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Dan Bikos, Daniel T. Lindsey, Jason Otkin, Justin Sieglaff, Louie Grasso, Chris Siewert, James Correia Jr., Michael Coniglio, Robert Rabin, John S. Kain, and Scott Dembek

of low-level water vapor convergence ( Chesters et al. 1983 ), providing potentially useful information on convective initiation locations before any clouds have formed. An example of this difference is provided in section 3 . 3. Examples Five examples are shown to highlight representative strengths and weaknesses of using synthetic imagery from the NSSL 4-km WRF-ARW for severe thunderstorm forecasting. The example in section 3d utilizes the CIMSS forward model, while the examples in the other

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Viel Ødegaard

resistance to vertical motion ( Fabry and Zawadzki 1995 ). a. Parameterization scheme 1) Condensation–precipitation parameterization The control condensation scheme treats condensation and precipitation, from large-scale (model resolved) and subgrid-scale convective processes. Convection is parameterized according to a modified version of the Kuo scheme, including modeling of large-scale instability processes. Rain is a diagnostic variable, while cloud liquid water and water vapor are prognostic

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William R. Ryerson and Joshua P. Hacker

these more complex visibility parameterizations are trained using observational predictors, they effectively improve the quality of the parameterization while remaining susceptible to error in the NWP predictions of the input parameters. Yet RH14 showed that error in the NWP predictions of parameters most germane for fog (i.e., q c , temperature, and water vapor) are the most important source of error, suggesting a limitation on the capability of parameterization strategies built from

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Thomas F. Lee, James R. Clark, and Steven D. Swadley

. To compute the SSM/I estimates of cloud liquidwater, the semiphysical algorithms developed by Pettyand Katsaros (1990) are used. Since estimates of cloudliquid water can be biased significantly by atmosphericwater vapor and surface wind stress, Petty and Katsarosincorporate a priori SSM/I statistical estimates of windspeed (Goodberlet et al. 1989) and integrated watervapor (their own algorithm) to compute integratedcloud liquid water. Retrieval of integrated vapor andespecially wind speed is

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Ramesh Vellore, Darko Koračin, Melanie Wetzel, Steven Chai, and Qing Wang

m (1500 m). The Eta-reanalyzed mean surface water vapor mixing ratio was 11.2 g kg −1 , whereas the observed value at the nearest buoy station (Tanner Banks buoy, indicated as TB in Fig. 1 ), located approximately 200 km east of the DYCOMS II target area, was 9.01 g kg −1 ; that is, the Eta fields placed more moisture in the DYCOMS II area by 25% compared with buoy observations. c. MM5 verification using aircraft and satellite data The forecast soundings of the baseline simulation (BL3) were

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Kenneth Holmlund

, India) (e.g., CGMS XXIII 1995 ). During the past few years the instrumentation on board these spacecraft has improved, particularly on GOES (Geostationary Operational Environmental Satellite) and GMS (Geostationary Meteorological Satellite), providing extended capabilities for the wind derivation process. Already vector fields are derived not only with images from the infrared window channels, but also from the visible and water vapor channels. This provides the capability to track not only cloud

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Melanie A. Wetzel, Steven K. Chai, Marcin J. Szumowski, William T. Thompson, Tracy Haack, Gabor Vali, and Robert Kelly

conditions, covering all possible image pixel viewing angles and cloud types. Cloud optical properties were determined from a narrowband parameterization based on Mie theory using the gamma size distribution. Gas absorption coefficients using the exponential sum fitting technique were derived for water vapor, carbon dioxide, ozone, and oxygen ( Meier et al. 1997 ). The satellite channel filter functions for GOES-10 were applied in the model calculations of band radiances. The following equation ( Han

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Pin-Fang Lin, Pao-Liang Chang, Ben Jong-Dao Jou, James W. Wilson, and Rita D. Roberts

-polarization radar measurements . Bull. Amer. Meteor. Soc. , 80 , 381 – 388 . Washburn, E. W. , 1924 : The vapor pressure of ice and of water below the freezing point . Mon. Wea. Rev. , 52 , 488 – 490 . Weckwerth, T. M. , 2000 : The effect of small-scale moisture variability on thunderstorm initiation . Mon. Wea. Rev. , 128 , 4017 – 4030 . Whipple, F. J. W. , 1927 : Formulae for the vapour pressure of ice and of water below 0°C . Mon. Wea. Rev. , 55 , 131 – 131 . Wilks, D. S. , 2006

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Fred J. Kopp and Harold D. Orville

framework is a deep-convection, twodimensional, time-dependent cloud, model that hasbeen applied to a variety of atmospheric situations. Adensity-weighted streamfunction has been used to extend the model to deep convection. Atmospheric wind,potential temperature, water vapor, cloud liquid, cloudice, rain, snow, and graupel/hail (in the form of icepellets, frozen rain, graupel, and small hail) are themain dependent variables. The nonlinear partial differential equations constituting the model include

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Andreas Ottenbacher, Maria Tomassini, Kenneth Holmlund, and Johannes Schmetz

, cloud motion winds (CMWs) from the tracking of IR channel images are operationally produced from U.S., Japanese, Indian, and European satellites. Algorithms used to derive wind speed and altitude from the radiance observations are well documented by, for example, Kelkar and Rao (1992) , Le Marshall et al. (1994) , Merrill et al. (1991 ), Nieman et al. (1993) , Schmetz et al. (1993) , and Uchida (1992) . The tracking of cloudy features in IR images is augmented by the use of the water vapor

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