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Tijana Janjić, Lars Nerger, Alberta Albertella, Jens Schröter, and Sergey Skachko

equiangular grid of 1° × 1°. With this approach, the land areas are filled with an arbitrary function and an iterative procedure is used to smooth the field over land and the land–ocean transition. Using the same procedure, the forecast and analysis fields are extended over the entire earth’s surface. Spherical harmonic analysis up to spherical harmonic degree 180 can then be applied to obtain the harmonic spectrum of each field. In this study, we only consider the error in the mean DOT obtained by

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Zhiyong Meng and Fuqing Zhang

Oklahoma City tornadic supercell storm assimilating radar and surface network data using EnKF . Preprints, 13th Conf. on Integrated Observing and Assimilation Systems for Atmosphere, Oceans, and Land Surface (IOAS-AOLS) , Pheonix, AZ, Amer. Meteor. Soc., Paper 6.4. [Available online at http://ams.confex.com/ams/89annual/techprogram/paper_150404.htm with a link to the extended abstract at http://twister.ou.edu/papers/LeiXueYu_AMS2009.pdf .] Li , H. , E. Kalnay , T. Miyoshi , and C. M

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Steven J. Greybush, Eugenia Kalnay, Takemasa Miyoshi, Kayo Ide, and Brian R. Hunt

1. Introduction The ensemble Kalman filter (EnKF; Evensen 1994 ) is a Monte Carlo approximation to the traditional filter of Kalman (1960) that is suitable for high-dimensional problems such as numerical weather prediction (NWP). One of the strengths of ensemble Kalman filters is the ability to evolve in time estimates of forecast error covariance, using the flow-dependent information inherent in an ensemble of model runs. Localization is a technique by which the impact of

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Thomas M. Hamill and Jeffrey S. Whitaker

approximately 2.4 days. This model is obviously much simpler than the operational numerical weather prediction models currently in use; the resolution is lower, there is no terrain, no land or water, and no atmospheric moisture. In fact, while this model is capable of supporting internal gravity waves, it does not produce an external mode. These simplifications should be kept in mind while interpreting the results and their implications for operational numerical weather prediction. b

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Takemasa Miyoshi, Yoshiaki Sato, and Takashi Kadowaki

1. Introduction The ensemble Kalman filter (EnKF), first proposed by Evensen (1994) , is now a feasible choice for use with operational numerical weather prediction (NWP). The Canadian Meteorological Centre (CMC) started to use an EnKF method with perturbed observations as an operational ensemble prediction system (EPS) in January 2005 ( Houtekamer and Mitchell 1998 , 2001 , 2005 ; Houtekamer et al. 2005 ). In the summer of 2005, the Met Office started to use the ensemble transform Kalman

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

assimilate observations from mixed-surface footprints (e.g., from areas where seawater is mixed with ice), observations from channels 4 and 5 over land, and observations for which the scan angle is larger than 35°. We also reject observations for which the difference between the observed value and h ( γ ) is more than 5 times larger than both the ensemble spread (standard deviation of the ensemble) and the presumed standard error of the observations. The model used in this study is the 2004 model

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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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