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Heather Dawn Reeves, Kimberly L. Elmore, Geoffrey S. Manikin, and David J. Stensrud

1. Introduction Valley cold pools (VCPs), which are shallow layers of cold air trapped in a valley or basin ( Whiteman et al. 2001 ), are common in the western United States during winter. Numerical forecasts of basic variables, such as temperature and humidity, are known to be problematic during VCPs, which makes for difficulty in anticipating the various forms of hazardous weather, such as fog or freezing rain that can occur. In this study, the North American Mesoscale Model (NAM) forecasts

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Michael C. Coniglio, Harold E. Brooks, Steven J. Weiss, and Stephen F. Corfidi

1. Introduction Forecasting the details of mesoscale convective systems (MCSs; Zipser 1982 ) continues to be a difficult problem. Recent advances in numerical weather prediction models and computing power have allowed for explicit real-time prediction of MCSs over the past few years ( Done et al. 2004 ; Kain et al. 2005 ). While these forecasts are promising, their utility and how to best use their capabilities in support of operations is unclear ( Kain et al. 2005 ). Therefore, refining our

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Matthew S. Jones, Brian A. Colle, and Jeffrey S. Tongue

–NCAR) Mesoscale Model (MM5) members tended to cluster over the northeast United States for 2-m surface temperature; however, they used the Medium-Range Forecast model (MRF; Hong and Pan 1996 ) and Blackadar ( Zhang and Anthes 1982 ) boundary layer parameterizations, which share nearly identical surface flux representations. It is hypothesized that if a more diverse subset of model physical parameterizations can be identified, a more useful ensemble probability density function can be produced even using the

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Yimin Ma, Xinmei Huang, Graham A. Mills, and Kevin Parkyn

observations and analyses and relying on the expertise of experienced meteorologists. Results have been reported by Van Zetten et al. (2001) , Morgan (2002) , and Bureau of Meteorology (2008) . In the remainder of this paper we will refer to this method as subjective verification. While very effective, it is a time-consuming task. An increasingly important form of guidance used by forecasters in preparing their wind change forecasts are the mesoscale NWP forecasts from the finest-mesh (approximately 5

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Luke E. Madaus and Clifford F. Mass

prediction is ultimately limited by a lack of high-density observations ( Mass et al. 2002 ; Roebber et al. 2002 , 2004 ). Current observation networks, particularly surface and radiosonde networks, were primarily designed for synoptic-scale forecasting and are ill-suited to constraining short-term, convective-scale forecasts ( Sun et al. 2014 ). Recent research has found that increased surface observation density can improve mesoscale forecast skill. For example, studies have shown that assimilating

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Tadashi Fujita, David J. Stensrud, and David C. Dowell

indicated. In this approach, the convective heating profiles and precipitation rates are compatible with the local environment instead of the observed precipitation rates. Results indicate that this approach also yields improved precipitation forecasts and better forecasts of the mesoscale environment. There also have been attempts to directly assimilate precipitation data by four-dimensional variational assimilation techniques ( Zupanski and Mesinger 1995 ; Zou and Kuo 1996 ; Guo et al. 2000

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Casey E. Letkewicz and Matthew D. Parker

1. Introduction Mesoscale convective systems (MCSs) are well known for their potential hazards, including flooding, severe winds, hail, and even tornadoes ( Maddox 1983 ; Johns and Hirt 1987 ; Houze et al. 1990 ; Doswell et al. 1996 ; Fritsch and Forbes 2001 ). To better anticipate and forecast these impacts, past studies have emphasized understanding the most fundamental dynamics of these systems, often neglecting other higher-order complications such as environmental heterogeneity or

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Yulia R. Gel

1. Introduction Regional operational numerical weather prediction (NWP) systems such as the Weather Research and Forecasting (WRF) Model and the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) provide forecasters and local weather consumers with high-resolution gridded mesoscale forecasts on a regular basis. However, various substantial systematic errors, or biases, which are present in all numerical weather prediction systems

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Adam J. Clark, William A. Gallus Jr., Ming Xue, and Fanyou Kong

Fritsch 1993 ), 2) Betts–Miller–Janjić (BMJ; Betts 1986 ; Betts and Miller 1986 ; Janjić 1994 ), and 3) Grell–Devenyi (GD; Grell and Devenyi 2002 ). ENS20 and ENS20 phys ensemble member specifications are provided in Tables 3 and 4 , respectively. For the SSEF control member, the 2100 UTC analyses from NCEP’s operational North American Mesoscale (NAM; Janjić 2003 ) model (at 12-km grid spacing) were used for the ICs and the 1800 UTC NAM 12-km forecasts were used for the LBCs. For the members

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Melissa A. Nigro, John J. Cassano, and Mark W. Seefeldt

model were necessary in order to accurately predict some of the other atmospheric variables, such as wind speed. Similarly, Bromwich et al. (2005) analyzed the performance of the polar-modified version of the MM5 model used in the Antarctic Mesoscale Prediction System (AMPS). The analysis looked at the spatial variability, seasonal variability, and forecast hour variability associated with the model performance. The study found that the surface temperature predictions are most accurate during the

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