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Jean-François Caron and Luc Fillion

this new technique in an assimilation system based on lagged forecast error samples, a careful examination should be made to ensure that the mean changes observed here in the temperature stratification are systematic in the day to day background errors. 2) Temperature observation We repeat the single observation experiments this time using a temperature observation. Figure 11 presents the mass and wind analysis increments resulting from the assimilation of the temperature observation when placed

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Takemasa Miyoshi, Yoshiaki Sato, and Takashi Kadowaki

perturbations provide better ensemble prediction than BV or SV. This study addresses only the former aspect by verifying deterministic forecast skills. The EnKF methods that do not use perturbed observations are categorized as ensemble square root filters (EnSRF; Anderson 2001 ; Whitaker and Hamill 2002 ; Tippett et al. 2003 ). The Japanese Meteorological Agency (JMA) started the development of the local ensemble transform Kalman filter (LETKF; Hunt et al. 2007 ), a kind of EnSRF, in the summer of 2005

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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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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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Marc Bocquet, Carlos A. Pires, and Lin Wu

ensemble members generated with different versions of the underlying forecast model (e.g., with different physical parameterizations; Meng and Zhang 2007 ; Fujita et al. 2007 ; Houtekamer et al. 2009 ; or with perturbations of model parameters; Wu et al. 2008 ). Another idea is to bridge the gap between variational and sequential approaches, and to improve the EnKF performance ( Kalnay et al. 2007 ) using ideas and techniques developed for 4D-Var. Such attempts are for example: the inner–outer loop

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Craig H. Bishop, Daniel Hodyss, Peter Steinle, Holly Sims, Adam M. Clayton, Andrew C. Lorenc, Dale M. Barker, and Mark Buehner

derived pertains to Buehner et al’s (2010a , b) ensemble-4D-VAR in which the 4D ensemble covariances are nonadaptively localized in space alone, with no localization in time or modulation of covariances between variable types. The fast algorithm draws heavily from techniques that have been developed for nonensemble variational DA. Variational DA algorithms perform a global minimization of a penalty function containing a background or prior term written in terms of a background error covariance

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Shu-Chih Yang, Eugenia Kalnay, and Brian Hunt

already assimilated the observation at a later time t n . With linear dynamics, the forecast from the smoothed analysis at t n −1 coincides with the KF analysis at t n ( Yang et al. 2009a ). Therefore, the KF–RIP procedure with linear dynamics can be reduced to repeatedly assimilating observations N times at t n without involving the smoothed analysis at t n −1 . With RIP, the analysis at the i th iteration is Here, the tilde is used to indicate that the observation has been used more than

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Alberto Carrassi and Stéphane Vannitsem

. , 123 , 1128 – 1145 . Derber , J. , 1989 : A variational continuous assimilation technique. Mon. Wea. Rev. , 117 , 2437 – 2446 . Evensen , G. , 1994 : Sequential data assimilation with a nonlinear quasigeostrophic model using Monte-Carlo methods to forecast error statistics

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Monika Krysta, Eric Blayo, Emmanuel Cosme, and Jacques Verron

finding the minimum takes the form of an ill-posed inverse problem requiring an additional regularization term. Some external knowledge on the system is then introduced, namely an initial guess (or background) x b (known as x f in the sequential approach), and the corresponding background error covariance matrix whose sequential counterpart is the forecast error covariance matrix . NWP favors purely statistical approaches to specify , as can be found for example in Parrish and Derber (1992

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