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Peter Sheridan and Simon Vosper

1. Introduction The Sierra Nevada range is a well-known source of strong mountain waves, downslope windstorms, and turbulence associated with lee-wave rotors, which represent hazards to aviation, residents, and property and are difficult for forecasters to predict ( Holmboe and Klieforth 1957 ; Grubisic and Lewis 2004 ). Continued increase in the resolution of operational numerical weather prediction (NWP) models is expected to improve forecasts as the phenomena become more explicitly resolved

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Laurence Armi and Georg J. Mayr

combination of changes on the large scale and the local scale made it possible for foehn to penetrate nearly 3 km down the Sierra Nevada slopes to the floor of Owens Valley. Figure 4 tracks the large-scale evolution at approximate crest height of the model Sierra Nevada in the European Centre for Medium-Range Weather Forecasts (ECMWF) global analyses in 6-hourly intervals from 1800 UTC through 1200 UTC (1000–0400 LST). Throughout this 18-h period, the large-scale flow upstream and across the sierras

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Stephen A. Cohn, Vanda Grubiššićć, and William O. J. Brown

waves and rotors as well as simulations of valley flows under more quiescent conditions. This includes the performance of forecast and research models under such conditions and simulation of small-scale features of rotors ( Doyle et al. 2009 , 2011 ; Schmidli et al. 2011 ). In turn, interpretation of the observations benefits from insights gained from the model simulations. Prior to T-REX and SRP, the majority of observations of rotors and the conditions surrounding them were made in the four

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Željko Večenaj, Stephan F. J. De Wekker, and Vanda Grubišić

mesoscale models that parameterize the effects of turbulence use a local closure technique (e.g., Stensrud 2007 ). If a prognostic equation for TKE is included, a parameterization for ε is required (e.g., Mellor and Yamada 1974 ): where is the mean value of TKE and Λ is an empirical length-scale parameter. There is unfortunately no unique rule as to how to determine this parameter. It is often chosen by nudging the simulated flow to the observed flow (e.g., Stull 1988 ). Using the data obtained

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