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

Related authors

Gravity wave instability structures and turbulence from more than 1.5 years of OH* airglow imager observations in Slovenia
René Sedlak, Patrick Hannawald, Carsten Schmidt, Sabine Wüst, Michael Bittner, and Samo Stanič
Atmos. Meas. Tech., 14, 6821–6833, https://doi.org/10.5194/amt-14-6821-2021,https://doi.org/10.5194/amt-14-6821-2021, 2021
Short summary
Intra-annual variations of spectrally resolved gravity wave activity in the upper mesosphere/lower thermosphere (UMLT) region
René Sedlak, Alexandra Zuhr, Carsten Schmidt, Sabine Wüst, Michael Bittner, Goderdzi G. Didebulidze, and Colin Price
Atmos. Meas. Tech., 13, 5117–5128, https://doi.org/10.5194/amt-13-5117-2020,https://doi.org/10.5194/amt-13-5117-2020, 2020
Short summary
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
Atmos. Meas. Tech., 12, 457–469, https://doi.org/10.5194/amt-12-457-2019,https://doi.org/10.5194/amt-12-457-2019, 2019
Short summary
High-resolution observations of small-scale gravity waves and turbulence features in the OH airglow layer
René Sedlak, Patrick Hannawald, Carsten Schmidt, Sabine Wüst, and Michael Bittner
Atmos. Meas. Tech., 9, 5955–5963, https://doi.org/10.5194/amt-9-5955-2016,https://doi.org/10.5194/amt-9-5955-2016, 2016
Short summary

Related subject area

Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Improving solution availability and temporal consistency of an optimal-estimation physical retrieval for ground-based thermodynamic boundary layer profiling
Bianca Adler, David D. Turner, Laura Bianco, Irina V. Djalalova, Timothy Myers, and James M. Wilczak
Atmos. Meas. Tech., 17, 6603–6624, https://doi.org/10.5194/amt-17-6603-2024,https://doi.org/10.5194/amt-17-6603-2024, 2024
Short summary
An improved geolocation methodology for spaceborne radar and lidar systems
Bernat Puigdomènech Treserras and Pavlos Kollias
Atmos. Meas. Tech., 17, 6301–6314, https://doi.org/10.5194/amt-17-6301-2024,https://doi.org/10.5194/amt-17-6301-2024, 2024
Short summary
Combining low- and high-frequency microwave radiometer measurements from the MOSAiC expedition for enhanced water vapour products
Andreas Walbröl, Hannes J. Griesche, Mario Mech, Susanne Crewell, and Kerstin Ebell
Atmos. Meas. Tech., 17, 6223–6245, https://doi.org/10.5194/amt-17-6223-2024,https://doi.org/10.5194/amt-17-6223-2024, 2024
Short summary
HAMSTER: Hyperspectral Albedo Maps dataset with high Spatial and TEmporal Resolution
Giulia Roccetti, Luca Bugliaro, Felix Gödde, Claudia Emde, Ulrich Hamann, Mihail Manev, Michael Fritz Sterzik, and Cedric Wehrum
Atmos. Meas. Tech., 17, 6025–6046, https://doi.org/10.5194/amt-17-6025-2024,https://doi.org/10.5194/amt-17-6025-2024, 2024
Short summary
Global-scale gravity wave analysis methodology for the ESA Earth Explorer 11 candidate CAIRT
Sebastian Rhode, Peter Preusse, Jörn Ungermann, Inna Polichtchouk, Kaoru Sato, Shingo Watanabe, Manfred Ern, Karlheinz Nogai, Björn-Martin Sinnhuber, and Martin Riese
Atmos. Meas. Tech., 17, 5785–5819, https://doi.org/10.5194/amt-17-5785-2024,https://doi.org/10.5194/amt-17-5785-2024, 2024
Short summary

Cited articles

Bai, S., Kolter, J. Z., and Koltun, V.: An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling, arXiv [preprint], https://arxiv.org/abs/1803.01271 (last access: 21 June 2023), 2018.  
Chau, J. L., Urco, J. M., Avsarkisov, V., Vierinen, J. P., Latteck, R., Hall, C. M., and Tsutsumi, M.: Four-Dimensional Quantification of Kelvin-Helmholtz Instabilities in the Polar Summer Mesosphere Using Volumetric Radar Imaging, Geophys. Ress. Let., 47, e2019GL086081, https://doi.org/10.1029/2019GL086081, 2020. 
Fujiyoshi, H., Hirakawa, T., and Yamashita, T.: Deep learning-based image recognition for autonomous driving, IATSS Research, 43, 244–252, https://doi.org/10.1016/j.iatssr.2019.11.008, 2019. 
Gargett, A. E.: Velcro Measurement of Turbulence Kinetic Energy Dissipation Rate ϵ, J. Atmos. Ocean. Tech., 16, 1973–1993, 1999. 
Guo, Z.-X., Yang, J.-Y., Dunlop, M. W., Cao, J.-B., Li, L.-Y., Ma, Y.-D., Ji, K.-F., Xiong, C., Li, J., and Ding, W.-T.: Automatic classification of mesoscale auroral forms using convolutional neural networks, J. Atmos. Terr. Phys., 235, 105906, https://doi.org/10.1016/j.jastp.2022.105906, 2022. 
Download
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.