for the last 9 yr to create the world’s first operational weak constraint 4D global variational data assimilation system called the Naval Research Laboratory (NRL) Atmospheric Variational Data Assimilation System–Accelerated Representer (NAVDAS-AR; Xu et al. 2005 ). NAVDAS-AR became the operational data assimilation scheme for global model atmospheric forecasting in September of 2009. NAVDAS-AR is the only observation space

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

and short-term forecasts in the Southern Hemisphere extratropics near the surface. We use this improved set of analyses as the baseline for the evaluation of the results obtained with the augmented observational dataset. Despite the aforementioned coding error, the former version of the LETKF provided analyses and short-term forecasts that in the SH, on average, were more accurate at the 99% significance level than those obtained with the then-operational SSI of NCEP at the same T62L28 resolution

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Zhiyong Meng and Fuqing Zhang

-model Observing System Simulation Experiments (OSSEs) to more real-data, real-time, quasi-operational applications ( Tong and Xue 2005 ; Barker 2005 ; Zhang et al. 2006 ; Chen and Snyder 2007 ; Meng and Zhang 2007 ; Fujita et al. 2007 , 2008 ; Hacker et al. 2007 ; Meng and Zhang 2008a , b ; Torn and Hakim 2008a , 2009a ; Zhang et al. 2009a ; Aksoy et al. 2009 , 2010 ). The first pseudo-operational regional-scale EnKF system, based on the Weather Research and Forecasting model (WRF), was

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Thomas M. Hamill and Jeffrey S. Whitaker

characteristics of ensemble predictions initialized from EnKFs with real observations. Of particular concern is ensuring that the spread (the standard deviation of ensemble perturbations about the mean) of ensemble forecast perturbations are consistent with the ensemble-mean forecast error; commonly, spread growth is smaller than error growth. The spread growth in forecasts from operational EnKFs is likely to be affected in part by the choice of methods for dealing with the model uncertainty during the

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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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