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David J. Stensrud, Geoffrey S. Manikin, Eric Rogers, and Kenneth E. Mitchell

guidance to forecasters on the evolution of the parameters used to evaluate the potential for heavy precipitation and severe thunderstorms. However, as our understanding of these types of events has improved, the important roles played by mesoscale features have been highlighted ( Maddox et al. 1979 , 1980 ; Olson 1985 ; Doswell 1987 ; Funk 1991 ; Doswell et al. 1993 ). During the warm season, Heideman and Fritsch (1988) show that over 80% of the more significant precipitation events 1 are

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Eric P. Grimit and Clifford F. Mass

Pennsylvania State University–National Center for Atmospheric Research fifth-generation Mesoscale Model (PSU–NCAR MM5; Grell et al. 1994 ) suggest diminishing returns as grid spacing drops below 12 km, when evaluated using standard measures of forecast skill ( Mass et al. 2002 ). Furthermore, numerical model forecasts can be very sensitive to slight changes in the larger-scale initial conditions ( Brooks et al. 1992 ). Recognition of such predictability issues has led to increased interest in developing

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Steven E. Koch and Christopher O’Handley

waves can exert important controls upon convection and mesoscale precipitation patterns, but in general, the operational community mistakenly perceives gravity waves as being too inconsequential, or occurring too infrequently, or being too difficult to forecast and diagnose, to be worthy of consideration in a daily forecast environment. Issues that immediately arise in this weather forecasting context include the following: 1) What kinds of gravity waves are important to the weather? 2) How

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Mika Peace, Trent Mattner, Graham Mills, Jeffrey Kepert, and Lachlan McCaw

mesoscale environment. Several of these (e.g., Mills 2005 , 2008a ; Charney and Keyser 2010 ; Zimet et al. 2007 ) describe dynamical mixing of dry and high-momentum air from the mid–upper troposphere to above a fire site. In each of the events above, extreme fire behavior occurred in an environment where dry, high-momentum air was present in the midtroposphere. Each study proposed meteorological mechanisms by which the surface fire activity could be enhanced by mixing of the air mass from the mid

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William H. Raymond and Roland B. Stull

DECEMBER 1990 RAYMOND AND STULL 2471Application of Transilient Turbulence Theory to Mesoscale Numerical Weather Forecasting WILLIAM H. RAYMOND*Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin, Madison, Wisconsin ROLAND B. STULL*Department of Meteorology, University of Wisconsin, Madison, Wisconsin(Manuscript received 20 Mamh 1989, in

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Shengjun Zhang, Tim Li, Xuyang Ge, Melinda Peng, and Ning Pan

code. Thus, even though the observed TC structure is given, there is an observation error associated with the observed data. We did include some level of reasonable observation noise. b. Application of the TCDI–3DVAR scheme to an operational forecast system The aforementioned dynamic initialization scheme has been implemented in the Coupled Ocean–Atmosphere Mesoscale Prediction System for Tropical Cyclones (COAMPS-TC; Hendricks et al. 2011 ). Figure 11 is a flowchart describing how the TCDI

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Dustan M. Wheatley and David J. Stensrud

high-resolution numerical weather prediction (NWP) products can serve as a proxy for the real atmosphere, but the initial conditions of NWP models are often devoid of important mesoscale features, a potential source of forecast error. Mesoscale surface data assimilation is one approach for improving model initialization/spinup and subsequently derived products. The present study emphasizes the potential role of including surface pressure observations in mesoscale ensemble data assimilation. To meld

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Donald C. Norquist

standard meteorological variables (temperature, wind, humidity, pressure) as produced by the theater numerical weather prediction (NWP) model. Cloud forecasts are required out to 36 h beyond the current (most recent observation) time, with a stated degree of accuracy. This paper describes the methods developed to provide the required cloud predictions and presents an assessment of their predictive skill. Because the cloud variables required are not explicitly predicted by any known mesoscale NWP

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A. Amengual, D. S. Carrió, G. Ravazzani, and V. Homar

and Kalnay 1993 ; Mullen and Baumhefner 1988 ; Houtekamer and Derome 1995 ; Du et al. 1997 ). Indeed, errors of any origin can grow rapidly during the quantitative precipitation forecasting and steer toward misleading predictions, especially when fast-growing modes, such as those leading mesoscale convective developments, are dominant for the predicted field. Therefore, QPF is highly sensitive to errors in the initial conditions (ICs), lateral boundary conditions (LBCs), and model physical

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Brian C. Ancell, Erin Kashawlic, and John L. Schroeder

assimilated were sparse, a result first discussed in Hamill and Snyder (2000) . At finer grid spacing (30 km), Meng and Zhang (2008a) and Meng and Zhang (2008b) showed improved forecast performance with an EnKF over that of 3DVAR with the Weather Research and Forecasting (WRF) Model for a mesoscale convective vortex and a month-long experiment verifying against radiosonde data. Meng and Zhang (2008b) assimilated only radiosonde data (relatively sparse), whereas Meng and Zhang (2008a) assimilated

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