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Robert J. Joyce and Pingping Xie

objective of this work is to develop a prototype model of the Kalman filter (KF)-based CMORPH that is capable of producing high-resolution global precipitation analysis with improved accuracy through the incorporation of additional IR-based information and through the integration of all PMW- and IR-based information available using more precise weights. Section 2 of this paper describes the current CMORPH algorithm and individual datasets used as inputs to the merging process, sections 3 and 4

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Lars Nerger, Tijana Janjić, Jens Schröter, and Wolfgang Hiller

1. Introduction The original ensemble Kalman filter (EnKF; Evensen 1994 ) has been developed with the aim to enable the application of sequential data assimilation algorithms based on the Kalman filter with large-scale numerical models. Burgers et al. (1998) and Houtekamer and Mitchell (1998) clarified that the EnKF requires an ensemble of perturbed observations for statistical consistency. The EnKF represents the state estimate by the mean of an ensemble of model state realizations, while

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Jing Lei, Peter Bickel, and Chris Snyder

1. Introduction The ensemble Kalman filter (EnKF; Evensen 1994 , 2003 , 2007 ) has become a popular tool for data assimilation because of its computational efficiency and flexibility ( Anderson 2001 ; Whitaker and Hamill 2002 ; Ott et al. 2004 ; Bengtsson et al. 2003 ; Evensen 2007 ). In various versions of EnKFs, one major difference is how to get the updated ensemble after obtaining the updated mean and variance. Stochastic methods ( Houtekamer and Mitchell 1998

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Lars Nerger

1. Introduction Ensemble square root Kalman filters are an efficient deterministic variant of the original ensemble Kalman filter (EnKF; Evensen 1994 ; Burgers et al. 1998 ). Common members of this class of filters are the ensemble transform Kalman filter (ETKF; Bishop et al. 2001 ), the ensemble adjustment Kalman filter (EAKF; Anderson 2001 , 2003 ), and the ensemble square root Kalman filter with serial processing of observations (EnSRF; Whitaker and Hamill 2002 ). Recently, also the

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Julius H. Sumihar, Martin Verlaan, and Arnold W. Heemink

1. Introduction One of the main goals of data assimilation is to improve the forecast performance of a dynamical model. Many data assimilation algorithms are based on Kalman filtering, where the state of a system is estimated by combining all the information that is available about the system in accord with their statistical uncertainty. The main computational issue in Kalman filter–based data assimilation is the propagation of the forecast error covariance matrix. A number of methods have been

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Jonathan R. Stroud and Thomas Bengtsson

1. Introduction Motivated by the work of Evensen (1994) and Burgers et al. (1998) , various ensemble Kalman filter (EnKF) approaches have been proposed for Monte Carlo–based data assimilation in high-dimensional atmospheric systems. Examples include serial assimilation algorithms ( Houtekamer and Mitchell 2001 ; Anderson 2001 ), covariance stabilization techniques ( Hamill et al. 2001 ; Anderson 2007 ), and square root filters ( Bishop et al. 2001 ; Tippett et al. 2003 ). These EnKF

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Bo Huang, Xuguang Wang, and Craig H. Bishop

1. Introduction The ensemble Kalman filter (EnKF; Evensen 1994 ) has been widely used in the atmospheric applications, since it was introduced as a Monte Carlo realization of the traditional Kalman filter ( Kalman and Bucy 1961 ). In the EnKF, the background error covariances are estimated and evolved by cycling an ensemble of short-range forecasts and analyses. Compared to the three-dimensional variational (3DVar) method generally employing the static background error covariances, the EnKF

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Brian J. Etherton

the “preemptive” forecast, a model forecast made by using observations to update model forecasts, but without having to assimilate observations and integrate the forecast model. The following five subsections of this introduction will step through the tools used in making a preemptive forecast: (a) the ensemble Kalman filter, (b) the use of the ensemble Kalman filter in targeting and data assimilation, as well as the issues associated with (c) rank deficiency and (d) model error, and finally (e) a

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Yongsheng Chen and Chris Snyder

applied variational methods to analyze a displacement field and align coherent structures. More recently, Lawson and Hansen (2005) proposed a new error model that includes a non-Gaussian alignment error and a Gaussian additive error, and they corrected two types of errors separately with an ensemble Kalman filter (EnKF). These existing analysis techniques are meant to “move” the vortex a distance that is significant compared to the horizontal scale of the vortex. When the error of the forecast

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Xiaodong Luo and Ibrahim Hoteit

1. Introduction The Kalman filter (KF) ( Kalman 1960 ) is a sequential data assimilation algorithm. For linear stochastic systems, it can be shown that the KF is an optimal linear estimator that minimizes the variance of the estimation error ( Simon 2006 , chapter 5). Because of its relative simplicity in implementation, the KF is suitable for many data assimilation problems. However, for high-dimensional systems such as weather forecasting models, direct application of the KF is prohibitively

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