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Benoît Vié, Olivier Nuissier, and Véronique Ducrocq

. As physical parameterizations at smaller scales show stronger nonlinearities, convective-scale perturbations grow much faster and even impact the large-scale predictability. The sensitivity to ICs ( Ducrocq et al. 2002 ) is also different between parameterized and resolved convection. Moreover, in addition to the uncertainties on ICs and the model errors, cloud-resolving ensembles must also consider the uncertainty due to lateral boundary conditions (LBCs), since they are run over a limited area

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

on the Met Office Unified Model (UM; Davies et al. 2005 ) and uses the ensemble transform Kalman filter (ETKF; Bishop et al. 2001 ) to calculate initial condition perturbations for 23 ensemble members. A control run at the same resolution without perturbations completes a 24-member suite that runs twice a day (global at 0000 and 1200 UTC, and regional at 0600 and 1800 UTC). The regional suite is driven by lateral boundary conditions output from the global suite initialized 6 h earlier and using

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

horizontal grid spacing of 24 km (2 times that of the operational model) and 38 vertical levels. This version of the model is nonhydrostatic and uses the new dynamics formulation ( Davies et al. 2005 ) for the dynamical core and a semi-implicit, semi-Lagrangian numerical scheme. The global control forecast from the Met Office Global and Regional Ensemble Prediction System (MOGREPS) provided lateral boundary conditions for the hindcast. The Met Office incremental 4D-Var assimilation scheme ( Rawlins

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

Single-Moment (WSM) six-class microphysics ( Dudhia et al. 2008 ), and the Mellor–Yamada–Janjić (MYJ) boundary layer scheme ( Mellor and Yamada 1982 ; Janjić 1990 ). A second-order diffusion scheme is employed, and no damping is used. Several global models were initially examined for use as initial and boundary conditions. An intercomparison between the 2008 versions of the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS), the Navy Operational Global Atmospheric

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

, especially the larger ones, and the interaction of the vortex with the steering flow may be handled more accurately. Concerning advanced data assimilation methods, four-dimensional variational data assimilation (4D-Var; Le Dimet and Talagrand 1986 ; Courtier et al. 1994 ; Rabier et al. 2000 ; Rawlins et al. 2007 ) has been a standard method for data assimilation at many operational centers worldwide. However, the typical 4D-Var does not directly produce an ensemble of initial conditions that

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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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