13 Mar 2023
 | 13 Mar 2023
Status: this preprint is currently under review for the journal AMT.

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

Abstract. We demonstrate how machine learning can be easily applied to support the analysis of large amounts of OH* airglow imager data. We use a TCN (temporal convolutional network) classification algorithm to automatically pre-sort images into the three categories “dynamic” (images where small-scale motions like turbulence are likely to be found), “calm” (clear-sky images with weak airglow variations) and “cloudy” (cloudy images where no airglow analyses can be performed). The proposed approach is demonstrated using image data of FAIM 3 (Fast Airglow IMager), acquired at Oberpfaffenhofen, Germany between 11 June 2019 and 25 February 2020, achieving a mean average precision of 0.82 in image classification. The attached video sequence demonstrates the classification abilities of the learned TCN.

Within the “dynamic” category, we find a subset of 13 episodes of image series showing turbulence. As FAIM 3 exhibits a high spatial (23 m pixel−1) and temporal (2.8 s per image) resolution, turbulence parameters can be derived to estimate the energy diffusion rate. Similar to the results the authors found for another FAIM station (Sedlak et al., 2021), the values of energy dissipation rate range from 0.03 to 3.18 W kg−1.

René Sedlak et al.

Status: open (until 18 Apr 2023)

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René Sedlak et al.

René Sedlak et al.


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Short summary
We show that machine learning can help classifying images of the OH* airglow, a thin layer in die 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, also turbulent wave breaking can be found. We use these observations of turbulence to derive the energy released by waves.