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Brian J. Squitieri and William A. Gallus Jr.

thresholds at 0600–1200 UTC and MAEs for the LLJ atmospheric water vapor content at 0300 UTC. (top) The older results before the corrections applied and (bottom) the newer results after the applied correction. Regarding the other noticeable change in results, 0600–1200 UTC MCS QPF skill correlated more with 0600 and 0900 UTC LLJ potential temperature forecast accuracy for strongly forced cases before the correction. Now, MCS QPF skill at 0300–0900 and 0600–1200 UTC demonstrates more significant

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Mana Inoue, Alexander D. Fraser, Neil Adams, Scott Carpentier, and Helen E. Phillips

these profiles exceed a threshold τ , and comparing this against the surface observations. Four polarLAPS variables explicitly involve water content: water vapor mixing ratio, rainwater mixing ratio, cloud water mixing ratio, and cloud ice mixing ratio. Only cloud water and cloud ice mixing ratios are relevant for Antarctic clouds. Water vapor is irrelevant for this study because it is invisible at visible wavelengths (400–700 nm) and therefore not a hazard to aviation, and rainwater mixing ratio

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Patrick Market, Stacy Allen, Roderick Scofield, Robert Kuligowski, and Arnold Gruber

efficiency. Braham (1952) worked with airflow and moisture budgets of typical airmass thunderstorms studied during the Thunderstorm Project. Surface observations and soundings provided sufficient data to calculate average moisture budgets, showing that total inflow of water vapor into these thunderstorms was almost 9 times the amount of precipitation measured at the ground. These numbers translate to around 10% PE, but it was Newton (1966) who did the efficiency calculation from Braham's (1952

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Luis M. Farfán

Satellite-10 ( GOES-10) is used to document the distribution of humidity and cloud cover. This includes imagery from the water vapor, visible, and infrared channels. Additional information used to explore the three-dimensional structure of the large-scale flow is derived from operational analyses of the Eta Model issued by the National Centers for Environmental Prediction (NCEP) at 40-km resolution. To examine in situ characteristics of the environment over northwestern Mexico (including Isla Socorro

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Thomas F. Lee

infrared image enhancements that target the brightness temperatures associated with stratus often fail to depict these clouds unambiguously. In real-time monitoring efforts, the first visible image of the day often reveals important information about storm structure that was missing during the previous night. There are several problems with infrared images. First, abundant tropical water vapor partially obscures the view of the satellite of stratiform clouds even when higher clouds are absent. Second

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Kyo-Sun Sunny Lim, Eun-Chul Chang, Ruiyu Sun, Kwonil Kim, Francisco J. Tapiador, and GyuWon Lee

the outer domain of a 9-km resolution grid. Four different cloud microphysics parameterization schemes, namely WSM6, WDM6, Thompson, and Morrison, were chosen to examine the impact of the selected parameterizations on the simulated winter precipitation. All four microphysics parameterizations have five hydrometeors, namely cloud water, rain, cloud ice, snow, and graupel with water vapor. WSM6 and WDM6 share the ice microphysics processes based on Hong et al. (2004) . The mass of cloud ice in both

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Max L. Dupilka and Gerhard W. Reuter

correlated with geographical differences and have little to do with the occurrence or nonoccurrence of tornadoes. Brooks et al. (1994a) suggest that the thermodynamic environment profile affects the formation of low-level mesocylones. The amount of water vapor available to the storm should affect the amount of precipitation generated and, as a result, the potential for evaporation and generation of vorticity ( Brooks et al. 1994a , b ). Greater moisture content may mean that the mesocyclone can move

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André Tremblay, Stewart G. Cober, Anna Glazer, George Isaac, and Jocelyn Mailhot

in clouds with decreasing temperature. Thus, it is conceivable that the SPS and APP schemes may fail to reproduce correctly the observed distribu- tion of SLW with temperature. b. A cloud microphysics scheme To delineate the conditions for the existence of SLW, Tremblay et al. ( 1995) performed several simulations with the cloud microphysics model of Zawadzki et al. ( 1993) . This model includes conservation equations for temperature, water vapor, cloud water, cloud ice, rain, snow, and

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Dan Bikos, John F. Weaver, and Jeff Braun

( Feltz et al. 1998 ) have also been used to gauge water vapor content in the lower atmosphere. The purpose of this note is to illustrate the role of geostationary satellite imagery in tracking low-level moisture during the day or night. The surface observations provide approximate position (when used as an overlay with satellite imagery) and some quantitative information about the returning moist air mass. Upper-air soundings give a coarse representation of the magnitude and depth of the moisture

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Ruping Mo, Melinda M. Brugman, Jason A. Milbrandt, James Goosen, Quanzhen Geng, Christopher Emond, Jonathan Bau, and Amin Erfani

operational forecasters develop a robust conceptual model for their prognostic assessment of orographic precipitation. The two selected events occurred in the Canadian western province of British Columbia (BC) during 26–28 January 2016 and 16–18 January 2017, respectively. They were the typical severe winter storms that can be attributed to a phenomenon called “atmospheric river” (AR): a long, narrow, and transient corridor of strong water vapor transport ( Newell et al. 1992 ; Zhu and Newell 1994

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