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Junjie Liu, Hong Li, Eugenia Kalnay, Eric J. Kostelich, and Istvan Szunyogh

data assimilation based on the nonhumidity observations through the error covariance term between the specific humidity and the other dynamical variables. A comparison between passive q and the control run shows the impact of winds, temperature, and surface pressure observations on the quality of specific humidity analyses. The third experiment, “univariate q ,” has two parallel assimilation cycles. One is the same as the control run, which creates the updated winds, temperature, and surface

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Marc Bocquet

(e.g., an adaptive grid) is well studied. It allows speeding up the simulations and refines the computations in which the numerical error could be large (see, e.g., Saad 2003 ). An example in geophysics, and in particular air quality, can be found in Constantinescu et al. (2008) . However, in data assimilation, control variables and observations are equally important components. Because they do not share the same representation space but are nevertheless combined in the data assimilation

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Malaquias Peña, Zoltan Toth, and Mozheng Wei

test the hypothesis, two of the cited approaches (adding random perturbations and combining a static background error covariance with a flow-dependent covariance in a hybrid) will be contrasted with alternative methods proposed here to more closely control noise in the generation of ensemble forecasts. The paper is organized as follows. Section 2 presents the model used, the general data assimilation procedure, and the experimental set up. Section 3 gives a description of the procedures

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Gérald Desroziers, Loïk Berre, Vincent Chabot, and Bernard Chapnik

unperturbed operational background, the quality control of observations can lead to different rejections of observations, especially for satellite data, since this quality control is partially based on the amplitudes of the differences between the observations and the background. This is part of the nonlinearity of the forecasting/assimilation system that must be kept in order to simulate the dynamics of the background and analysis errors in a real-size system more closely. For the time being, no model

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Chris Snyder, Thomas Bengtsson, Peter Bickel, and Jeff Anderson

1. Introduction Ensemble methods for data assimilation are presently undergoing rapid development. The ensemble Kalman filter (EnKF), in various forms, has been successfully applied to a wide range of geophysical systems including atmospheric flows from global to convective scales ( Whitaker et al. 2004 ; Snyder and Zhang 2003 ), oceanography from global to basin scales ( Keppenne et al. 2005 ), and the land surface ( Reichle et al. 2002 ). Particle filters are another class of ensemble

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