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Guillem Candille

dispersion—measures the agreement between the spread defined by the ensemble and the observation standard deviation, and the forecast error of the ensemble mean. A dispersion larger than 1 characterizes an underdispersive system. The limited number of cases on which the statistics are accumulated could restrict the significance of the diagnoses. Confidence intervals (5%–95%) on the CRPS and the RCRV are then defined by bootstrap techniques ( Efron and Tibshirani 1993 ) in order to provide statistically

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Hiroshi Koyama and Masahiro Watanabe

arising from the discretization of fluid elements, parameterizations for unresolved subgrid-scale phenomena, and approximations in governing equations. To reduce the forecast error caused by these model imperfections, two main approaches can be used: improving the atmospheric model and introducing the model ensemble technique. First, the fundamental improvement of the model itself, which involves improving the parameterization schemes, narrowing the grid intervals, and using more accurate governing

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Mark DeMaria

) will be referred to as the logistic growth equation model (LGEM). 3. Preliminary forecast results A preliminary version of LGEM was developing using a multiple regression technique (hereafter LGEM-MR) for estimating the model parameters from the SHIPS model input. This input included maximum winds and storm positions at 6-h intervals from the NHC best track ( Jarvinen et al. 1984 ) and predictors from Reynolds SST ( Reynolds et al. 2002 ) and the NCEP Global Forecasting System (GFS) model analyses

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André Walser, Marco Arpagaus, Christof Appenzeller, and Martin Leutbecher

1. Introduction Probabilistic weather forecasting methodologies have been developed in order to take into account the chaotic behavior of the atmospheric synoptic-scale flow (see the reviews by Ehrendorfer 1997 ; Palmer 2000 ). Since 1992, global ensemble prediction systems (EPSs) based on multiple forecasts became operational at the National Meteorological Center [NMC; now the National Centers for Environmental Prediction (NCEP); Toth and Kalnay 1997 ] and at the European Centre for Medium

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Ian T. Jolliffe and Cristina Primo

1. Introduction It is common to use rank histograms to evaluate the performance of ensemble forecasting systems (see Elmore 2005 , hereinafter E05 , and references cited therein). An ideal system produces a “flat” or uniform histogram, but because of sampling variation the histograms are almost never exactly flat. The question then arises: can observed deviations from “flatness” or uniformity be attributed to chance, or do they indicate deficiencies in the forecasts? An overall test of

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Mei Hong, Ren Zhang, Dong Wang, Min Wang, Kefeng Liu, and Vijay P. Singh

values at adjoining times; that is, . By sampling an equal time interval, , taking an equal time interval , where is the initial time, becomes the forecast time, and is the retrospective order. Incorporating and , a discretized self-memorization equation is obtained as where is the dynamic core of the self-memorization equation: ; . We call the technique where one performs a forecast and computation based on Eq. (4) a self-memorization principle. Using Eq. (2) as the dynamical

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Fan Han and Istvan Szunyogh

resolution (e.g., Mass et al. 2002 ; Done et al. 2004 ). This result may be due to limitations of the traditional, point-to-point verification techniques rather than to lack of forecast improvement. In particular, such techniques can indicate large errors in situations where generally well-predicted precipitation events of high spatial variability are slightly misplaced. This problem has motivated the search for verification techniques that can separate the location (displacement) error from errors in

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Edward R. Mansell, Conrad L. Ziegler, and Donald R. MacGorman

the entire earth, including over all oceans, have already been demonstrated. Thus, techniques for assimilating lightning data could be applied in extensive regions where radar coverage does not exist, such as over oceans. Relatively little has been done, however, to develop techniques for assimilating lightning data. Two studies ( Alexander et al. 1999 ; Chang et al. 2001 ) demonstrated an improvement in the 12–24-h forecast of rainfall and location of convection in an intense extratropical

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James S. Goerss

forecast TC intensity, initial TC position and forecast displacement of TC position (latitude and longitude), TC speed of motion, and the number of members available to the consensus model. In the next section we describe how these predictors are used to estimate CONU TC track forecast error for the Atlantic basin. We also illustrate how the results of this error estimation are displayed for the NHC forecasters. In section 3 we outline the results of independent data testing of the technique. In the

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Astrid Suarez, David R. Stauffer, and Brian J. Gaudet

model and verification network is included in sections 2 and 3 , respectively. Descriptions of the wavelet transform and decomposition methodology are provided in section 4 . The efficacy of this new technique is demonstrated for the verification of submeso variability due to changes in the Weather Research and Forecasting (WRF) Model initial conditions. The technique is first illustrated for a case study characterized by terrain-induced gravity waves and nonstationary, submeso motions using

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