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Dev Niyogi, Kiran Alapaty, Sethu Raman, and Fei Chen

, and energy exchange ( Farquhar et al. 1980 ; Collatz et al. 1991 , 1992 ; Anderson et al. 2000 ; see also Niyogi and Raman 1997 ). These schemes generally have been applied in leaf/canopy scale models (e.g., Baldocchi 1992 ), or in global/regional climate studies ( Sellers et al. 1996 ; Calvet et al. 1998 ; Cox et al. 1999 ; Dai et al. 2003 ). Mesoscale or weather forecast models continue to be predominantly Jarvis-based (e.g., Chen and Dudhia 2001 ; Ek et al. 2003 ), but several reasons

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Yubao Liu, Thomas T. Warner, James F. Bowers, Laurie P. Carson, Fei Chen, Charles A. Clough, Christopher A. Davis, Craig H. Egeland, Scott F. Halvorson, Terrence W. Huck Jr., Leo Lachapelle, Robert E. Malone, Daran L. Rife, Rong-Shyang Sheu, Scott P. Swerdlin, and Dean S. Weingarten

paper is the first in a series of papers that provides a unified and comprehensive description of the modeling technology, the challenges associated with the operational use of the forecasting system, and the scientific insights gained by its use. The U.S. Army test ranges are typically located where there is strong local forcing from complex orography or coastlines, resulting in myriad mesoscale processes ( Rife et al. 2002 ). These processes include coastal breezes; orographic effects such as

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Ben C. Bernstein, Roy M. Rasmussen, Frank McDonough, and Cory Wolff

with the expected LWC already calculated as part of the CIP and FIP icing severity algorithms ( Bernstein et al. 2006b ; Wolff et al. 2009 ). Diagnoses and short-term forecasts of icing, SLD, and mesoscale variability thereof, can be further enhanced through expanding the use of the satellite-, radar-, surface-, and model-based features described above. Acknowledgments The authors would like to express our sincere thanks to the crews of the NASA-Glenn Twin Otter, the NRC Convair-580, and the

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Robert D. Sharman, Yubao Liu, Rong-Shyang Sheu, Thomas T. Warner, Daran L. Rife, James F. Bowers, Charles A. Clough, and Edward E. Ellison

1. Introduction Part I of this series of papers ( Liu et al. 2008a ) provides an overview of an operational mesogamma-scale forecast model, called the Real-Time Four-Dimensional Data Assimilation (RTFDDA) system, that is in use at the U.S. Army Test and Evaluation Command (ATEC) test ranges. The forecast component of RTFDDA is based on the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5; Grell et al. 1995 ). The forecast model

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

realistic (as opposed to idealized) atmosphere, and they provide insights into dynamic interaction processes that may occur during a real event. This study uses the coupled Weather Research and Forecasting (WRF) Model and fire-spread model (SFIRE) module, described in detail by Mandel et al. (2011) . WRF and SFIRE couple the WRF Model with an implementation of the Rothermel (1972) fire-spread equations. Coupled fire–atmosphere models have been used in a number of studies to show that dynamical

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Iris Odak Plenković, Luca Delle Monache, Kristian Horvath, and Mario Hrastinski

procedure should be able to cope even with drastic changes in both the starting model and the AN forecast error. 3. NWP model data Three configurations of the operational limited-area mesoscale NWP model ALADIN ( ALADIN International Team 1997 ) of the Croatian Meteorological and Hydrological Service are used to generate 10-m wind speed forecasts: The operational limited-area mesoscale ALADIN model is launched twice a day (0000 UTC and 1200 UTC) at 8-km horizontal grid spacing (A8). The A8 model uses

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Miriam L. Rorig, Steven J. McKay, Sue A. Ferguson, and Paul Werth

weather predictions ( Mass et al. 2003 ). Because most fire weather forecasting tools use variables that are output by the mesoscale models, many value-added products are being generated in support of the fire community. For example, gridded next-day predictions of NFDRS indices are currently available for the continental United States ( http://www.fs.fed.us/land/wfas/wfas26.html ) and for the Pacific Northwest ( http://www.fs.fed.us/pnw/airfire/sf ) ( Hoadley et al. 2006 ). The current study uses

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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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Marc A. Byrne, Arlene G. Laing, and Charles Connor

simulations by Grell et al. (2000) . Computing costs are minimized by utilizing the MM5 multiprocessor mode on a Linux cluster. An eight-processor mode was found to be the most stable configuration on the cluster and each minute of computing time accounted for 4 min and 40 s of forecast time. To account for spinup, as the mesoscale model is given time to create dynamically and physically consistent meteorological fields, the model was started on 25 November, although the continuous phase of the eruption

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Stephen D. Burk and William T. Thompson

AUGUST 1992 BURK AND THOMPSON 925Airmass Modification over the Gulf of Mexico: Mesoscale Model and Airmass Transformation Model Forecasts STEPHEN D. BURK AND WILLIAM T. THOMPSONNaval Oceanographic and Atmospheric Research Laboratory, Atmospheric Directorate, Monterey, California(Manuscript received 8 April 1991, in final form 2 December 1991)ABSTRACT Several numerical models are

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