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Wansuo Duan and Zhenhua Huo

. Various approaches have been introduced to generate initial perturbations for ensemble forecasting and have been applied in operational weather and climate predictions. Among these approaches, singular vectors (SVs) ( Lorenz 1965 ; Molteni et al. 1996 ; Mureau et al. 1993 ) have been adopted in operational forecasts by the European Centre for Medium-Range Weather Forecasts (ECMWF) and have achieved great success in reducing forecast uncertainties. SVs are a group of orthogonal initial perturbations

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Siegfried D. Schubert, Max J. Suarez, and Jae-Kyung Schemm

Branch, Code 911, Laboratory for Atmospheres,Goddard Space Flight Center, Greenbelt, MD 20771. A complicating factor in these studies is that timesof increased forecast skill also tend to be times duringwhich the atmosphere is more persistent. Tracton etal. (1989), for example, show the skill of persistenceand experimental extended range forecasts (winter of1986/87) produced with the operational MediumRange Forecast model at the National MeteorologicalCenter (NMC) ~ While the NMC model generally

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Oreste Reale, William K. Lau, Kyu-Myong Kim, and Eugenia Brin

European Centre for Medium Range Weather Forecasts (ECMWF) so-called nature run (a 13-month-long simulation in free running mode performed with the then-operational ECMWF model at the horizontal resolution of T511, corresponding to approximately 40 km) and verified that the simulation contained quasi-realistic AEW activity and a realistic number of Atlantic tropical cyclones. Strictly speaking, the resolutions adopted by the previously referenced studies cannot yet be considered adequate to resolve the

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P. J. Roebber and A. A. Tsonis

operational data assimilation cycle was mimicked: an analysis was constructed, consisting of a weighted average of observations (incorporating an unbiased error e nb,o ) and a first guess (obtained from a previous short-term forecast from the model). This analysis was used as the basis for the next forecast, and the short-term forecast from that run was then cycled back into the succeeding analysis. For all our tests, the set of forecasts were extended from two to four iterations into the future, a time

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James A. Ridout

atmospheric forecast model and a hydrostatic ocean model ( Hodur 1997 ). Forecasts are carried out using lateral boundary conditions from the Navy Operational Global Atmospheric Prediction System (NOGAPS; Hogan and Rosmond 1991 ). For the present work, Davies (1976) boundary conditions are used, though other options are available. The inner nests are run without allowing for feedback to the parent nests. This treatment simplifies interpretation of the results, and test runs with the nest feedback

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W. Lawrence Gates

is essentially freefrom boundary influence over the central 20 per cent or338cJOURNAL OF METEOROLOGYVOLUME 14FIG. 7. Predicted and observed 48-hr 500-mb height changes for period beginning at 1500 GCT 22 January 1953, in hundreds of feet.so of the grid area under average conditions. Thismargin of uncontaminated results may be satisfactory for some purposes of testing and short-range prediction. However, if forecasts for longer periods arerequired, either for operational purposes or to

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Corey K. Potvin, Elisa M. Murillo, Montgomery L. Flora, and Dustan M. Wheatley

sensitivity to IC resolution exhibited in our experiments indicates that the evolution of many physically and societally important aspects of supercells, including low-level rotation and heavy rainfall, are primarily determined by larger scales. It should be borne in mind that in addition to IC resolution errors, other operational forecast error sources (e.g., physics parameterization, finite grid resolution, large-scale analysis errors) will remain significant for decades to come. It is also important to

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David Orrell

system was first increased to Δ F = 1.5 and Δ t = 6 so as to give errors comparable (for the same scaling as before) with operational weather models. From Eq. (2) , this represents an effective increase of about 3.7 in the model drift. The observation error was increased to 2 m s −1 . The upper panel of Fig. 3 shows a plot of the resulting forecast error, when measured relative to the untreated observations. The next step was to treat the observations using four-dimensional variational data

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Hongyan Zhu and Alan Thorpe

(UKMO) and the European Centre for Medium-Range Weather Forecasts (ECMWF) forecasting systems. In these studies, substantial forecast differences between the ECMWF and the UKMO operational forecasts could mostly be traced to differences between the two operational analyses, rather than between forecast models. On the other hand, recent results from Harrison et al. (1999) indicate that the impact of model uncertainties on forecast error cannot be ignored. A study by Orrell et al. (2001) , claimed

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J. Berner, G. J. Shutts, M. Leutbecher, and T. N. Palmer

all currently operational ensemble systems is that they are underdispersive; that is, if realistic initial perturbations are chosen, the best estimate of the true atmospheric state is on average more often outside the range of predicted states than statistically expected (e.g., Buizza et al. 2005 ). In other words, the trajectories of the individual ensemble members do not diverge rapidly enough to represent forecast error. This underdispersiveness might arise in part from a misrepresentation of

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