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Jannik Wilhelm, T. R. Akylas, Gergely Bölöni, Junhong Wei, Bruno Ribstein, Rupert Klein, and Ulrich Achatz

1. Introduction Internal gravity waves (GWs) play a significant role in atmospheric dynamics on various spatial scales ( Fritts and Alexander 2003 ; Kim et al. 2003 ; Alexander et al. 2010 ; Plougonven and Zhang 2014 ). Already in the lower atmosphere GW effects are manifold. Examples include the triggering of high-impact weather (e.g., Zhang et al. 2001 , 2003 ) and clear-air turbulence ( Koch et al. 2005 ), as well as the effect of small-scale GWs of orographic origin on the predicted

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Gergely Bölöni, Bruno Ribstein, Jewgenija Muraschko, Christine Sgoff, Junhong Wei, and Ulrich Achatz

1. Introduction The parameterization of gravity waves (GWs) is of significant importance in atmospheric global circulation models (GCM), in global numerical weather prediction (NWP) models, and in ocean models. In spite of the increasing available computational power and the corresponding increase of spatial resolution of GCMs and NWP models, for the time being, an important range of GW spatial scales remains unresolved both in climate simulations and in global NWP ( Alexander et al. 2010

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Junhong Wei, Gergely Bölöni, and Ulrich Achatz

; Butchart 2014 ). The dynamics of the middle atmosphere can influence the tropospheric circulation by downward control ( Haynes et al. 1991 ), and it can be very important for the forecasting of weather ( Baldwin and Dunkerton 2001 ) and climate ( Scaife et al. 2005 , 2012 ). Despite the increasing computational power, an important range of GW spatial scales remains unresolved in most atmospheric global circulation models (GCM) or in global numerical weather prediction (NWP) models ( Alexander et al

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Claudia Christine Stephan, Cornelia Strube, Daniel Klocke, Manfred Ern, Lars Hoffmann, Peter Preusse, and Hauke Schmidt

fine to capture a major fraction of the GW spectrum (e.g., Beres et al. 2004 ; Choi and Chun 2011 ). GWs in high-resolution (~4 km) simulations of regional mesoscale models, such as the Weather Research and Forecasting (WRF) Model, can have a high degree of realism ( Grimsdell et al. 2010 ; Orr et al. 2015 ; Stephan and Alexander 2015 ; Stephan et al. 2016 ). In the light of ever-increasing computational capabilities, the above challenges have served as a strong motivation to devise global

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