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Aaron J. Hill, Christopher C. Weiss, and Brian C. Ancell

predictability in forecasting convection because of such initial condition sensitivities. Previously, Zhang et al. (2003) illustrated that moist processes (e.g., through convective and microphysical parameterizations) create a limitation to mesoscale predictability, enhancing the Melhauser and Zhang (2012) findings. Furthermore, Martin and Xue (2006) utilized a large ensemble to carry out perturbation experiments on water vapor mixing ratio, soil moisture, and meridional wind near the surface to

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T. N. Krishnamurti, S. Pattnaik, and D. V. Bhaskar Rao

experience gained in physical initialization with large-scale models ( Krishnamurti et al. 1991 , 1993 , 2001 ), it is possible to formulate a simplified version of rain-rate initialization for mesoscale models. That is the goal of this paper. We hope to see the extent to which the observed estimates of rain rate from satellites can be incorporated within a mesoscale model. We also wish to ask how far into the future we can demonstrate a positive impact on the forecast skills. In our study we

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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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Edward R. Mansell, Conrad L. Ziegler, and Donald R. MacGorman

1. Introduction Recent studies have shown that forecasts can be improved by incorporating the effects of deep convection during the initialization period of mesoscale forecast models. For example, based on model experiments that used subjective analyses to improve initial conditions, Stensrud and Fritsch (1994a) suggested that forecast skill could be enhanced by using data assimilation procedures that include “the effects of parameterized convection, as indicated by radar or satellite during

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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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Luke E. Madaus, Gregory J. Hakim, and Clifford F. Mass

1. Introduction Short-term numerical weather forecasts continue to suffer from poor definition and prediction of mesoscale weather features (e.g., Roebber et al. 2004 ). This is particularly true for small-scale, but potentially high-impact features such as the timing and structure of frontal passages ( Colle et al. 2001 ) or convective development and evolution ( Melhauser and Zhang 2012 ; Hanley et al. 2013 ). Not only can these features present significant hazards to public safety, but

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