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Herbert Riehl, William H. Haggard, and Richard W. Sanborn

VOL. 13, NO. 5 JOURNAL OF METEOROLOGY OCTOBER 1956ON THE PREDICTION OF 24-HOUR HURRICANE MOTION By Herbert Riehl, University of ChicagoCmdr. William H. Haggard, USNR, and Lt. Cmdr. Richard W. Sanborn, USN Bureau of Aeronautics Project AROWA (Manuscript received 20 February 1956)ABSTRACTA quantitative technique for the prediction of 24-hour hurricane motion from 500-millibar charts ispresented. Verification from current forecasts made during

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Richard H. Jones

658 JOURNAL OF THE ATMOSPHERIC SCIENCES VoLu~te22Optimal Estimation of Initial Conditions for Numerical Prediction~ RtCaA~ H. Jo~EsThe Johns Hopkins University, Baltimore, Md.(Manuscript received 1 September 1964, in revised form 9 July 1965)ABSTRACT A numerical-statistical forecasting method is presented in which statistical techniques are used on theerrors of numerical predictions. This, in effect

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Eugene J. Aubert

APRIL 1956EUGENE J. AUBERT207A MULTI-LEVEL BAROCLINIC MODEL APPLICABLE TNUMERICAL PREDICTIONBy'IGugeme J. AubertAir Force Cambridge Research Center(Manuscript received 31 August 1955)ABSTRACTSubject to various approximations, a system of partial differential equations is developed which is capableof forecasting the height of and vertical motion at the various pressure surfaces. Various techniques ofnumerical solution are indicated.I. IntroductionDuring the last decade, numerical weather

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Ian D. Rutherford

method amounts to performing an interpolation in the field of apparent forecast errors, asdefined by the available observations, by a linear least-squares technique similar to Gandin's. Statistics onthe errors of observation and prediction are required. The spatial autocorrelations of the forecast errordepend on the particular forecast model employed, the forecast period, etc. Those statistics necessary for theassimilation of synoptic data with four-level baroclh~ic 12-hr forecasts have been derived

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Xuguang Wang and Craig H. Bishop

. Section 6 compares the maximal growth within ensemble perturbation subspaces under the total energy norm. Section 7 compares the ensemble forecast skills in terms of ensemble mean. In section 8 , we introduce methods to measure the error variance prediction accuracy and perform comparisons between the ETKF and breeding schemes. The computational expense of the techniques is compared in section 9 . Section 10 summarizes our results. 2. Ensemble generation methods a. Simple breeding Following

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Craig H. Bishop and Zoltan Toth

illustrates the validity of a fundamental assumption of the ensemble transform technique—that estimates of prediction and analysis error covariance matrices may, in principle, be made from appropriately configured ensemble perturbations. Prediction or forecast error covariance matrices 1 represent key aspects of our uncertainty in weather forecasts. For example, one might be interested in using adaptive observations to minimize normalized prediction error variance at some verification time, t υ , over

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Mariano A. Estoque

AUGUST 1957MARIAN0 A. ESTOQUE293A GRAPHICALLY INTEGRABLE PREDICTION MODEL INCORPORATING OROGRAPHIC INFLUENCESBy Mariano A. Estoque University of Chicagose(Manuscript received 15 November 1956)ABSTRACTAn attempt to incorporate the effect of sloping terrain in a two-level model is presented. The resultingprediction equations may be integrated by graphical techniques. To derive the equations, it is necessary toprescribe an analytic expression for the vertical velocity profile. An

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Brad E. Beechler, Jeffrey B. Weiss, Gregory S. Duane, and Joseph Tribbia

, improving computational data assimilation techniques to account for errors in the locations of structures will improve the model’s prediction. Tools have been developed for a forecaster to locate structures and correct their positions by hand ( Hou and Strum 1999 ); however an objective, automated algorithm is more suited for the vast amounts of data in use today. The causes of location errors are complicated but certainly model simplifications, discretization, poor estimates of the background error

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Ernest M. Rampey

JUNE 1953ERNEST M. RAMPEVFORECASTING THE SPEED OF MOVEMENT OF SELECTED CYCLONESALONG UPPER-AIR STEERING CHANNELS By Ernest M. Rampey U. S. Weather Bureau(Original manuscript received 24 November 1952 ; revised manuscript received 26 January 1953)ABSTRACTFollowing the selection of upper-air steering channels on the basis of the height of the "closed" circulation, an objective technique is developed for forecasting the 24- and 30-hour displacements of surface "lows

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Martin Leutbecher

operational version predicts forecast error variance reductions that deviate substantially from the actual average reductions of the magnitude of the forecast error ( Majumdar et al. 2001 ). While recent research (C. Bishop 2002, personal communication) shows that the error of the ETKF forecast error variance prediction is significantly reduced by improving the technique's routine analysis error covariance estimate, the skill of the ETKF is limited by small ensemble sizes and the inconsistency between the

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