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Harold E. Brooks, Charles A. Doswell III, and Robert A. Maddox

120 WEATHER AND FORECASTING VOLUME?FORECASTER'S FORUMOn the Use of Mesoscale and Cloud-Scale Models in Operational ForecastingHAROLD E. BROOKS, CHARLES A. BOSWELL III, AND ROBERT A. MADDOXNOAA /National Severe Storms Laboratory, Norman, Oklahoma27 August 1991 and 8 November 1991 In the near future, the technological capability will be available to use mesoscale and cloud

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John S. Snook, Peter A. Stamus, James Edwards, Zaphiris Christidis, and John A. McGinley

LAPS within the OWSS was important. For the first time, a mesoscale forecast model initialized with comparably high-resolution analyses was implemented in an operational environment using technology representative of that planned for NWS forecast offices in the next several years. The LAPS forecasts were produced by the Regional Atmospheric Modeling System (RAMS) ( Pielke et al. 1992 ; Walko et al. 1995 ) developed at Colorado State University. The operational forecaster interactively selected

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Robert E. Hart, Gregory S. Forbes, and Richard H. Grumm

) and Mesoscale Eta Models (MESO; Black 1994 ), the National Center for Environmental Prediction’s (NCEP’s) most complex synoptic model and first mesoscale model, respectively, produce high-resolution output at 30–50 vertical levels and at every forecast hour in the form of soundings (or“profiles”). Therefore, the fine time and spatial resolutions of the model output make possible operational prediction of mesoscale features unlike what has been possible previously. The forecasting problem is how

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Shih-Yu Wang, Tsing-Chang Chen, and S. Elwynn Taylor

1996 ; Marchok et al. 2007 ), or African easterly waves ( Céron and Guérémy 1999 ) are essential to accurate QPFs over the specific regions. Examining numerical forecasts of MPs is important so that additional contributions to QPF errors for progressive MCSs can be identified. The purpose of this study is to evaluate the forecasts of MPs and their associated rainfall in the National Centers for Environmental Prediction’s (NCEP’s) operational North American Mesoscale (NAM) model. Evaluation of

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David J. Stensrud and Steven J. Weiss

1. Introduction Numerical weather prediction is one of the foundations upon which operational forecasters rely to produce the most accurate and timely weather forecasts. These numerical predictions have improved substantially over the last 30 years ( Kalnay et al. 1998 ), but the most interesting and threatening weather events occur on the mesoscale and are both spatially and temporally intermittent in nature. Owing to the lack of perfect observations, particularly on the smaller scales, and

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Bryan M. Burlingame, Clark Evans, and Paul J. Roebber

times of 24–48 h are possible, at least when verified on the meso- α to synoptic scales, and that convection-allowing forecasts are more skillful than convection-parameterizing forecasts. More recent studies that exclusively utilize convection-allowing forecast frameworks have refined our understanding of the scales at which CI forecasts have meaningful skill. Duda and Gallus (2013) examine the predictability of warm-season CI preceding mesoscale convective system formation in convection

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Ariel E. Cohen, Steven M. Cavallo, Michael C. Coniglio, and Harold E. Brooks

1. Introduction One substantial source of forecast inaccuracy in mesoscale models 1 is the representation of lower-tropospheric thermodynamic and kinematic structures ( Jankov et al. 2005 ; Stensrud 2007 ; Hacker 2010 ; Hu et al. 2010 ; Nielsen-Gammon et al. 2010 ). The accurate representation of these structures is critical in improving forecasts of high-impact weather phenomena for which model output can aid in the assessment of whether necessary conditions for such phenomena would be

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Ding Jincai, Charles A. Doswell III, Donald W. Burgess, Michael P. Foster, and Michael L. Branick

WEATHER AND FORECASTING VOLUME7Verification of Mesoscale Forecasts Made during MAP '88 and MAP '89 DING JINCAI,* CHARLES A. DOSWELL III, AND DONALD W. BURGESS~NOAA, Environmental Research Laboratories, National Severe Storms Laboratory, Norman, Oklahoma MICHAEL P. FOSTER AND MICHAEL L. B~mcINOAA, National Weather Service, Forec~t O.~ice, Norman, Oklahoma (Manuscript received 25 October 1991, in final form 5 May 1992) Two

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Clark Evans, Steven J. Weiss, Israel L. Jirak, Andrew R. Dean, and David S. Nevius

parameterizations, approximations of severity (e.g., Kain et al. 2010 ; Sobash et al. 2011 , 2016a , b ; Gallo et al. 2016 ; Adams-Selin and Ziegler 2016 ; Gagne et al. 2017 ). In this sense, CAMs provide additional explicit storm information that forecasters can use to refine the conceptual model for the event established by their assessment of the larger-scale and mesoscale environments. In addition to being able to crudely resolve thunderstorms, CAMs are also able to crudely resolve meso- γ - to meso

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Susanna Hagelin, Ludovic Auger, Pascal Brovelli, and Olivier Dupont

( Rasmussen et al. 2001 ); to fog and visibility ( Fabbian et al. 2007 ; Gultepe et al. 2006 ); and to more general systems ( Huang et al. 2012 ; Isaacs et al. 2014 ). The Applications of Research to Operations at Mesoscale (AROME) nowcasting system (AROME NWC; Auger et al. 2014, manuscript submitted to Quart. J. Roy. Meteor. Soc. ) is based on the operational mesoscale weather forecasting model of Météo-France ( Seity et al. 2011 ) and AROME Airport is developed using AROME NWC as a base. The purpose

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