Articles | Volume 12, issue 1
https://doi.org/10.5194/amt-12-457-2019
https://doi.org/10.5194/amt-12-457-2019
Research article
 | 
25 Jan 2019
Research article |  | 25 Jan 2019

Seasonal and intra-diurnal variability of small-scale gravity waves in OH airglow at two Alpine stations

Patrick Hannawald, Carsten Schmidt, René Sedlak, Sabine Wüst, and Michael Bittner

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Cited articles

Baker, D. J. and Romick, G. J.: The rayleigh: interpretation of the unit in terms of column emission rate or apparent radiance expressed in SI units, Appl. Optics, 15, 1966–1968, 1976. 
Baker, D. J. and Stair Jr., A. T.: Rocket Measurements of the Altitude Distributions of the Hydroxyl Airglow, Phys. Scripta, 37, 611–622, 1988. a
Becker, E.: Sensitivity of the Upper Mesosphere to the Lorenz Energy Cycle of the Troposphere, J. Atmos. Sci., 66, 647–666, https://doi.org/10.1175/2008JAS2735.1, 2009. a
Bradski, G.: The OpenCV Library, Dr. Dobb's Journal of Software Tools, 2000. a
Coble, M. R., Papen, G. C., and Gardner, C. S.: Computing Two-Dimensional Unambiguous Horizontal Wavenumber Spectra from OH Airglow Images, IEEE T. Geosci. Remote, 36, 368–382, 1998. a
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Short summary
We use a near-infrared camera for the investigation of gravity waves. The camera observes the airglow layer, which is modulated by the gravity waves. The image processing, including the removal of the stars is explained. We describe the analysis with a 2D fast Fourier transform and automatic derivation of the wave parameters. The results show a clear seasonal and intra-diurnal variability, which is characterised in order to improve our understanding of gravity waves in the middle atmosphere.