Extraction of spatially confined small-scale waves from high-resolution all-sky airglow images based on machine learning

  • Since June 2019, a scanning airglow camera is operated operationally every night at DLR Oberpfaffenhofen (48.09° N, 11.28° E), Germany. It provides nearly all-sky images (diameter 500 km) of the OH* airglow layer (height ca. 85–87 km) with an average spatial resolution of ca. 150 m and a temporal resolution of ca. 2 min. We analyse about three years (941 nights between October 2020 and September 2023) of OH* airglow all-sky images for spatially confined wave structures with horizontal wavelengths of ca. 20 km and less. Such structures are often referred to as ripples and are considered to be instability structures. However, Li et al. (2017) showed that they could also be secondary waves. While ripples move with the background wind, secondary waves do not. To identify small-scale and spatially confined structures, we adapt and train YOLOv7 (You Only Look Once, version 7), a machine learning approach, to determine their position and extent on the sky as well as their horizontalSince June 2019, a scanning airglow camera is operated operationally every night at DLR Oberpfaffenhofen (48.09° N, 11.28° E), Germany. It provides nearly all-sky images (diameter 500 km) of the OH* airglow layer (height ca. 85–87 km) with an average spatial resolution of ca. 150 m and a temporal resolution of ca. 2 min. We analyse about three years (941 nights between October 2020 and September 2023) of OH* airglow all-sky images for spatially confined wave structures with horizontal wavelengths of ca. 20 km and less. Such structures are often referred to as ripples and are considered to be instability structures. However, Li et al. (2017) showed that they could also be secondary waves. While ripples move with the background wind, secondary waves do not. To identify small-scale and spatially confined structures, we adapt and train YOLOv7 (You Only Look Once, version 7), a machine learning approach, to determine their position and extent on the sky as well as their horizontal wavelength. Those wavelengths are compared to two-dimensional FFT (Fast Fourier Transform) results. We analyse the seasonal variations in the orientation of the wave fronts, the direction of advection and the horizontal wavelengths of these structures and deduce that instability signatures are observed especially in summer. Finally, we introduce a concept for “operating-on-demand” in order to derive energy dissipation rates from our measurements.show moreshow less

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Metadaten
Author:Sabine Wüst, Jakob Strutz, Patrick Hannawald, Jonas Steffen, Rainer LienhartORCiDGND, Michael BittnerORCiDGND
URN:urn:nbn:de:bvb:384-opus4-1307482
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/130748
ISSN:1867-8548OPAC
Parent Title (English):Atmospheric Measurement Techniques
Publisher:Copernicus
Place of publication:Göttingen
Type:Article
Language:English
Date of first Publication:2026/05/29
Publishing Institution:Universität Augsburg
Release Date:2026/06/25
Volume:19
Issue:10
First Page:3539
Last Page:3556
DOI:https://doi.org/10.5194/amt-19-3539-2026
Institutes:Mathematisch-Naturwissenschaftlich-Technische Fakultät
Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Mathematisch-Naturwissenschaftlich-Technische Fakultät / Institut für Physik
Mathematisch-Naturwissenschaftlich-Technische Fakultät / Institut für Physik / Professur für Atmosphärenfernerkundung
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Maschinelles Lernen und Maschinelles Sehen
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
5 Naturwissenschaften und Mathematik / 53 Physik / 530 Physik
Licence (German):CC-BY 4.0: Creative Commons: Namensnennung