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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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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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Svetlana V. Poroseva, Nathan Lay, and M. Yousuff Hussaini

1. Introduction Severe tropical cyclones, known variously in different parts of the world as hurricanes or typhoons, have had a devastating impact on life, property, and economy. Improving the quality of tropical cyclone track forecasts and developing reliable tools for the quantitative assessment of forecast accuracy and credibility are crucial for enhancing community resilience. Among the various forecasting techniques, multimodel forecasts appear to be generally more reliable in predicting

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Daniel Hodyss, Elizabeth Satterfield, Justin McLay, Thomas M. Hamill, and Michael Scheuerer

techniques. While ensemble weather prediction systems have improved greatly, the predictions are still frequently affected by systematic errors, including biased ensemble mean forecasts and often an insufficiency of ensemble spread. Consequently, much attention has been paid in recent years to statistical postprocessing techniques, whereby the current guidance is adjusted based on relationships noted between past forecasts and observations/analyses. In many circumstances, the goal is to produce a

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Max A. Little, Patrick E. McSharry, and James W. Taylor

1. Introduction The source of most atmospheric rainwater is the sea, with rain forming when large droplets eventually become heavy enough to fall to the ground. Rainfall over land eventually flows back to the sea, completing the cycle ( Brutsaert 2005 ). Water is vital to life, but also immensely destructive: understanding the movement of atmospheric water is critical. Forecasting rainfall is therefore important in many disciplines, for example, economics and finance, hydrology, meteorology

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