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Stacey M. Hitchcock, Michael C. Coniglio, and Kent H. Knopfmeier

and placement of subsequent convective development. However, in order to consistently resolve important mesoscale features explicitly, NWP models and observational networks likely need even higher spatial and temporal resolution than what is currently available operationally ( Sobash and Stensrud 2015 ). Therefore, it is important to continue to explore ways to improve the depiction of the mesoscale environment in model initial conditions, even for short-term forecasts ( Stensrud et al. 2009

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Masaru Kunii, Kosuke Ito, and Akiyoshi Wada

finite number of ensemble members. Moreover, in EnKF it is not necessary to use tangent-linear and adjoint versions of forecast models. After promising results in the application of EnKF to global numerical weather prediction (NWP) systems, the technique has also been applied to a regional NWP model (e.g., Snyder and Zhang 2003 ; Dowell et al. 2004 ; Bonavita et al. 2008 ; Miyoshi and Kunii 2012 ; Kunii and Miyoshi 2012 ; Kunii 2014a ). For its application to a limited-area mesoscale model such

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Nathan Snook, Ming Xue, and Youngsun Jung

been shown to produce superior probabilistic predictions compared to ensembles initialized using traditional perturbation methods ( Houtekamer et al. 2005 ; Hamill and Whitaker 2011 ). EnKF analyses have been successfully applied to ensemble forecasts of convective systems, including supercell thunderstorms (e.g., Aksoy et al. 2009 , 2010 ; Dawson et al. 2012 ) and mesoscale convective systems (e.g., Snook et al. 2012 , hereafter SXJ12 ; Putnam et al. 2014 ), as well as tropical cyclones (e

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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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Anastasios Papadopoulos, Themis G. Chronis, and Emmanouil N. Anagnostou

than CTRL on the order of 5%–7%. This suggests that the model can provide improved quantitative forecasting results even up to 12 h after the lightning assimilation, with an expected better performance in the first 6 h. 6. Conclusions A technique for improving convective precipitation forecasts through assimilation of regional lightning data in a mesoscale model was developed. Data from the National Observatory of Athens long-range lightning network (ZEUS), which primarily covers Europe, and the

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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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Kelly Lombardo, Eric Sinsky, Yan Jia, Michael M. Whitney, and James Edson

.g., Reynolds-Fleming and Luettich 2004 ; Hunter et al. 2007 ; Orton et al. 2010 ). Therefore, predicting the likelihood of development as well as the associated characteristics (timing of passage, inland penetration distance, temperature variations) is beneficial to a variety of communities, including those with interests in wind energy (e.g., Steele et al. 2015 ) and coastal fog forecasts (e.g., Tang 2012 ). Sea breezes are shallow mesoscale phenomena, with a depth on the order of 1 km (i.e., Atkins

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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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