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Baruch Ziv, Yoav Yair, Karin Presman, and Martin Füllekrug

our space observations were mesoscale convective systems (MCSs), including squall lines. Because these observations were to be predetermined on the basis of these forecasts, it was important to assess their accuracy. To this end we compared the location and top heights of the MCSs that were predicted for the area between 40°S and 40°N latitude with actual observed ones using satellite imagery. Section 2 describes the methodology used for the assessment, section 3 presents the results, and

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Claudia Roeger, Roland Stull, David McClung, Joshua Hacker, Xingxiu Deng, and Henryk Modzelewski

forecast agrees with the measurement ( Roeger et al. 2001 ). A forecast of good quality may also show skill, which is the degree of correctness above some reference baseline, such as a climatological average. Thus, by determining the accuracy and skill of a forecast, one can improve it and use it with confidence in the future. Although these theoretical ideas about weather forecast verification are well known, not many verification results are actually published or are easily accessible for mesoscale

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Philip A. Lutzak

temporally limited in coverage due to their source from 1- or 3-hourly model output with spatial grid limitations that are too large to represent a mesoscale undular bore event. Thus, it is only these data taken together with all recommended forecast parameters that provide a cumulative indication that such an event is likely to occur.

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John V. Cortinas Jr. and David J. Stensrud

716 WEATHER AND FORECASTING VOLUME 10The Importance of Understanding Mesosca~e lV~odel Parameterizafion Schemes for Weather Forecasting JOHN V. CORTINAS JR.NOYDl /National Severe Storms Laboratory and Cooperative Institute for Mesoscale Meteorological Studies, Norman, Oklahoma DAVID J. STENSRUDNOAA /National Severe Storms Laboratory

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Brian P. Reen, Robert E. Dumais Jr., and Jeffrey E. Passner

1. Introduction Observations are often used to enhance the initial conditions in a mesoscale model to improve the model forecast. However, in order to use observations made at discrete points to improve a three-dimensional simulation, one must determine how the influence of those observations should be spread spatially and temporally. In this study, we illustrate how the introduction of observations into the initial conditions can cause excessive drying and then demonstrate solutions to

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T. Cherubini, S. Businger, C. Velden, and R. Ogasawara

1. Introduction A lack of observational data over the surrounding ocean makes weather forecasting a special challenge in Hawaii. The central North Pacific region that encompasses the Hawaiian Islands is characterized by rapidly evolving mesoscale systems, which compound the forecast challenge. Forecast errors can frequently be traced to errors in initial conditions, particularly in dynamically active areas where observational data are scarce ( Klinker et al. 1998 ). Temperature and, to some

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Craig S. Schwartz

Range, but the observed rainfall far exceeded forecasters’ expectations. This underprediction may have been related to reliance on operational numerical weather prediction (NWP) model guidance with parameterized convection, including the Global Forecast System (GFS), North American Mesoscale Forecast System (NAM), and Rapid Refresh (RR) models, which also severely underpredicted the storm-total precipitation (discussed in section 3 ). However, numerous studies have demonstrated that high

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Shu-Chih Yang, Eugenia Kalnay, and Takemasa Miyoshi

period. However, during the spinup period, the choice of the initial conditions for the ensemble affects not only the accuracy of the analyses and forecasts but also the length of the spinup period ( Kalnay and Yang 2010 , hereafter KY10 ). All data assimilation methods suffer from spinup problems; however, the spinup problems tend to be more serious for EnKFs than for variational methods because both the mean and the ensemble perturbations require initial conditions. In mesoscale NWP, spinup occurs

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Kelly M. Mahoney and Gary M. Lackmann

Wales, Australia . Wea. Forecasting , 16 , 261 – 269 . Glickman , T. , Ed., 2000 : Glossary of Meteorology . 2nd ed. Amer. Meteor. Soc., 855 pp . Grim , J. A. , G. M. McFarquhar , R. M. Rauber , A. M. Smith , and B. F. Jewett , 2009 : Microphysical and thermodynamic structure and evolution of the trailing stratiform regions of mesoscale convective systems during BAMEX. Part II: Column model simulations . Mon. Wea. Rev. , 137 , 1186 – 1205 . Grubišić , V. , and M. W

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John M. Peters, Erik R. Nielsen, Matthew D. Parker, Stacey M. Hitchcock, and Russ S. Schumacher

featured an outer domain with a 15-km grid spacing, an inner domain with a 3-km grid spacing ( Fig. 8a ), a one-way feedback from the outer domain to the inner domain, and was run from 0000 UTC 24 June to 1200 UTC 25 June 2015 with lateral boundaries updated every hour. The third WRF simulation was configured with the North American Mesoscale Forecast System (NAM) analysis at 0000 UTC 24 June 2015 as ICs, and the subsequent 6-hourly NAM analyses as LBCs [this simulation is hereafter referred to as the

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