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Edward I. Tollerud, Fernando Caracena, Steven E. Koch, Brian D. Jamison, R. Michael Hardesty, Brandi J. McCarty, Christoph Kiemle, Randall S. Collander, Diana L. Bartels, Steven Albers, Brent Shaw, Daniel L. Birkenheuer, and W. Alan Brewer

dropsonde measurements of wind, pressure, temperature, and moisture) were included in some parallel runs via a modified telescoping Barnes scheme. The vertical resolution of the LAPS analyses was 25 hPa. During the field experiment, the NOAA Forecast Systems Laboratory (now the Global Systems Division of the ESRL) provided real-time mesoscale numerical model guidance to the IHOP_2002 operations center from multiple advanced modeling systems with the goal of assessing their performance in a quasi

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Margaret A. LeMone, Fei Chen, Mukul Tewari, Jimy Dudhia, Bart Geerts, Qun Miao, Richard L. Coulter, and Robert L. Grossman

numerical simulations use the Advanced Research Weather Research and Forecasting (ARW-WRF) model ( Skamarock et al. 2005 ), coupled to the Noah land surface model (LSM), which was initialized using the National Center for Atmospheric Research (NCAR) High-Resolution Land Data Assimilation System (HRLDAS; Chen et al. 2007 ). The data were collected in southeast Kansas using aircraft, surface flux towers, and three radar wind profilers, during May–June, 2002, as part of the International H 2 O Project

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F. Couvreux, F. Guichard, P. H. Austin, and F. Chen

1. Introduction Water vapor variability was the main focus of the International H 2 O Project (IHOP_2002), which took place in May–June 2002 over the southern Great Plains of the United States ( Weckwerth et al. 2004 ). This field project gathered together most of the techniques for measuring water vapor. We address water vapor variability at the mesoscale (scales larger than thermals, ranging from tens to a few hundreds of kilometers). Comparatively few investigations have considered this

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John R. Mecikalski, Kristopher M. Bedka, Simon J. Paech, and Leslie A. Litten

, University of Alabama in Huntsville, Huntsville, AL, 92 pp . Johnson , D. B. , P. Flament , and R. L. Bernstein , 1994 : High-resolution satellite imagery for mesoscale meteorological studies. Bull. Amer. Meteor. Soc. , 75 , 5 – 33 . Johnson , J. T. , P. L. MacKeen , A. Witt , E. D. Mitchell , G. J. Stumpf , M. D. Eilts , and K. W. Thomas , 1998 : The storm cell identification and tracking algorithm: An enhanced WSR-88D algorithm. Wea. Forecasting , 13 , 263 – 276

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Robin L. Tanamachi, Wayne F. Feltz, and Ming Xue

mesoscale networks, on the 9-km grid. The analysis was performed for 0600 UTC using the 6-h forecast from the 0000 UTC cycle of the operational NCEP Eta Model as the background. The special data used include surface observations from the Oklahoma, southwest Kansas, and west Texas Mesonets, the Atmospheric Radiation Measurement (ARM) Program Surface Meteorological Observation System (SMOS) data, Big Bend (Kansas) groundwater Management District Number 5 soil and surface observations, and aircraft

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Roger M. Wakimoto and Hanne V. Murphey

– 1548 . Schaefer , J. T. , 1974 : The lifecycle of the dryline. J. Appl. Meteor. , 13 , 444 – 449 . Schaefer , J. T. , 1986 : The dryline. Mesoscale Meteorology and Forecasting, P. S. Ray, Ed., Amer. Meteor. Soc., 549–572 . Schultz , D. M. , C. C

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S. B. Trier, F. Chen, K. W. Manning, M. A. LeMone, and C. A. Davis

transfer for inhomogeneous atmosphere: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res. , 102 , D14 . 16663 – 16682 . Mueller , C. K. , J. W. Wilson , and N. A. Crook , 1993 : The utility of sounding and mesonet data to nowcast thunderstorm initiation. Wea. Forecasting , 8 , 132 – 146 . Ogura , Y. , and Y. L. Chen , 1977 : Life history of an intense mesoscale convective storm in Oklahoma. J. Atmos. Sci. , 34 , 1458 – 1476 . Paegle , J. , K. C. Mo

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John H. Marsham, Stanley B. Trier, Tammy M. Weckwerth, and James W. Wilson

1. Introduction Nocturnal convective storms in the United States are poorly forecast compared with convection during the day ( Davis et al. 2003 ). One aspect of nocturnal warm-season precipitation that contributes to the difficulty of its prediction is the greater occurrence of elevated convection at night (e.g., Wilson and Roberts 2006 ). Here, we refer to convection where the conditionally unstable source air is located above the boundary layer as “elevated” ( Glickman 2000

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Diane Strassberg, Margaret A. LeMone, Thomas T. Warner, and Joseph G. Alfieri

and Water, P. A. Matson and R. C. Harriss, Eds., Blackwell Science, 126–163 . Lenschow , D. H. , J. C. Wyngaard , and W. T. Pennell , 1980 : Mean-field and second-moment budgets in a baroclinic, convective boundary layer. J. Atmos. Sci. , 37 , 1313 – 1326 . Liu , Y. , F. Chen , T. Warner , and J. Basara , 2006 : Verification of a mesoscale data-assimilation and forecasting system for the Oklahoma City area during the Joint Urban 2003 field project. J. Appl. Meteor

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Roger M. Wakimoto and Hanne V. Murphey

mesoscale forecasting of convection and its behavior. Mon. Wea. Rev. , 104 , 1474 – 1483 . Purdom , J. F. , 1982 : Subjective interpretation of geostationary satellite data for nowcasting. Nowcasting, K. Browning, Ed., Academic Press, 149–166 . Reed , R. J. , and M. D. Albright , 1997 : Frontal structure in the interior of an intense mature ocean cyclone. Wea. Forecasting , 12 , 866 – 876 . Richter , H. , and L. F. Bosart , 2002 : The suppression of deep moist convection near

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