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Zhiyong Meng and Fuqing Zhang

EnKF in comparison to the variational data assimilation techniques. These advantages include the following: 1) the background error covariance is flow dependent, which reflects the error of the day; 2) the model and observation operator can be nonlinear; 3) it provides not only the best estimation of the state, but also the associated flow-dependent uncertainty; therefore, it can be seamlessly coupled with ensemble forecasting; 4) there is no need to code a tangent linear or adjoint model; 5) it is

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Loïk Berre and Gérald Desroziers

1. Introduction Usual data assimilation systems for numerical weather prediction (NWP), using Kalman filter or variational techniques, are based on a statistical combination of observations and a background, which is usually a short-term forecast. This statistical estimation requires the specification of spatial covariances of errors in these two kinds of information. As presented in Hollingsworth (1987) and Daley (1991 , p. 125), the role of background error covariances is to spatially

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