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Joao Gari da Silva Fonseca Jr., Fumichika Uno, Hideaki Ohtake, Takashi Oozeki, and Kazuhiko Ogimoto

WRF focused on solar resources (WRF-Solar) has been developed ( Jiménez et al. 2016a , b ), and it is already being updated ( Golnas 2018 ). Another example comes from the JMA, which in February 2017 replaced its original mesoscale model called NHM ( Saito et al. 2007 ) with a new model called a system based on a unified concept for atmosphere (ASUCA; Japan Meteorological Agency 2014 ). This new model is expected to increase the accuracy of low-level cloud forecasts by implementing horizontal

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Alexandre O. Fierro, Jidong Gao, Conrad L. Ziegler, Edward R. Mansell, Donald R. MacGorman, and Scott R. Dembek

1. Introduction It is a challenging problem to numerically forecast isolated convective storms owing to the need to accurately treat complex physical interactions between dynamical and microphysical processes over a large range of scales (e.g., Stensrud et al. 2009 ). Mesoscale convective systems (MCSs), which consist of a grouping of isolated convective storm cells and often associate with a broad area of nonconvective or stratiform precipitation (e.g., Houze 1993 ; Cotton 1999 ), are also

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Paul E. Bieringer, Peter S. Ray, and Andrew J. Annunzio

models. This was a result of the wide operational use of these lower-resolution models and the demanding computational requirements associated with high-resolution modeling. In the early 1990s, Vukićević and Errico (1990) Vukićević (1991) , Errico and Vukićević (1992) , and Errico et al. (1993) compared the relative influence of localized initial and lateral boundary condition error on limited-area hydrostatic mesoscale model forecasts. Vukićević and Errico illustrated that the primary

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David J. Stensrud and J. Michael Fritsch

2084 MONTHLY WEATHER REVIEW VOLUME 122Mesoscale Convective Systems in Weakly Forced Large-Scale Environments. Part Numerical Simulations and Implications for Operational Forecasting DAVID J. STENSRUDNOAA/ERL/National Severe Storms Laboratory, Norman, Oklahoma J. MICHAEL FRITSCHDepartment of Meteorology, The Pennsylvania State University, University Park

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Thomas Nehrkorn, John Henderson, Mark Leidner, Marikate Mountain, Janusz Eluszkiewicz, Kathryn McKain, and Steven Wofsy

urban emissions over a range of time scales (from days to years). The Weather Research and Forecasting (WRF; Skamarock and Klemp 2008 ) model was combined with the Stochastic Time-Inverted Lagrangian Transport particle dispersion model (WRF-STILT; Lin et al. 2003 ; Nehrkorn et al. 2010 ) and an anthropogenic emission inventory (the Vulcan Project; Gurney et al. 2009 ) to model CO 2 concentrations, which were then compared with in situ measurements. Computations using this modeling system were

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Thomas J. Greenwald, Rolf Hertenstein, and Tomislava Vukićević

physical cloud parameterizations for regional-scale forecast models. However, it is the ability to explicitly predict clouds at mesoscales that is the minimum requirement for fully exploiting the information content and mesoscale variability that satellite radiance data provide. Also contributing to this lack of progress to a certain extent has been the shortage of fast radiative transfer models for cloudy atmospheres. While many types of models do exist, the vast majority of them are impractical for

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David H. Bromwich, Andrew J. Monaghan, Kevin W. Manning, and Jordan G. Powers

program. Bromwich et al. (2003) examined the performance of the AMPS 10-km domain during a combined synoptic/mesoscale event in the western Ross Sea in January 2001. They found that the model reproduced the evolution of upper-level conditions in agreement with observations and resolved near-surface mesoscale features such as topographically induced high/low vortices. They also noted that the accuracy of the forecasts was strongly dependent on the quality of the initial conditions despite the

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Fuqing Zhang

significantly to the real-time forecast difficulty. More recently, Zhang et al. (2003 , hereafter ZSR03 ) demonstrated that the mesoscale predictability can be seriously limited by strong upscale growth of small-scale small-amplitude initial error in the presence of moist convection, much as foreseen by Lorenz (1969) . Consistent with this result, Buizza and Chessa (2002) pointed out the significance of including stochastic perturbations in the global ensemble prediction system at the European Centre

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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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Lei Zhang and Zhaoxia Pu

. , and J. Sun , 2002 : Assimilating radar, surface and profiler data for the Sydney 2000 forecast demonstration project . J. Atmos. Oceanic Technol. , 19 , 888 – 898 . Dudhia , J. , 1989 : Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model . J. Atmos. Sci. , 46 , 3077 – 3107 . Fritsch , J. M. , and R. E. Carbone , 2004 : Improving quantitative precipitation forecasts in the warm season: A USWRP research and

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