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H. J. S. Fernando, E. R. Pardyjak, S. Di Sabatino, F. K. Chow, S. F. J. De Wekker, S. W. Hoch, J. Hacker, J. C. Pace, T. Pratt, Z. Pu, W. J. Steenburgh, C. D. Whiteman, Y. Wang, D. Zajic, B. Balsley, R. Dimitrova, G. D. Emmitt, C. W. Higgins, J. C. R. Hunt, J. C. Knievel, D. Lawrence, Y. Liu, D. F. Nadeau, E. Kit, B. W. Blomquist, P. Conry, R. S. Coppersmith, E. Creegan, M. Felton, A. Grachev, N. Gunawardena, C. Hang, C. M. Hocut, G. Huynh, M. E. Jeglum, D. Jensen, V. Kulandaivelu, M. Lehner, L. S. Leo, D. Liberzon, J. D. Massey, K. McEnerney, S. Pal, T. Price, M. Sghiatti, Z. Silver, M. Thompson, H. Zhang, and T. Zsedrovits

Comprehensive, multiscale, and multidisciplinary observations allow scientists to discover novel flow physics, address current deficiencies of predictive models, and improve weather prediction in mountainous terrain. Through woods and mountain passes the winds, like anthems, roll. —Henry Wadsworth Longfellow For centuries, humans have been both fascinated and awed by mountain weather, and its intriguing aberrancy continues to baffle weather forecasters. For instance, a clear morning on a

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Robert S. Arthur, Katherine A. Lundquist, Jeffrey D. Mirocha, and Fotini K. Chow

mesoscale atmospheric model was done by Colette et al. (2003) in the Advanced Regional Prediction System (ARPS). Since then, other models have included topographic effects on radiation. These include the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5; Zängl 2005 ) and the Weather Research and Forecasting (WRF) Model ( Skamarock et al. 2008 ), which is used in this study. Although topographic shading improves the representation of surface

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Jeffrey D. Massey, W. James Steenburgh, Sebastian W. Hoch, and Derek D. Jensen

1. Introduction The variability of regional land surface characteristics in mesoscale numerical weather prediction (NWP) models has a potentially strong influence on near-surface forecasts. Some sources of land surface variability, such as coastlines and topographic features, are easily represented in NWP models, but other more subtle land surface characteristics (e.g., albedo, emissivity, roughness length, soil porosity, soil texture, and soil moisture) are more difficult to specify and

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Jeffrey D. Massey, W. James Steenburgh, Sebastian W. Hoch, and Jason C. Knievel

1. Introduction Near-surface (2 m) temperature (NST) forecasts are critical for the protection of life and property, for economic and operational activities, and for routine day-to-day planning but remain a major challenge for numerical weather prediction. Modeling systems in many regions of the world have trouble simulating NSTs and typically underpredict the diurnal NST cycle, which largely reflects a pronounced nighttime NST warm bias (e.g., Steeneveld et al. 2008 ; Edwards et al. 2011

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Raquel Lorente-Plazas and Joshua P. Hacker

Observation Program (MATERHORN) funded by the Office of Naval Research (MURI) Award N00014-11-1-0709 (Program Officers: Drs. Ronald Ferek and Daniel Eleuterio), with additional funding from the Army Research Office (Program Officers: Gordon Videen and Walter Bach), Air Force Weather Agency, Research Offices of University of Notre Dame and University of Utah. The authors thank the DART team, especially Nancy Collins, for their help with the code modifications. REFERENCES Aksoy , A. , F. Zhang , and J

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Matthew E. Jeglum, Sebastian W. Hoch, Derek D. Jensen, Reneta Dimitrova, and Zachariah Silver

1. Introduction Fluctuations of near-surface air temperature are a common feature of the stable nocturnal boundary layer. In this study, we define a large temperature fluctuation (LTF) as a drop of at least 3°C in less than 30 min followed by a recovery of at least half the initial temperature drop within one hour of the temperature minimum. LTFs can indicate substantial modification of the horizontal and vertical structure of the stable boundary layer. The potential mechanisms that could

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