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Brian J. Etherton

such guidance more time to make decisions. A forecast cycle (which for most global and regional models, usually starts at 0000, 0600, 1200, or 1800 UTC) consists of taking in all available observations, funneling those observations through a data assimilation scheme, initializing a forecast model, and then integrating that model out to the desired forecast time. This cycle of assimilation and integration often takes a couple of hours to complete. A forecast ensemble is a set of computer forecasts

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Christian Keil and George C. Craig

precipitation intensity is in practice nondeterministic, especially during the warm season. Numerous studies (e.g., Bright and Mullen 2002 ; Yuan et al. 2005 , and references therein) suggest that the ensemble approach could improve short-range weather forecasts, especially precipitation forecasting. In ensemble prediction systems the inherent observational uncertainty, model error, and the chaotic, nonlinear behavior of atmospheric dynamics can be incorporated, providing a range of scenarios with

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Andrew R. Lawrence and James A. Hansen

1. Introduction A popular method used to account for initial condition uncertainty in Numerical Weather Prediction (NWP) is the ensemble approach to forecasting ( Palmer et al. 1992 ; Toth and Kalnay 1993 ). The atmosphere’s initial condition probability distribution function (PDF) is discretely sampled, and each sample is propagated forward by the NWP model. The resulting collection of forecasts is treated as a discrete sample of the forecast PDF. One factor that can limit an ensemble

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Veronica J. Berrocal, Adrian E. Raftery, and Tilmann Gneiting

1. Introduction Ensemble prediction systems have been developed to generate probabilistic forecasts of weather quantities that address the two major sources of forecast uncertainty in numerical weather prediction: uncertainty in initial conditions, and uncertainty in model formulation. Originally suggested by Epstein (1969) and Leith (1974) , ensemble forecasts have been operationally implemented on the synoptic scale ( Toth and Kalnay 1993 ; Houtekamer et al. 1996 ; Molteni et al. 1996

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Fanyou Kong, Kelvin K. Droegemeier, and Nicki L. Hickmon

1. Introduction In Kong et al. (2006 , hereafter Part I ), the authors extended the concept of ensemble forecasting down to the scale of individual convective storms by applying a full-physics numerical prediction system, initialized with observations including Weather Surveillance Radar-1988 Doppler (WSR-88D) radar data, to an observed tornadic thunderstorm complex that passed through the Fort Worth, Texas, area on 28–29 March 2000. Using domains of 24-, 6-, and 3-km

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Matthew S. Jones, Brian A. Colle, and Jeffrey S. Tongue

). As a result, several recent studies have explored the benefits and shortcomings of short-range ensemble forecast (SREF) modeling systems ( Stensrud et al. 2000 ; Wandashin et al. 2001 ; Grimit and Mass 2002 ; Alhamed and Lakshmivarahan 2002 ; among others). Developers of these SREF systems have quantified the impact of initial condition uncertainty, model dynamics diversity, and model physics variability on short-term forecasts. The relative importance of physics (PHS) versus IC uncertainty is

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Paul Block and Balaji Rajagopalan

). This motivates the current research to develop a robust framework for generating ensemble forecasts of the Kiremt season precipitation. This paper begins with a description of the datasets utilized, followed by background on Ethiopian climatology and interannual variability. Potential predictors of the seasonal precipitation are then identified. Next, the proposed nonparametric approach for producing ensemble forecasts is presented, along with the traditional linear regression method. Skill

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Jeffrey S. Whitaker, Thomas M. Hamill, Xue Wei, Yucheng Song, and Zoltan Toth

1. Introduction Ensemble-based data assimilation (EDA) methods are emerging as alternatives to four-dimensional variational data assimilation (4DVAR) methods (e.g., Thépaut and Courtier 1991 ; Courtier et al. 1994 ; Rabier et al. 2000 ) for operational atmospheric data assimilation systems. All EDA algorithms are inspired by the Kalman filter (KF), though in EDA the background-error covariances are estimated from an ensemble of short-term model forecasts instead of propagating the background

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Warren J. Tennant, Zoltan Toth, and Kevin J. Rae

Centers for Environmental Prediction (NCEP) Ensemble Forecasting System (EFS; Toth and Kalnay 1997 ; Toth et al. 2001 ; Buizza et al. 2005 ) is used operationally in South Africa for medium-range forecasts up to 14 days ahead. Ensemble methods ( Leith 1974 ) are considered to be an effective way to estimate the probability density function of future states of the atmosphere by addressing uncertainties present in initial conditions and in model approximations. Notwithstanding, biases remain in these

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Giuseppe Mascaro, Roberto Deidda, and Enrique R. Vivoni

1. Introduction The meteorological and hydrological scientific communities have aimed to develop hydrometeorological forecasting systems for streamflow predictions using ensemble techniques to account for all the sources of uncertainty ( Schaake et al. 2007 ). Advanced ensemble hydrometeorological forecasting systems include the combined use of meteorological and hydrological models as well as of statistical downscaling models, land surface models, and data assimilation systems. The use of such

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