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Niels Bormann and Crispin J. Marks

1. Introduction New Zealand’s extreme orography is not well resolved in today’s generation of global models but has a profound effect on the country’s weather ( Wratt et al. 1996 ). Consequently there is a natural tendency to investigate the value that can be added to the forecasting process by operational runs of mesoscale models forced by analyses and forecasts from global numerical weather prediction (NWP) centers. These mesoscale runs are expected to provide improved guidance

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Caren Marzban and Scott Sandgathe

acknowledge Mike Baldwin, Barbara Brown, Chris Davis, and Randy Bullock for contributing to all levels of this project. Partial support for this project was provided by the Weather Research and Forecasting Model Developmental Testbed Center (WRF/DTC), and by the National Science Foundation Grant 0513871. REFERENCES Baldwin , M. E. , S. Lakshmivarahan , and J. S. Kain , 2001 : Verification of mesoscale features in NWP models. Preprints, Ninth Conf. on Mesoscale Processes, Fort Lauderdale, FL

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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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Alan E. Lipton and George D. Modica

1. Introduction Clouds and their effect on the surface energy budget are deservedly the topic of much discussion and research in numerical weather prediction. One common motivation for addressing this subject is a desire to improve a prediction model’s capability to simulate the evolution of important mesoscale patterns. The U.S. Air Force (USAF) is among those with an interest in improving this capability, particularly with regard to short-term forecasts of clouds and

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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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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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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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