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Ryan D. Torn

flow will excite new troughs and ridges downstream of the ET via Rossby wave packets (e.g., Chang and Orlanski 1993 ; Orlanski and Sheldon 1995 ; Hakim 2003 ). These ET-related wave packets can lead to decreased predictability over much of the hemisphere following ET (e.g., Jones et al. 2003 ; Harr et al. 2008 ; Anwender et al. 2008 ). In particular, Harr et al. (2008) and Anwender et al. (2008) found that the variability of ensemble forecasts during ET is related to two distinct modes

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Lauriana C. Gaudet, Kara J. Sulia, Tzu-Chin Tsai, Jen-Ping Chen, and Jessica P. Blair

microphysical models used herein are described in section 2 and details regarding the simulations within the ensemble in section 3 . The following sections address the observations and ensemble forecast of the Ontario Winter Lake-effect System (OWLeS; Kristovich et al. 2017 ) intensive observing period 4 (IOP4) in a synergistic manner. Section 4 includes an overview of the LES mesoscale characteristics and associated precipitation. A breakdown of the precipitation type and its influence on the LES

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Shuyi S. Chen and William M. Frank

structural features in * Current affiliation: Department of Atmospheric Sciences, University of Washington, Seattle, Washington. Corresponding author address: Dr. Shuyi Si Chen, Department ofAtmospheric Science, AK-40, University of Washington, Seattle, WA98195.common with MCCs. These weather systems have beencategorized as mesoscale convective systems (MCSs).Both MCCs and MCSs are particularly important toextratropical summertime weather forecasts due totheir heavy rainfall and severe weather

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Lisa J. Neef, Saroja M. Polavarapu, and Theodore G. Shepherd

1. Introduction Four-dimensional (4D) data assimilation schemes are ones that use background knowledge from the assimilating model to derive flow-dependent forecast error statistics. Such flow-dependent forecast error statistics should also, in principle, contain information about any dynamical balance that might exist between the mass and velocity fields. This is important because, since observation errors project onto all degrees of freedom, dynamical balance can easily be destroyed by the

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J. M. Fritsch, J. D. Murphy, and J. S. Kain

of squall lines. Tellus, 7, 405-436.Gamache, J. F., and R. A. Houze, 1982: Mesoscale air motions asso ciated with a tropical squall line. Mon. Wea. Rev., 110, 118-135.--., and --., 1985: Further analysis of the composite wind and thermodynamic structure of the 12 September GATE squall line. Mon. Wea. Rev., 113, 1241-1259.Giordano, L. A., and J. M. Fritsch, 1991: Strong tornadoes and flash flood-producing rainstorms during the warm season in the Mid Atlantic Region. Wea. Forecasting, 6

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J. M. Fritsch and C. F. Chappell

Numerical Prediction of Convectively Driven Mesoscale Pressure Systems. Part H: Mesoscale Model $. M. FtUTSCH ANO C. F. CHAPPELLAtmospheric Physics and Chemistry Laboratory, NOAA, Boulder, CO 80303(Manuscript received 2 February 1979, in final form 10 April 1980)ABSTRACTA 20-level, three-dimensional, primitive equation model With 20 km horizontal resolution is used topredict the development of convectively driven mesoscale pressure systems. Systems produced by themodel

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C. B. Chang, D. J. Perkey, and C. W. Kreitzberg

devoted to simulation results using the mesoscale mesh. 2. Analysis and prediction techniques The Drexel/NCAR Limited Area Mesoscale Pre diction System (LAMPS) was used to perform the numerical simulations, for this study. A brief over view of the system follows.a. Analysis procedures The analysis and initialization procedures used toobtain the initial conditions for the fine-mesh forecast are outlined below. 1) The mass field was objectively analyzed onisentropic surfaces using the analysis

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Jason A. Sippel and Fuqing Zhang

cyclone intensity forecasts, Sippel and Zhang (2008 , hereafter SZ08) and Zhang and Sippel (2009 , hereafter ZS09) used mesoscale ensembles and sensitivity experiments to investigate intensity forecast uncertainty in a nondeveloping Gulf of Mexico low. SZ08 found that variations in convective available potential energy (CAPE) and moisture through a deep layer were two factors that strongly influenced the genesis forecast. Ensemble members with higher initial values of these two variables had

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Y. Qiang Sun, Fuqing Zhang, Linus Magnusson, Roberto Buizza, Jan-Huey Chen, and Kerry Emanuel

1. Introduction Predictability is a fundamental concept for numerical weather prediction (NWP). As the numerical prediction proceeds, dynamical instabilities and chaotic nonlinear interactions cause the forecast uncertainty to increase until the differences between individual ensemble forecasting members are statistically indistinguishable from random draws from the numerical model’s climate. After that, NWP provides no information that could not be readily obtained from a previously generated

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Falko Judt and Shuyi S. Chen

by convection in the rainband regions of Hurricanes Rita and Katrina. These differences play a key role in the formation of Rita’s secondary eyewall. High-resolution, cloud-resolving model forecast fields obtained in real time during RAINEX are used to explain the physical and dynamic processes. The model results are compared with the RAINEX observations to evaluate and validate the evolution in hurricane structure in both storms. 2. Model and data a. The numerical model The numerical model used

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