Articles | Volume 16, issue 12
https://doi.org/10.5194/amt-16-3141-2023
https://doi.org/10.5194/amt-16-3141-2023
Research article
 | 
26 Jun 2023
Research article |  | 26 Jun 2023

Analysis of 2D airglow imager data with respect to dynamics using machine learning

René Sedlak, Andreas Welscher, Patrick Hannawald, Sabine Wüst, Rainer Lienhart, and Michael Bittner

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Cited articles

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Short summary
We show that machine learning can help in classifying images of the OH* airglow, a thin layer in the middle atmosphere (ca. 86 km height) emitting infrared radiation, in an efficient way. By doing this, dynamic episodes of strong movement in the OH* airglow caused predominantly by waves can be extracted automatically from large data sets. Within these dynamic episodes, turbulent wave breaking can also be found. We use these observations of turbulence to derive the energy released by waves.
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