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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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A. J. Alkezweeny

796 JOURNAL OF APPLIED METEOROLOGY Vo~.u~n. 9The Millipore Filter Technique for Ice Nuclei Measurement A. J. AL~zw~Meteorology Research, Inc., Altadena, Calif.(Manuscript received 27 January 1970, in revised form 6 May 1970) An investigation into the technique for measuring ice nuclei by the Millipore filter method is presented.It was found that the measured concentration is independent of the

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Ian J. Barton

regional model of ocean dynamics appears to be the best method of deriving accurate ocean currents at a regional scale (10–200 km). Techniques for filtering MCC currents will be a vital cog in this combination of remote-sensing technologies. Accurate measurement and prediction of surface currents are required for search and rescue, pollution monitoring, and management of marine industries. 2. The maximum cross-correlation technique Scanning satellite instruments can provide an image of the sea surface

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Chi-Fan Shih and Takeshi Ohtake

412 ~OURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY VOLUME2An Improved Filter Technique for Ice Nucleus Measurements CHI-FAN SHIH* AND TAKESHI OHTAKEGeophysical Institute, University of Alaska, Fairbanks, AK 9970112 July 1984 and 13 March 1985 No satisfactory standard field method has been established for the measurement of ice nucleus concentrations,although the filter technique is a promising candidate if the tendency for ice nucleus

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P. L. Houtekamer and Herschel L. Mitchell

of the most serious approximations in current NWP practice is that the statistics used for assimilating data are largely homogeneous, isotropic, and independent of the flow. This shortcoming, essentially what distinguishes 3D from 4D data assimilation, has led to an intense effort to develop 4D data assimilation techniques over the past decade. Attention has focused on two techniques: the Kalman filter (e.g., Cohn and Parrish 1991 ) and the 4D variational algorithm (e.g., Courtier et al. 1994

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Cuong M. Nguyen, Dmitri N. Moisseev, and V. Chandrasekar

. Recent developments in radar signal processors allowed for improvement of clutter suppression methods by using spectral clutter suppression techniques ( Siggia and Passarelli 2004 ). To compensate for the effect of notching it was proposed to use Gaussian Model Adaptive Processing (GMAP; Siggia and Passarelli 2004 ), which interpolates over the notched spectral lines. The limitation of spectral filtering techniques is the effect of spectral leakage, caused by finite sample length, on the spectral

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Naila F. Raboudi, Boujemaa Ait-El-Fquih, Clint Dawson, and Ibrahim Hoteit

to update each grid point using only observations falling within a preset influence radius ( Sakov and Bertino 2011 ; Houtekamer and Mitchell 1998 ). In our experiments, the localization support radii vary between 25 and 1000 km. The filters were also equipped with the covariance inflation technique, which is commonly used to increase the spread of the forecast or analysis ensembles. Luo and Hoteit (2011) followed a robust filtering strategy and interpret it as an EnKF equipped with different

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J. Hubbert and V. N. Bringi

JUNE I995 NOTES AND CORRESPONDENCE 643An Iterative Filtering Technique for the Analysis of Copolar Differential Phase and Dual-Frequency Radar Measurements J. HUBBERT AND V. N. BRINGIDepartment of Electrical Engineering, Colorado State University, Fort Collins, Colorado20 June 1994 and 18 November 1994ABSTRACT Copolar

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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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J. B. Klemp, W. C. Skamarock, and J. Dudhia

of filtered equations. In this paper we focus on split-explicit integration techniques for the compressible nonhydrostatic equations, and propose new procedures that are suitable for integrating these equations when cast in conservative (flux) form. In semi-implicit schemes, the terms responsible for sound-wave propagation are averaged in time, producing an implicit set of equations for variables at the new time level that requires the solution of a three-dimensional Helmholz equation for

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