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G. David Alexander and William R. Cotton

: Opposing mesoscale circulations, a case study. Wea. Forecasting, 3, 189–204. Tao, W.-K., J. Simpson, C.-H. Sui, B. Ferrier, S. Lang, J. Scala, M.-D. Chou, and K. Pickering, 1993: Heating, moisture, and water budgets of tropical and midlatitude squall lines: Comparisons and sensitivity to longwave radiation. J. Atmos. Sci., 50, 673–690. Tremback, C. J., and R. Kessler, 1985: A surface temperature and moisture parameterization for use in mesoscale numerical models. Preprints, Seventh Conf

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Song-Lak Kang and Kenneth J. Davis

1. Introduction Numerous mesoscale modeling studies have suggested that land surface heterogeneity is quite significant in weather and climate forecasting. Weaver and Avissar (2001) for example, using a mesoscale atmospheric model, reported that specification of land surface heterogeneity was essential for accurate simulation of cloud development over the southern Great Plains of the United States. Ramos da Silva and Avissar (2006) , also using a mesoscale model, documented that

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Rebecca M. Cintineo and David J. Stensrud

include the growth in error to where the forecast no longer provides predictive utility to forecasters. The same is true for the storm scale, and the present study seeks to provide a more practical approach to predictability based upon specific storm features. This approach is similar to that of previous studies that investigated the predictability of mesoscale convective systems (MCSs) ( Stensrud and Wicker 2004 ; Wandishin et al. 2008 , 2010 ). The predictability investigated here is meant to

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Xingqin Fang and Ying-Hwa Kuo

circulation models (e.g., Koshyk and Hamilton 2001 ; Hamilton et al. 2008 ; Augier and Lindborg 2013 ) and regional mesoscale numerical weather prediction models (e.g., Skamarock 2004 ; Ricard et al. 2013 ), other global models, such as the ECMWF’s weather prediction model Integrated Forecast System (IFS), produce mesoscale spectrum significantly steeper than that analyzed from the observations, even at relatively high resolution (e.g., Shutts 2005 ; Burgess et al. 2013 ; Augier and Lindborg 2013

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Tiffany A. Shaw and Theodore G. Shepherd

sign; that is, [ECHAM3 atmospheric GCM (see ); Boville and Bretherton 2003 ; the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (see , section 3.6)], where the tendencies in (1) are understood to be those arising from subgrid-scale parameterizations. This assumption of local conservation is evidently not appropriate

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Wansuo Duan and Zhenhua Huo

baroclinic unstable modes in the baroclinically unstable regions in realistic numerical weather forecast models, which indicates the usefulness of the initial perturbations of particular structures in ensemble forecasts. Therefore, to demonstrate the superiority of CNOPs in ensemble forecasting compared to RPs, realistic weather models, such as the Fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5; Dudhia 1993 ) or Weather Research and

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Michael L. Waite and Chris Snyder

. Davis , and M. Weisman , 2004 : The next generation of NWP: Explicit forecasts of convection using the Weather Research and Forecasting (WRF) model . Atmos. Sci. Lett. , 5 , 110 – 117 . Gage , K. S. , 1979 : Evidence for a k −5/3 law inertial range in mesoscale two-dimensional turbulence . J. Atmos. Sci. , 36 , 1950 – 1954 . Gkioulekas , E. , and K. K. Tung , 2005a : On the double cascades of energy and enstrophy in two dimensional turbulence. Part 1. Theoretical formulation

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Antti Arola

-Range Weather Forecasts model is at about 30 m. Therefore the reference level used to calculate the fluxes in all of the surface flux parameterizations, which were considered in our study, was about 26 m, which is the third lowest sigma level of the mesoscale model. The first method to be applied to extend the basic mosaic method, is modified from an approach proposed by Vihma (1995) . It uses stability-corrected logarithmic profiles to calculate local wind speed, air temperature, and specific humidity

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Jun-Ichi Yano, Mitchell W. Moncrieff, and Xiaoqing Wu

1. Introduction Precipitating convection is known to be organized into various kinds of cloud systems on scales ranging from a few kilometers to more than a thousand kilometers, which is the entire range of mesoscale motion. Their ubiquity and structural diversity are amply illustrated in visible and infrared satellite images. One prominent kind of organization is the mesoscale convective system, also known as a tropical cloud cluster, which is an ensemble of cumulonimbus consisting of two

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Rupa Kamineni, T. N. Krishnamurti, S. Pattnaik, Edward V. Browell, Syed Ismail, and Richard A. Ferrare

measurements during CAMEX, but in the future, this capability could be on several aircraft and eventually in space. In the meantime, global humidity profiling will be provided by the Aqua satellite; this points to a natural extension of the present work to examine the impact of moisture profiling datasets on global weather forecasts. A major thrust on mesoscale high-resolution modeling of hurricanes has emerged in recent years; see, for example, Kurihara et al. (1995 , 1998 ), Cocke (1998) , Zhang

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