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Willem A. Landman, David DeWitt, Dong-Eun Lee, Asmerom Beraki, and Daleen Lötter

.5 ( Roeckner et al. 1996 ) and consists of two sets. The first set (available from January 1950 to the present) is produced by forcing the ECHAM4.5 with observed SST and consists of 24 ensemble members. The second set (available from 1957 to July 2008), also consisting of 24 ensemble members, is produced by forcing the model with SST anomalies that are forecast using constructed analogs ( Van den Dool 1994 ). Forecast data from two coupled models are also used and their ocean models are, respectively, the

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Robert A. Maddox and Charlie A. Crisp

1. Introduction During March of 1948 two destructive tornadoes struck Tinker Air Force Base (AFB) within the course of only five days. The first tornado occurred shortly after 2200 CST the evening of 20 March (i.e., about 0400 UTC on 21 March). The damage was severe and there had been no forecasts or warnings indicating the potential severity of the day’s weather and storms. This tornado produced more than $10 million in damage on the base ( American Meteorological Society 1948 ), making it the

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Daniel N. Shoemaker, William M. Gray, and John D. Sheaffer

synoptic reconnaissance by United States Air Force aircraft on theaccuracy of tropical cyclone motion forecasts. Synoptic reconnaissance missions were requested for the purposeof collecting data on atmospheric conditions in proximity to developed cyclones at levels and locations whichwere assumed to govern the future motion of each storm. The results presented here suggest that synopticreconnaissance data contributed to improved JTWC motion forecasts. Data include results for cyclone

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Adam J. French and Matthew D. Parker

in the forecasting sense, to organize our cases based upon the characteristics of their background environments. Cases were subjectively grouped based on the strength of the synoptic-scale forcing, similar to the method of Evans and Doswell (2001) and could be classified by one of two general environments: a weakly forced (WF) synoptic environment characterized by a low-amplitude 500-hPa trough and a weak or nonexistent surface cyclone in the vicinity of the merger, and a strongly forced (SF

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Tom H. Durrant, Diana J. M. Greenslade, Ian Simmonds, and Frank Woodcock

, wave models, and ocean models is also critically dependent on the quality of these forcing fields. Increased computational power, improved modeling techniques, and increased availability of observations have facilitated the rapid development of NWP capabilities in recent years. Deficiencies remain, however, due to factors such as imperfect model physics and uncertainties in initial and boundary conditions. Identifying the nature and distribution of these errors provides valuable input to model

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Matthew J. Bunkers, Jeffrey S. Johnson, Lee J. Czepyha, Jason M. Grzywacz, Brian A. Klimowski, and Mark R. Hjelmfelt

long duration. Additional large-scale controls on supercell longevity include the strength and velocity of the forcing mechanisms as well as the orientation of forcing mechanisms relative to the vertical wind shear and mean wind (e.g., LaDue 1998 ; Bluestein and Weisman 2000 ; Roebber et al. 2002 ), which ultimately relates to the convective mode ( Dial and Racy 2004 ). In a numerical simulation of the 3 May 1999 tornado outbreak, Roebber et al. (2002) found weak-to-moderate forcing favored

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Frederick Sanders and Brian J. Hoskins

directions and relative magnitudes of Q-vectors from a map of isobars andisotherms. The divergence of this vector field represents the forcing function in the quasi-geostrophic omegaequation. The direction of the Q-vector at a point is determined by the rate of change of the geostrophic windvector taken along the isotherms, with the colder air.to the left in the Northern Hemisphere. Its direction is 90-to the right of this vector change of wind. The strength of the Q-vector is proportional to the

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Paul J. Roebber, James M. Frederick, and Thomas P. DeFelice

in the presence of water vapor ( − q ∇ · V ). While the vapor flux divergence term is itself an easily understood process, this split was made to explicitly separate advective processes from moisture divergence, which often result from distinct physical forcings (e.g., a broad southerly flow with a small Laplacian of warm air advection producing little low-level convergence but ample moisture advection; strong differential cyclonic vorticity advection producing low-level convergence in the

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Michael Evans and Michael L. Jurewicz Sr.

1. Introduction Numerous theoretical and observational studies have shown that bands of heavy snow often occur in regions where upward vertical motion associated with forcing for large-scale ascent is enhanced within the ascending branch of a thermally direct circulation associated with strong, steeply sloped lower- to midtropospheric frontogenesis. Snowfall within these regions can be particularly heavy when the enhanced upward motion becomes collocated with a region of reduced or negative

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Isaque Saes Lanfredi and Ricardo de Camargo

at the initial stage. The maximum amplification occurs under the influence of the Andes mountain range through the ageostrophic cold-air advection on its east side, as stated by Marengo et al. (1997) , in agreement with Gan and Rao (1994) and Seluchi et al. (1998) . According to Xu (1990) , the momentum balance on the eastern side of the Andes is responsible for forcing the cold air mass to move northward through the effect known as mountain channeling. North of 18°S, the mountains show a

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