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Tammy M. Weckwerth and David B. Parsons

vapor. Radiosondes, the traditional means of obtaining water vapor measurements, are insufficient because they provide vertical profile information at widely distributed locations, are typically only available twice a day, and sometimes contain significant errors and biases (e.g., Soden and Lanzante 1996 ; Guichard et al. 2000 ; Wang et al. 2002 ; Revercomb et al. 2003 ; Turner et al. 2003 ; Ciesielski et al. 2003 ). Additionally, there is a general absence of operational, scanning ground

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Robert Wood

. Fig . 10. Vertical profiles of water vapor q and liquid water q l mixing ratios, equivalent potential temperature θ e , and temperature T for a summertime shallow and quite well-mixed STBL observed over the North Sea to the east of a ridge. Means from horizontal legs are depicted by dots. The dotted lines in each case show the values expected for a well-mixed layer. Adapted from Nicholls (1984) . Fig . 11. Photograph of the stratocumulus cloud top taken on a research flight over the

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John Molinari and Michael Dudek

of futurework. Definitions and terminology. Cumulus parameterization requires the creation of subgrid-scale implicitclouds, which vertically transport heat, water vapor,and other quantities, generally in the absence of gridscale saturation. Closure assumptions are required todefine the relationship between these implicit cloudsand the grid-scale variables. For mesoscale models, theform of closure may have to difl~r from that for largescale models (Fritsch and Chappell 1980; Frank 1983).Details

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Andrew Staniforth and Jean Côté

MONTHLY WEATHER REVIEW VOLUME 119 Williamson and Rasch (1989), Williamson (1990),and Rasch and Williamson ( 1990a, 1990b, 1991 ) havepursued this approach and thoroughly compared spectral and semi-Lagrangian schemes for the transport ofwater vapor in otherwise Eulerian, hydrostatic primitive-equation models, in the context of both mediumrange forecasting and general circulation modeling. Thetransport of water vapor

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T. N. Krishnamurti

/ I X I ~ I ! ! ~ , ,-~,lr- , r-..~uT--%T 0 40 50 60 7'0 80 90 I00 I I 0 120 , '~-. , , , ~ ~ r~..., '~.._j.~r , ,~, / - I 40 0 60 70 80 90 I00 I lO IZO 20 L L ,o 3o 4o ~o 6o ?o 80 9o ~oo t~od3- ~' ',~.~L/ ~.J V"~o~r'~ o 30 40 ,50 60 70 80 90 I00 I I0 120FIG. 10. Mean zonal water vapor ttux fields for: (a) 1-15 June; (b) 16-30 June; (c) 7-18 July; (d) 25 July-6 Aug. Units kg/m s-~ (from

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Markus Gross, Hui Wan, Philip J. Rasch, Peter M. Caldwell, David L. Williamson, Daniel Klocke, Christiane Jablonowski, Diana R. Thatcher, Nigel Wood, Mike Cullen, Bob Beare, Martin Willett, Florian Lemarié, Eric Blayo, Sylvie Malardel, Piet Termonia, Almut Gassmann, Peter H. Lauritzen, Hans Johansen, Colin M. Zarzycki, Koichi Sakaguchi, and Ruby Leung

splitting, competition for resources is also a problem for parallel splitting because it can result in unrealistically strong removal of resources. The most egregious cases of this are, for example, negative concentrations of water vapor, hydrometeors, or other tracer species. These are typically resolved by rescaling tendencies to prevent overconsumption. This approach may leave more subtle cases untreated and, where applied, results in transport that does not locally satisfy the transport equations of

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David M. Schultz and Philip N. Schumacher

additional concern for assessing MSI using mesoscale-model output involves how the model is initialized. Observations of water substance (vapor, cloud water, hydrometeors) in the atmosphere tend to be more sparse than observations of the thermal, wind, or mass fields. Therefore, models rely heavily on developing their own water fields. If the model simulation is started as a static initialization (i.e., lacks cloud water and precipitation fields at the initial time; the so-called cold start), like many

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Clark Evans, Kimberly M. Wood, Sim D. Aberson, Heather M. Archambault, Shawn M. Milrad, Lance F. Bosart, Kristen L. Corbosiero, Christopher A. Davis, João R. Dias Pinto, James Doyle, Chris Fogarty, Thomas J. Galarneau Jr., Christian M. Grams, Kyle S. Griffin, John Gyakum, Robert E. Hart, Naoko Kitabatake, Hilke S. Lentink, Ron McTaggart-Cowan, William Perrie, Julian F. D. Quinting, Carolyn A. Reynolds, Michael Riemer, Elizabeth A. Ritchie, Yujuan Sun, and Fuqing Zhang

positive impact upon the model first-guess field from assimilating targeted observations earlier in the TC life cycle ( Weissmann et al. 2011 ). Targeted lidar-derived water vapor observations improved analysis quality but had a negligible mean impact on forecast quality for the eight T-PARC missions considered by Harnisch et al. (2011) . The effects of assimilating Doppler wind lidar observations collected during T-PARC varied, as they had a positive impact at all lead times on TC track and large

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Roland A. Madden and Paul R. Julian

precipitation of break andactive phases during MONEX (taken from Cadet1986). The break and active monsoon phases shownin Fig. 10 were accompanied by correspondingly smalland large water vapor flux across the Arabian Sea (Cadet and Greco 1987). Wylie and Hinton (1982) computed 10-day-averaged surface wind stress over the Indian Ocean duringMONEX. They showed maximum values of 0.35N m-2 in the region of the Somali jet over the westernArabian Sea in mid-June (their Fig. 7) before the onsetof heavy rainfall

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Robert A. Houze Jr.

the western North Pacific Ocean, and cyclones in the South Pacific and Indian Oceans. They are all the same phenomenon. Gray (1968) identified most of the environmental factors favoring tropical cyclones. They originate over oceans, as their primary energy source is the latent heat of water vapor in the atmospheric boundary layer. They nearly always form over regions where the sea surface temperature (SST) exceeds 26.5°C ( Fig. 1a ). Once tropical cyclones form, they tend to be advected by the

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