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Syed Ismail, Richard A. Ferrare, Edward V. Browell, Gao Chen, Bruce Anderson, Susan A. Kooi, Anthony Notari, Carolyn F. Butler, Sharon Burton, Marta Fenn, Jason P. Dunion, Gerry Heymsfield, T. N. Krishnamurti, and Mrinal K. Biswas

turn can influence cloud microphysics, latent heat release, vertical transport and convection development, and precipitation. Fields of water vapor concentration are a key component for understanding processes of precipitation, evaporation, and latent heat release in cloud systems. The lack of adequate and accurate moisture measurements with sufficient vertical and horizontal resolutions limits the ability of most numerical models to represent these processes. Krishnamurti et al. (1994) found

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Andrew J. Heymsfield, Aaron Bansemer, Gerald Heymsfield, and Alexandre O. Fierro

convection. The amount of large ice reaching the −20°C level is key in understanding subsequent droplet depletion in the updrafts. The large ice depletes cloud droplets directly by accretion and indirectly by vapor diffusion onto the ice. The total water mixing ratio X T in a volume of air, normalized by the amount of water vapor flowing into cloud base X CB is given by and provides a relative measure of precipitation efficiency and indirectly a way to characterize the ability of small particles to

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Joël Arnault and Frank Roux

simulation are analyzed in section 3 and compared to Meteosat-9 images in the water vapor channel at 7.3 μ m. In section 4 , we show the limits of L55 ’s analysis when applied to the energetics of a disturbance in a finite domain, and we propose an alternate formulation for hydrostatic, compressible, and anelastic frameworks in a limited domain. In section 5 , a scale analysis applied to the simulated pre-Helene disturbance allows us to separate the most important terms in the proposed energy

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Zhaoxia Pu, Xuanli Li, and Juanzhen Sun

reflectivity data assimilation. This warm rain process includes the condensation of water vapor into cloud water, accretion of cloud water by rain, automatic conversion of cloud water to rain, and evaporation of rain to water vapor (for details, see Kessler 1969 ; Xiao et al. 2007 ). Then, according to Sun and Crook (1997) , when assuming the Marshal–Palmer distribution of drop size for rainwater and n 0 = 8 × 10 6 mm −4 , the radar reflectivity (in dB Z ) can be estimated from rainwater mixing ratio

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R. A. Hansell, S. C. Tsay, Q. Ji, N. C. Hsu, M. J. Jeong, S. H. Wang, J. S. Reid, K. N. Liou, and S. C. Ou

Liou 1992 ). The code also employs a parameterized version of the LW water vapor continuum model (CKD2.4) to account for strong water vapor absorption. Modifications to the code enable time series of retrieved AERI IR AOT (scaled to λ = 0.55 μ m) and combined AERIPLUS [a physical retrieval algorithm developed by the University of Wisconsin Space Science and Engineering Center (UW-SSEC), see Feltz et al. 2003 ), used to retrieve temperature/moisture profiles from AERI radiances] and radiosonde

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Wallace Hogsett and Da-Lin Zhang

potential energy (TPE) are derived based on the nonhydrostatic MM5 system ( Dudhia 1993 ). Table 1 summarizes the energetic quantities used in this study. Various energies per unit volume (J m −3 ) are defined as where V h is the horizontal velocity with respect to the ground, q υ is the water vapor mixing ratio, L υ is the latent heat of vaporization, c υ is the specific heat at constant volume, and the other variables assume their typical meteorological definition. The vertical component of

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Chanh Q. Kieu and Da-Lin Zhang

water vapor conservation equation, so that water vapor will still be advected into the storm in the same manner as that in CTL. Any condensation corresponding to the reduced latent heat release within the damping region will be removed as precipitation reaching the surface to eliminate its water loading effects on the circulation of Eugene. 3. Results The sensitivity of the genesis of Eugene to the different TCG parameters described in the preceding section can be evaluated through the time series

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Edward K. Vizy and Kerry H. Cook

compared to NASA AMMA radiosonde measurements from Kawsara, Senegal (14.7°N, 17.1°W), and Praia, Cape Verde Islands (14.9°N, 23.5°W), and dropsonde measurements from aircraft missions. A comparison between the WRF simulated and observed precipitable water (PW) values is given in Table 2 . Overall, total water vapor amounts in WRF are approximately 10% lower than the observed values. The differences for individual sites range from near zero up to 43.22%. Note that the dropsonde measurements have

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Zhuo Wang, M. T. Montgomery, and T. J. Dunkerton

resolution of ERA-40 was barely adequate. For a complete definition of all technical terms used herein please consult DMW09 and the glossary therein. 2 The inverted-V patterns in earth-relative streamlines and low cloud or water vapor should not be confused, as they are (dynamically and longitudinally) distinct. The schematic in Fig. 3 shows an inverted-V pattern of streamlines in the earth-relative frame. 3 The earth-relative streamlines using unfiltered data suggest a tiny closed circulation that

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Chanh Q. Kieu and Da-Lin Zhang

six classes of hydrometeors, namely, water vapor, cloud water, rain, snow, graupel, and cloud ice. Note that no cumulus parameterization is used in the 4- and 1.33-km resolution domains. The four nested grid domains have the ( x , y ) dimensions of 251 × 201 (A), 252 × 252 (B), 388 × 382 (C), and 451 × 451 (D) with a grid size of 36, 12, 4, and 1.33 km, respectively (see Fig. 4a for the nested domains A–D). There are 38 σ levels in the vertical: 1.000, 0.993, 0.980, 0.966, 0.950, 0.933, 0

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