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John E. Janowiak, Peter Bauer, Wanqiu Wang, Phillip A. Arkin, and Jon Gottschalck

predictions have been conducted over the past few decades. Janowiak (1992) evaluated the performance of short-range forecasts (12–36 h) of precipitation that were generated by the operational numerical weather prediction models (ca. 1989) at the European Centre for Medium-Range Weather Forecasts (ECMWF) and at the National Centers for Environmental Prediction (NCEP) in the United States. One of the conclusions in that paper was that while the models did a very good job of representing the seasonal and

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Thomas M. Hamill, Jeffrey S. Whitaker, Michael Fiorino, and Stanley G. Benjamin

individual member, especially at longer leads, thus contributing to the HFIP goal of a reduction in track error. The diversity of track forecasts and maximum surface wind speed forecasts (TC intensity) could be used to quantitatively assess risk and make more appropriate and earlier decisions about coastal evacuations. The current state of the art of TC forecasts from global operational ensemble systems was recently described in Majumdar and Finocchio (2010) . Two technological improvements that may

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E. A. Irvine, S. L. Gray, J. Methven, and I. A. Renfrew

results of which are presented in Irvine et al. (2009) . Using the Met Office operational 4D-Var system, the overall forecast impact was small and forecasts were improved and degraded by similar magnitudes (up to 5%, measured in terms of total energy). The study presented here takes one targeting case from Irvine et al. (2009) , in which dropsondes were targeted in a total-energy singular-vector sensitive region in the lee of Greenland to improve the 24-h forecast over Scandinavia. Irvine et al

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Mio Matsueda, Masayuki Kyouda, Zoltan Toth, H. L. Tanaka, and Tadashi Tsuyuki

scientific advance [(World Meteorological Organization) WMO 2006 ]. In recent years, ensemble forecasts have become a major component of operational global weather-prediction systems, gaining increasing attention at various time scales (short, medium, and long range) for both operational and research purposes. In ensemble forecasting, multiple forecasts are performed by introducing perturbations in the initial conditions, in the boundary conditions or in the models themselves, mainly in order to

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William A. Komaromi, Sharanya J. Majumdar, and Eric D. Rappin

aforementioned objective sensitivity methods and their limitations. Two TCs, Typhoon Sinlaku (2008) and Hurricane Ike (2008), are selected for this study, given their importance to society and the problems encountered in their operational forecasts 3–5 days prior to landfall. For each case, the variations in the TC track due to initial perturbations of differing locations and amplitudes are examined, together with an investigation of how the environment is modified to alter the track forecasts. The

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Ronald Gelaro, Rolf H. Langland, Simon Pellerin, and Ricardo Todling

Group on Data Assimilation and Observing Systems (DAOS IWG) to directly compare the impact of observations in different operational, or near-operational, forecast systems. The specific objective of the comparison experiment is to provide, if possible, robust answers to the following questions: How similar or different are the impacts of observations in one forecast system versus another? Which observation types have the largest total impacts, and impacts per observation? How do observation impacts

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Benoît Vié, Olivier Nuissier, and Véronique Ducrocq

integrating the Liouville equation, which describes the temporal evolution of a probability density function (PDF). Both the imperfect knowledge of the initial PDF and the prohibitive numerical cost of this integration led to the development of ensemble prediction ( Leith 1974 ). Using ensemble prediction systems (EPSs) is now a well-known approach, which has been developed since the 1990s for medium-range synoptic-scale forecasting. Such global operational EPSs generate a set of atmospheric states

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Warren J. Tennant, Glenn J. Shutts, Alberto Arribas, and Simon A. Thompson

improvements to the atmospheric observing system, increased computing power, and more sophisticated models, the development of operational EPS suites took place at, inter alia, the National Centers for Environmental Prediction (NCEP; Toth and Kalnay 1993 ), the Meteorological Service of Canada (MSC; Houtekamer et al. 1996 ), and the European Centre for Medium-Range Weather Forecasts (ECMWF; Buizza and Palmer 1995 ; Molteni et al. 1996 ). Some centers focused on estimating uncertainty in initial

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Elizabeth Satterfield and Istvan Szunyogh

Szunyogh (2010) , here we provide only a brief summary of the design of the experiments. All experiments are carried out with a reduced resolution (T62L28) 2004 version of the model component of the operational National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS). Observations are assimilated with the implementation of the local ensemble transform Kalman filter (LETKF) data assimilation scheme on the NCEP GFS, which was described in detail in Szunyogh et al. (2008) . With

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Sharanya J. Majumdar, Kathryn J. Sellwood, Daniel Hodyss, Zoltan Toth, and Yucheng Song

observation locations ( Hodyss and Majumdar 2007 ). In this paper, we use a multimodel ensemble comprising 145 forecasts from three operational centers. We therefore expect the covariance structure to be more accurate than that derived by Sellwood et al. (2008) , given that the uncertainty derived from a multimodel ensemble is expected to be closer to the true range of possibilities than that represented by a single model ensemble. It is worth noting, however, that correlated model error may become

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