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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

fits to typical uncertainties documented in Schwartz et al. (2008) and Remsberg et al. (2008) . We also assimilate v4.2 MLS ozone retrievals from 100 to 0.6 hPa and v4.2 MLS water vapor retrievals from 50 to 0.002 hPa. Since MLS water vapor precision degrades rapidly with height at upper levels [e.g., Table 2 of Lambert et al. (2007 )], as in Eckermann et al. (2009) , adjacent data are smoothed at heights above 0.05 hPa by applying three-point along-track smoothing prior to assimilation. From

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Benjamin Witschas, Stephan Rahm, Andreas Dörnbrack, Johannes Wagner, and Markus Rapp

troposphere, and Kiemle et al. (2007) made use of airborne DWL data in combination with water vapor measurements of a differential absorption lidar in order to estimate the latent heat flux in the boundary layer. Recently, Chouza et al. (2016) showed that vertical wind speed can be retrieved from airborne DWL measurements with a mean systematic uncertainty of 0.05 m s −1 and that the data are valuable for characterizing island-induced GWs. They also revealed that adequate corrections of horizontal

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Qingfang Jiang, James D. Doyle, Stephen D. Eckermann, and Bifford P. Williams

Mellor and Yamada (1974) and Thompson and Burk (1991) . The surface heat and momentum fluxes are computed following Louis (1979) and Louis et al. (1982) . The gridscale evolution of the moist processes is explicitly predicted from budget equations for cloud water, cloud ice, rainwater, snowflakes, and water vapor ( Rutledge and Hobbs 1983 ), and the subgrid-scale moist convective processes are parameterized using an approach based on Kain and Fritsch (1993) . A δ -four-stream approximation is

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Stephen D. Eckermann, Dave Broutman, Jun Ma, James D. Doyle, Pierre-Dominique Pautet, Michael J. Taylor, Katrina Bossert, Bifford P. Williams, David C. Fritts, and Ronald B. Smith

-atmosphere sounding (UAS) channels of the Special Sensor Microwave Imager/Sounder (SSMIS) on four Defense Meteorological Satellite Program (DMSP) polar orbiters (F16–F19 inclusive), using the NAVGEM UAS radiance assimilation procedures described in Hoppel et al. (2013) ; limb measurements of temperature and water vapor up to 0.002 hPa from version 3.3 retrievals of the Microwave Limb Sounder (MLS) on the Aura satellite and of temperature up to 10 −4 hPa from version 2.0 retrievals of the Sounding of the

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

fields like wind, pressure, temperature, water vapor mixing ratio, and the Brunt–Väisälä frequency are stored every 5 min for the inner domain to obtain a temporal resolution similar to the lidar raw data. This high-resolution output enables the combination and comparison of WRF simulations with lidar, aircraft, and radiosonde data. The numerical WRF Model is commonly used for many different atmospheric phenomena [e.g., polar lows ( Wagner et al. 2011 ), tropical cyclones ( Davis et al. 2008

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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

) as the Jacobian term ∂ T B i / ∂ T quantifying the linearized CRTM response of brightness temperature in channel i to small atmospheric temperature perturbations at a given altitude. In deriving these CRTM Jacobians we used background profiles of temperature, ozone and water vapor mixing ratio derived by averaging high-altitude Navy reanalysis fields of Eckermann et al. (2009b) over various DEEPWAVE areas of interest (see Fig. 4 ) for the months June–July and years 2007–09 inclusive

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