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Christopher G. Kruse and Ronald B. Smith

into a simpler field of wave diagnostic quantities. It combines several standard signal processing algorithms. The starting point is a set of gridded 2D velocity, pressure, and temperature fields from a numerical simulation or interpolated observational dataset. Ideally, these fields are on a level surface and a uniform square grid as the method assumes constant horizontal resolution. The first two steps (deplaning and high-pass spatial filtering) split the field into a smoothly varying background

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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

Island produced any deep gravity wave activity to observe. Thus, ahead of DEEPWAVE a 9-yr climatology of stratospheric gravity wave amplitudes over the greater New Zealand region was recomputed from AIRS data using modified analysis and averaging algorithms and a different collection of AIRS thermal radiance channels to improve signal to noise and geographical resolution [following Eckermann and Wu (2012) and Eckermann et al. (2016b, unpublished manuscript)]. The resulting variance maps, shown in

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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

Abstract

During the Deep Propagating Gravity Wave Experiment (DEEPWAVE) project in June and July 2014, the Gulfstream V research aircraft flew 97 legs over the Southern Alps of New Zealand and 150 legs over the Tasman Sea and Southern Ocean, mostly in the low stratosphere at 12.1-km altitude. Improved instrument calibration, redundant sensors, longer flight legs, energy flux estimation, and scale analysis revealed several new gravity wave properties. Over the sea, flight-level wave fluxes mostly fell below the detection threshold. Over terrain, disturbances had characteristic mountain wave attributes of positive vertical energy flux (EFz), negative zonal momentum flux, and upwind horizontal energy flux. In some cases, the fluxes changed rapidly within an 8-h flight, even though environmental conditions were nearly unchanged. The largest observed zonal momentum and vertical energy fluxes were MFx = −550 mPa and EFz = 22 W m−2, respectively.

A wide variety of disturbance scales were found at flight level over New Zealand. The vertical wind variance at flight level was dominated by short “fluxless” waves with wavelengths in the 6–15-km range. Even shorter scales, down to 500 m, were found in wave breaking regions. The wavelength of the flux-carrying mountain waves was much longer—mostly between 60 and 150 km. In the strong cases, however, with EFz > 4 W m−2, the dominant flux wavelength decreased (i.e., “downshifted”) to an intermediate wavelength between 20 and 60 km. A potential explanation for the rapid flux changes and the scale “downshifting” is that low-level flow can shift between “terrain following” and “envelope following” associated with trapped air in steep New Zealand valleys.

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Ronald B. Smith and Christopher G. Kruse

McFarlane 2000 ; Webster et al. 2003 ; Garner 2005 ). The standard representation is an “airflow-blocking algorithm.” The concept of mountain airflow blocking due to density stratification has been well developed by Kao (1965) , Hunt et al. (1979) , Snyder et al. (1985) , Spangler (1987) , Smith (1989) , Miranda and James (1992) , Smith and Grønås (1993) , Ólafsson and Bougeault (1996) , Baines (1997) , Reinecke and Durran (2008) , and others. These studies all found that when 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

, this process allows us to coherently average brightness temperature maps from different channels i with similar kernel functions, as (2) T B j = ⁡ ( n j tot ) − 1 ∑ n = 1 n j tot T B i ⁡ ( n ) . Gravity wave perturbations T B j ′ are extracted from T B j imagery by fitting and removing the larger-scale background structure using algorithms described in appendix A , section c . Products so derived for DEEPWAVE from specific nadir satellite sensor data are now described. a. AIRS 15-μm

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

-to-noise ratio (SNR) conditions is based on the maximum function of accumulated spectra (MFAS), which retrieves the wind vector without estimating single-radial wind velocities, as is necessary, for instance, when applying sine-wave fitting methods. A modified version of the MFAS algorithm that additionally exploits the frequency deviation of accumulated spectra from their nominal value to further increase the number of reliable wind vector estimates is first used in this study. The principle of the

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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

ensemble forecasts described in section 2b(2) employed stochastic kinetic energy backscatter (SKEB), as described in section 2b of Reynolds et al. (2011) , but with an additional convective dissipation mask based on moisture convergence (see section 3b of Reynolds et al. 2011 ) that enhances kinetic energy by introducing vorticity perturbations in areas where convective processes are likely to occur. 2) Data assimilation algorithm (i) Formulation The current NRL Atmospheric Variational DAS (NAVDAS

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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

operational analyses, a reanalysis of the 2014 DEEPWAVE austral winter was performed using a high-altitude research version of the NAVGEM system ( Hogan et al. 2014 ). The reanalysis discussed here used a T119L74 forecast model and a T47L74 tangent linear model utilizing a four-dimensional variational data assimilation (4DVAR) algorithm in which 80-member forecast ensembles helped to define background error covariances for the analysis (so-called hybrid-4DVAR). The L74 levels have a top full model layer

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