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Ramesh Vellore, Darko Koračin, Melanie Wetzel, Steven Chai, and Qing Wang

1. Introduction For more than a decade, there has been a great demand to understand the significance of predictability in numerical weather prediction models, as the predictability issues are related to the forecast skill. Because of the complex interactions of dynamical, radiative, and microphysical processes that occur on small spatial and temporal scales, realistic simulation of the cloud-capped marine boundary layer remains a potential challenge for most mesoscale models and even more so

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Craig S. Schwartz, Glen S. Romine, Kathryn R. Smith, and Morris L. Weisman

; Xue et al. 2010 ; Johnson et al. 2011 ; Johnson and Wang 2012 , 2013 ). CAPS typically extracted perturbations from the operational Short Range Ensemble Forecast (SREF; Du et al. 2009 ) system and added them to a high-resolution control [such as the North American Mesoscale Forecast System (NAM; Rogers et al. 2009 ) analysis interpolated onto the 4-km domain] to produce their initial high-resolution ensembles ( Kong et al. 2008 , 2009 ). Elsewhere, approaches for producing high

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Binbin Zhou and Jun Du

reconfigured to support daily weather forecasts in China for the event as part of the Research Demonstration Project (hereafter referred as SREF-B08RDP) under the auspices of the World Weather Research Program (WWRP) of the World Meteorological Organization (WMO). Taking advantage of this SREF-B08RDP project, a fog prediction scheme was quantitatively and objectively verified using this mesoscale ensemble data over eastern China to fulfill three goals. The first goal is to examine the effectiveness of a

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Qingyun Zhao, Qin Xu, Yi Jin, Justin McLay, and Carolyn Reynolds

improving ensemble analyses and forecasts for rapid environmental assessment for the U.S. Navy. The system has been extensively tested with real observations from conventional meteorological networks and meteorological satellites. In particular, assimilation experiments have been designed and performed to investigate how the TES technique performs with conventional meteorological observations and satellite data on the synoptic scale, mesoscale, and storm-allowing scale. This paper reports the results

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Daran L. Rife, Christopher A. Davis, and Jason C. Knievel

temporal features in gridded fields. To demonstrate it, we apply our approach to wind at 10 m (AGL), as simulated by a mesoscale NWP model. Variations in 10-m winds over minutes to hours are of great concern to many who rely on weather forecasts. For example, at the test ranges operated by the U.S. Army Test and Evaluation Command (ATEC), who sponsored this work, accurate forecasts of near-surface wind are critical for planning and conducting transport-and-dispersion experiments, precision airdrop

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Dennis G. Baker

206 WEATHER AND FORECASTING VOLUME IGrants ATM-8407142 and 8600543.An Experiment in Mesoscale Weather Forecas~ng in the Michigan Area DENNIS G. BAKERDepartment of Atmospheric & Oceanic Science, University of Michigan, Ann Arbor, MI 48109(Manuscript received 23 July 1986, in final form 25 September 1986)ABSTRACT During an experiment in mesoscale weather forecasting in the Michigan area, consensus

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Eric A. Hendricks, Melinda S. Peng, Xuyang Ge, and Tim Li

3DVAR system in the Naval Research Laboratory’s mesoscale TC prediction model. A companion study (Zhang et al. 2011, manuscript submitted to Wea. Forecasting , hereafter ZLGPP) examines the dynamic initialization scheme with the Weather Research and Forecasting Model (WRF). The outline of this paper is as follows. In section 2 , the mesoscale TC prediction model is described, and both the control and dynamic initialization procedures are discussed. In section 3 , structure and intensity

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Raul Fernando Mendez Turrubiates, Markus Gross, and Vanesa Magar

1. Introduction Mesoscale model forecasts provide remarkable detail and realism in the resolved convective systems, as well as in the temperature and wind distributions. Less dependence on parameterizations of physical processes and more resolved physics lead to a representation of weather features that appear convincing, and their value has been reported in numerous studies ( Done et al. 2004 ; Kain et al. 2006 , 2008 ; Weisman et al. 2008 ; Schwartz et al. 2009 ). The resolution of

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David R. Novak and Brian A. Colle

1. Introduction A major challenge of cool-season quantitative precipitation forecasting (QPF) is to determine the spatial and temporal variability of precipitation within extratropical cyclones ( Ralph et al. 2005 ). Variability in the location and intensity of cool-season precipitation is often determined by the development and evolution of mesoscale precipitation bands ( Ralph et al. 2005 ). Thus, improving mesoscale band forecasts will help improve cool-season QPF. Mesoscale precipitation

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F. Anthony Eckel and Clifford F. Mass

1. Introduction Operational use of mesoscale, short-range (0–48 h) ensemble forecasting (SREF) has lagged far behind the successful implementation of medium-range (2–10 day) ensemble forecasting systems at the National Centers for Environmental Prediction (NCEP) and the European Centre for Medium-Range Weather Forecasting (ECMWF; Toth and Kalnay 1993 ; Tracton and Kalnay 1993 ; Molteni et al. 1996 ). While research into mesoscale SREF application has been generally positive ( Du et al. 1997

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