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Michael M. Bell and Michael T. Montgomery

conjunction with NASA Genesis and Rapid Intensification Processes (GRIP) and National Oceanic and Atmospheric Administration (NOAA) Intensity Forecast Experiment (IFEX) campaigns. Composite analyses of developing and nondeveloping disturbances (“developers” and “nondevelopers”) using the PREDICT dataset have revealed distinctions between favorable and unfavorable thermodynamic environments for genesis. Montgomery and Smith (2012) , Davis and Ahijevych (2013) , and Komaromi (2013) showed that

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F. J. Robinson, S. C. Sherwood, D. Gerstle, C. Liu, and D. J. Kirshbaum

associated with unobservable details or stochastic behavior and reveal model errors that would not be detectable in a single event. Second, contrasting results in different forcing situations can reveal aspects that are more important to the interaction of convection with larger scales. Several previous studies have followed this strategy. J. Wu et al. (2009) found that the Weather Research and Forecasting (WRF) CRM was able to simulate qualitatively the significant differences in the character of

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Mankin Mak, Yi Lu, and Yi Deng

recent years, there have been a number of investigations of fronts using 3D primitive equation models ( Rotunno et al. 1994 ; Zhang 2004 ) and nonhydrostatic models ( Plougonven and Snyder 2007 ; Wei and Zhang 2014 ) of increasingly fine resolution. The more recent investigations primarily focus on the generation of mesoscale gravity wave modes in a jet–front system. The sensitivity studies suggest that it would require grid resolution of 25 km or less to adequately resolve the properties of such

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Peter V. Hobbs, Clifford F. Mass, and Mark T. Stoelinga

storms on the Washington coast (the CYCLES Project), which led to a classification of rainbands in extratropical cyclones and detailed information on cloud structures and precipitation processes in the various types of rainbands. Within the past decade, Mass and colleagues have used observations and model simulations from the COAST Project, as well as daily real-time mesoscale model forecasts, to study fronts and precipitation in the Pacific Northwest, including the effects of orography. Problems

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M. E. B. Gray

tropical cloud clusters. Part III: Effects of mesoscale convective organization. J. Atmos. Sci., 46, 1566–1588. Cotton, W. R., H. Jiang, S. C. R. Rafkin, G. D. Alexander, and R. L. McAnelly, 1996: Parametrization of cumulus and MCSs in GCMs to mesoscale models. Proc. ECMWF Workshop on New Insights and Approaches to Convective Parametrization, Reading, United Kingdom, European Centre for Medium-Range Weather Forecasts, 288–305. Donner, L. J., 1993: A cumulus parameterization including mass

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Mitchell W. Moncrieff and Changhai Liu

consistent with fast eastward propagation (about 17 m s −1 ). A similar three-branch morphology occurs in simulations by Trier et al. (2006) for the same period using the Weather Research and Forecasting (WRF) model and in many simulations cited in the introduction to this paper. The simulated propagation speed compares to the Carbone et al. (2002) radar estimates. It has long been known that fast-moving systems with deep mesoscale downdrafts occur widely over the United States ( Houze et al. 1989

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Malakondayya Challa, Richard L. Pfeffer, Qiang Zhao, and Simon W. Chang

work done by the symmetric flow across the isobars. In our later investigations we used the 3D Naval Research Laboratory (NRL) mesoscale model developed by Madala and Chang (1979) and Madala et al. (1987) , which is a version of the Navy Operational Regional Atmospheric Prediction System (NORAPS). As in the earlier simulations with Sundquist’s model, hurricanes developed from the initial conditions corresponding to the developing disturbances and did not develop from those corresponding to the

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David M. Schultz

within this comment. First, operational forecasters in the U.S. National Weather Service sometimes discuss the importance of midlatitude cumulus congestus clouds leading to moistening of dry air above the planetary boundary layer and the eventual development of deep moist convection. The argument posed by Hohenegger and Stevens (2013) is for the tropics. The question remains how applicable their ideas are for the midlatitudes. Second, Hohenegger and Stevens (2013) use the term “moisture

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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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