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Jonathan E. Thielen and William A. Gallus Jr.

1. Introduction Mesoscale convective systems (MCSs) play a crucial role climatologically in precipitation across the central United States. These systems account for roughly 30%–70% of the precipitation that occurs during the April–September period (warm season) in this region ( Ashley et al. 2003 ) and are therefore key phenomena of interest when seeking to improve the quantitative precipitation forecast (QPF) skill of models ( Fritsch et al. 1986 ). While this rainfall is essential to

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David Siuta, Gregory West, and Roland Stull

large errors in the estimated hub-height wind. NWP models, such as the Weather Research and Forecasting (WRF) Model ( Skamarock et al. 2008 ), have taken the forefront in wind speed forecast research and can be used to directly forecast winds at hub height to avoid vertical interpolation. WRF has two dynamical cores: the Nonhydrostatic Mesoscale Model (NMM) and the Advanced Research version of WRF (ARW). The NMM core is used by the National Centers for Environmental Prediction (NCEP) North American

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Jian-Jian Wang, Hann-Ming Henry Juang, Kevin Kodama, Steve Businger, Yi-Leng Chen, and James Partain

forecast guidance than the AVN for this heavy rainfall event on Kauai. The RSM performed reasonably well in predicting the mesoscale environmental conditions and occurrence of heavy rainfall in the vicinity of Kauai. b. Cyclogenesis event of 2–4 March 1996 During the period of 2–4 March 1996, a wave developed on a stationary front northwest of Hawaii and moved northeast at 7 m s −1 , producing heavy rains and high winds across most of the island chain. Although heavy rains affected the entire state

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A. Philip, T. Bergot, Y. Bouteloup, and F. Bouyssel

fog forecasting with 3D mesoscale models. Cuxart and Jiménez (2012) showed that, during a radiation fog event, local circulations were induced by topography that impacted the top of the thermal inversion. These motions created wind shear at the top of the fog layer and participated in the vertical development of the fog. Bari et al. (2015) studied the fog evolution at different locations over a coastal region and pointed out that advection impacted the fog life cycle. In their study, a

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Israel L. Jirak and William R. Cotton

1. Introduction The comments from Bunkers (2009 , hereafter B09 ) regarding the mesoscale convective system (MCS) index proposed in Jirak and Cotton (2007 , hereafter JC07 ) are appreciated. The MCS index was developed from the North American Regional Reanalysis (NARR) dataset with the intention that it would be continually improved to optimally work in the operational setting. Therefore, the feedback in B09 generated from testing the MCS index at the Weather Forecast Office in Rapid City

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Matthew J. Bunkers

1. Introduction Jirak and Cotton (2007 , hereafter JC07 ) proposed a new index to assist in forecasting the development of mesoscale convective systems (MCSs). This “MCS index” is the summation of three components that are a function of the 1) “best” lifted index (LI), 2) 0–3-km shear vector magnitude (SVM), and 3) 700-hPa temperature advection (TAdv). JC07’s study also reemphasized important aspects of MCS development, namely, the importance of the low-level jet (e.g., Junker et al. 1999

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John S. Kain, Ming Xue, Michael C. Coniglio, Steven J. Weiss, Fanyou Kong, Tara L. Jensen, Barbara G. Brown, Jidong Gao, Keith Brewster, Kevin W. Thomas, Yunheng Wang, Craig S. Schwartz, and Jason J. Levit

ensemble configuration and performance. The configurations of these two members are highlighted in Table 1 and the forecast domain is shown in Fig. 1 . For both years, background fields were generated by interpolating the National Centers for Environmental Prediction (NCEP) North American Mesoscale (NAM; Rogers et al. 2009 ) model 0000 UTC analysis (native 12-km grid) to the 4-km high-resolution grid. One of these members (hereafter C0) used the background fields directly as the initial conditions

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David Ahijevych, James O. Pinto, John K. Williams, and Matthias Steiner

1. Introduction Because of their large size, intensity, and longevity, mesoscale convective systems (MCSs) impact society in many ways: public safety (flash flooding); wind farm energy generation, above ground transmission of electricity, and cellular communication towers (severe wind events); agricultural practices (water usage); and safe and efficient air travel (turbulence, wind shear, hail). Better forecasts of MCSs will lead to more advanced public warning of severe weather ( Stensrud et

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Michael Colbert, David J. Stensrud, Paul M. Markowski, and Yvette P. Richardson

1. Introduction Operational convection-allowing models (CAMs) have become instrumental tools in forecasting convective and mesoscale weather events, including deep, moist convection ( Kain et al. 2006 , 2013 ; Benjamin et al. 2011 ; Snively and Gallus 2014 ). CAM forecasts have been shown to provide useful guidance on the mode of convection ( Done et al. 2004 ; Weisman et al. 2008 ; Kain et al. 2008 ), improved forecasts of rainfall amounts ( Lean et al. 2008 ; Roberts and Lean 2008

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William R. Burrows

SEPTEMBER 1991 WILLIAM R. BURROWS 357Objective Guidance for 0-24-Hour and 24-48-Hour Mesoscale Forecasts of Lake-Effect Snow Using CART WILLIAM R. BURROWSMeteorological Services Research Branch, Atmospheric Environment Service, Downsview, Ontario, Canada(Manuscript received 19 September 1990, in final form 3 May 1991) ABSTRACT Lake

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