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  • Mountain Terrain Atmospheric Modeling and Observations (MATERHORN) x
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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

tranquil mountain slope can swiftly change into violent storms within hours while a nearby valley remains calm. The variability of mountain weather spans a wide swath of space–time scales, contributing to a myriad of phenomena that stymie the predictability of mountain weather. Although isolated mountains are rare, about 20% of Earth’s land surface is covered by mountainous areas ( Louis 1975 ). Topography less than 600 m in height (<5% of the atmospheric-scale height) is referred to as hills, but

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

aspect ratio of the barrier with large β indicating mountains elongated in the cross-flow direction. For ≪ 1, small-amplitude waves or wave breaking are the likely flow regimes that will develop. The flow is relatively insensitive to β in these cases. However, the atmospheric stability and flow field during these regimes are not conducive to maintaining a strong CAP (and therefore a large temperature gradient) that would allow for LTFs to occur ( Lee et al. 1989 ; Lareau and Horel 2015a

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Raquel Lorente-Plazas and Joshua P. Hacker

of bias is uncertain, because incorrect attribution can cause the assimilation adapt to an unknown bias. Dee (2004) speculated that background departures can increase using observation bias correction due to the presence of systematic model errors. Although all atmospheric models are biased, and biased observations are common, most past work has focused on addressing one source of bias (e.g., Baek et al. 2006 ; Auligné et al. 2007 ). Few studies have addressed both model and observation

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Manuela Lehner, C. David Whiteman, Sebastian W. Hoch, Derek Jensen, Eric R. Pardyjak, Laura S. Leo, Silvana Di Sabatino, and Harindra J. S. Fernando

Granite Mountain at the U.S. Army’s Dugway Proving Ground in Utah during the Mountain Terrain Atmospheric Modeling and Observations (MATERHORN) field campaign. The field campaign consisted of two 1-month field experiments, which were conducted in October of 2012 and May of 2013, with the overarching objectives of studying interactions of motions of different scales in complex terrain and of improving the predictability of weather in complex terrain ( Fernando et al. 2015 ). In this paper, we focus

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Joshua P. Hacker and Lili Lei

III) forms the basis for experiments. For an atmospheric analog, this model improves on the widely used models documented in Lorenz (1995) because the dominant waves in L05 result in strong spatial correlations between neighboring grid points. A summary of the most relevant parts of Lorenz (2005) follows, and we refer the interested reader to that paper for further details. Using grid index n over N grid points, the model is written as Here, Z is the prognostic variable, which has

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Sean M. Wile, Joshua P. Hacker, and Kenneth H. Chilcoat

used in this study. The outermost domain used 36-km horizontal grid spacing, the middle 12 km, and the innermost 4 km. The Data Assimilation Research Testbed (DART) facilitates ensemble data assimilation. DART is a community software environment created for ensemble data assimilation research, is principally developed by staff at the National Center for Atmospheric Research, and contains several varieties of ensemble filters ( Anderson et al. 2009 ). The specific serial implementation of the

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