Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 16, issue 12
Articles | Volume 16, issue 12
https://doi.org/10.5194/amt-16-3141-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-16-3141-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 12
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

Video supplement

Observations of the OH* airglow imager FAIM 3 on 4 August 2019, classified into episodes with clouds, calm movement of the OH* layer and dynamical episodes (strong movement of the OH* layer) by the application of an AI algorithm René Sedlak, Andreas Welscher, Sabine Wüst, and Michael Bittner https://doi.org/10.5446/60729

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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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