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Sonja Gisinger, Andreas Dörnbrack, Vivien Matthias, James D. Doyle, Stephen D. Eckermann, Benedikt Ehard, Lars Hoffmann, Bernd Kaifler, Christopher G. Kruse, and Markus Rapp

1. Introduction The overarching objectives of the Deep Propagating Gravity Wave Experiment (DEEPWAVE; see appendix A for a list of key acronyms used in this paper) were to observe, model, understand, and predict the deep vertical propagation of internal gravity waves from the troposphere to the lower thermosphere and to study their impacts on the atmospheric momentum and energy budget ( Fritts et al. 2016 ). Convection, fronts, flow over mountains, and spontaneous adjustments occurring at the

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Johnathan J. Metz, Dale R. Durran, and Peter N. Blossey

. One potential further explanation is the presence of trapped mountain lee waves with very deep vertical penetration and significant amplitude in the stratosphere. Yet to the best of our knowledge, such waves have never been documented in observations, nor have they been simulated numerically or evaluated using linear theory for realistic atmospheric conditions. In contrast, mountain lee waves with maximum amplitude in the lower troposphere are often observed downstream of topographic barriers

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Ronald B. Smith and Christopher G. Kruse

, respectively. This empirical description represents the physics of stratified atmospheric boundary layer over complex terrain. Slower winds give effectively smoother terrain and smaller drag coefficients. Functionally, the drag matrix is , where . 7. Comparing wave drag laws with WRF drag time series The goal of this section is to compare the matrix drag law [ (25) , (26) , (34) , and (37) ] against the WRF wave drag time series. To review, our drag law uses the instantaneous regional wind speed to

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Ronald B. Smith, Alison D. Nugent, Christopher G. Kruse, David C. Fritts, James D. Doyle, Steven D. Eckermann, Michael J. Taylor, Andreas Dörnbrack, M. Uddstrom, William Cooper, Pavel Romashkin, Jorgen Jensen, and Stuart Beaton

expressions, the symbols g , C p , θ , Ω, and ϕ are gravity, specific heat capacity, potential temperature, Earth rotation rate, and latitude, respectively. Gravity waves play a significant role in atmospheric dynamics by dispersing mesoscale horizontal potential temperature gradients, aiding geostrophic adjustment, and transporting energy and momentum from source to sink regions ( Eliassen and Palm 1960 ; Bretherton 1969 ; Holton 1982 ; Fritts and Nastrom 1992 ; Alexander et al. 2010 ). The

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Ronald B. Smith and Christopher G. Kruse

. Gary , 1996 : Stratospheric horizontal wavenumber of winds, potential temperature, and atmospheric tracers observed by high-altitude aircraft . J. Geophys. Res. , 101 , 9441 – 9470 , doi: 10.1029/95JD03835 . 10.1029/95JD03835 Bannon , P. R. , and J. A. Yuhas , 1990 : On mountain drag over complex terrain . Meteor. Atmos. Phys. , 43 , 155 – 162 , doi: 10.1007/BF01028118 . 10.1007/BF01028118 Blumen , W. , 1965 : Momentum flux by mountain waves in a stratified rotating atmosphere . J

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Stephen D. Eckermann, Jun Ma, Karl W. Hoppel, David D. Kuhl, Douglas R. Allen, James A. Doyle, Kevin C. Viner, Benjamin C. Ruston, Nancy L. Baker, Steven D. Swadley, Timothy R. Whitcomb, Carolyn A. Reynolds, Liang Xu, N. Kaifler, B. Kaifler, Iain M. Reid, Damian J. Murphy, and Peter T. Love

)/National Center for Atmospheric Research (NCAR) Gulfstream V research aircraft (NGV; Laursen et al. 2006 ). As shown in Fig. 1b , during DEEPWAVE, the NGV was equipped with in situ and remote sensing instruments with the necessary vertical range, space–time resolution, and measurement precision to observe gravity wave dynamics over most of the 0–100-km altitude range. NGV observing missions were planned and supported by a suite of gravity wave–resolving numerical weather prediction (NWP) systems ( Fig. 1b

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Christopher G. Kruse, Ronald B. Smith, and Stephen D. Eckermann

general circulation . J. Geophys. Res. Atmos. , 118 , 11 589 – 11 599 , doi: 10.1002/2013JD020526 . 10.1002/2013JD020526 Alexander , M. J. , and Coauthors , 2010 : Recent developments in gravity-wave effects in climate models and the global distribution of gravity-wave momentum flux from observations and models . Quart. J. Roy. Meteor. Soc. , 136 , 1103 – 1124 , doi: 10.1002/qj.637 . 10.1002/qj.637 Aumann , H. H. , and C. R. Miller , 1995 : Atmospheric infrared sounder (AIRS) on the

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Stephen D. Eckermann, James D. Doyle, P. Alex Reinecke, Carolyn A. Reynolds, Ronald B. Smith, David C. Fritts, and Andreas Dörnbrack

instruments on the National Science Foundation (NSF)/National Center for Atmospheric Research (NCAR) Gulfstream V research aircraft (NGV; Laursen et al. 2006 ). Yet this very lack of observational knowledge about gravity waves that spurred DEEPWAVE also complicated logistical planning for an NGV-based gravity wave measurement campaign: for example, identifying the best site and time of year; designing near-real-time flight-planning strategies to locate, intercept, and observe specific aspects of gravity

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Christopher G. Kruse and Ronald B. Smith

1. Introduction Gravity waves are atmospheric buoyancy oscillations that transport energy and horizontal momentum vertically throughout the atmosphere ( McLandress 1998 ). The vertical propagation and dissipation of gravity waves are important as the carried energy and momentum are deposited wherever these waves break, affecting the mean flow. Gravity waves and their dissipation have long been recognized to be important in middle atmosphere dynamics ( Fritts 1989 ). Important gravity wave

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Benedikt Ehard, Peggy Achtert, Andreas Dörnbrack, Sonja Gisinger, Jörg Gumbel, Mikhail Khaplanov, Markus Rapp, and Johannes Wagner

wave excitation and propagation. Our results should encourage other scientists to apply this kind of an approach to past and future datasets of middle atmospheric lidar measurements. Section 2 provides an overview of the methodological approach of this study, starting with a description of the instruments and tools used. The results are presented in section 3 and discussed in detail in section 4 . Finally, the conclusions are given in section 5 . 2. Methodology a. Ground-based lidar

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