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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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Irving I. Gringorten

388JOURNAL OF METEOROLOGYVOLUME IFORECASTING BY STATISTICAL INFERENCES By Irving I. GringortenAir Force Cambridge Research Laboratories(Manuscript received 27 July 1950)ABSTRACTIn recognition of the fact that a weather forecast is rarely 100 per cent accurate, this paper considers thevalue of figures for the probability of a meteorological event in meeting specified operational requirements.An objective method is presented for deciding between alternative meteorological predictors. It is

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H. M. Christensen, I. M. Moroz, and T. N. Palmer

Parameter Estimation System (EPPES) ( Järvinen et al. 2012 ; Laine et al. 2012 ), which runs online in conjunction with an operational ensemble forecasting system. As well as producing an improved estimate of the value of the parameters, this procedure also produces an estimate of the uncertainty in the values of these parameters in the form of a joint probability distribution. EPPES is of central interest to this study, as this distribution can be used to develop a perturbed parameter scheme without

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Jeffrey L. Anderson

model's attractor. Three relatively inexpensive algorithms for finding the localam'actor structure in a simple model are examined; these make use of singular vectors, normal modes, andperturbed integrations. All of these are related to heuristic algorithms that have been applied to select ensemblemembers in operational forecast models. The method of perturbed integrations, which is somewhat similar tothe "breeding" method used at the National Meteorological Center, is shown to be the most effective

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Kevin Judd, Carolyn A. Reynolds, Thomas E. Rosmond, and Leonard A. Smith

1. Introduction In operational numerical weather prediction (NWP), data assimilation is a process whereby a series of observations is transformed into a single best-guess model state or an ensemble of model states from which forecasts are to be launched. In the perfect model scenario, an ensemble would consist of a set of model states, each the end point of a model trajectory consistent with observations of the system. If the system evolves on an attractor, then the ensemble members should lie

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Nedjeljka Žagar, Roberto Buizza, and Joseph Tribbia

1. Introduction The need for uncertainty information related to numerical weather forecasts has been long recognized and, over the past two decades, nearly all major weather services have implemented operational global ensemble prediction systems (e.g., Buizza et al. 2005 ). The value of ensemble prediction systems can be described by their ability to provide flow-dependent estimates of forecast uncertainty. Verification and diagnostics of ensemble performance focus on reliability and accuracy

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Isla Gilmour, Leonard A. Smith, and Roberto Buizza

1. Introduction Uncertainty in the initial condition combined with model error renders prediction of a chaotic system nontrivial. Indeed, the day-to-day variation in growth of uncertainties of the initial state of the atmosphere has led many operational weather forecasting centers to adopt ensemble forecasting (i.e., the use of ensembles of initial conditions evolved under a model). Investigations that aim either to further understanding of uncertainty growth in the system or to improve

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John O. Roads

present operational models produces an rms error equalto the climatological rms error and a correlation of 0.5 on about day 12 of the forecast. At the largest scales,this limiting point is reached shortly thereafter. The error continues to grow at a decreasing rate until at about30 days the forecast skill is extremely small and comparable to the skill of a persistence forecast. Various time averages at various lags were examined for skill in the extended range. Filters that weightedmost strongly

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Richard C. J. Somerville

,the use of global rather than hemispheric models, even for forecasts of only a few days, might be beneficialin operational practice.1. IntroductionThe ultralong planetary waves, of middle latitudesare the pride and the sorrow of numerical weatherprediction. On the one hand, it is in these largestscales of motion that much of the theoretical basisfor deterministic extended-range predictability is tobe found. Relative to the theoretical expectations,however, the skill of typical actual forecasts ofthese

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P. D. Thompson and W. Lawrence Gates

betweenpredicted and observed 24-hr height changes of only 0.62, show considerable synoptic "skill," especiallyover the eastern United States. These forecasts are felt to demonstrate conclusively the applicability ofnumerical prediction techniques to operational forecasting. Further research on the effects of both thephysical and mathematical approximations of the methods of numerical prediction is suggested.1. IntroductionIt is by now a widely accepted fact that the behaviorof large-scale disturbances in the

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