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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 Jun 2023
Research article |
| 26 Jun 2023
| 26 Jun 2023
Analysis of 2D airglow imager data with respect to dynamics using machine learning
René Sedlak
Institute of Physics, University of Augsburg, Augsburg, Germany
Andreas Welscher
Institute of Physics, University of Augsburg, Augsburg, Germany
German Remote Sensing Data Center, German Aerospace Center,
Oberpfaffenhofen, Germany
Patrick Hannawald
German Remote Sensing Data Center, German Aerospace Center,
Oberpfaffenhofen, Germany
German Remote Sensing Data Center, German Aerospace Center,
Oberpfaffenhofen, Germany
Rainer Lienhart
Institute of Computer Science, University of Augsburg, Augsburg,
Germany
Michael Bittner
Institute of Physics, University of Augsburg, Augsburg, Germany
German Remote Sensing Data Center, German Aerospace Center,
Oberpfaffenhofen, Germany
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Two identical instruments in a parallel setup were used to observe the mesospheric OH airglow for more than 10 years (2009–2020) at 47.42°N, 10.98°E. This allows unique analyses of data quality aspects and their impact on the obtained results. During solar cycle 24 the influence of the sun was strong (∼6 K per 100 sfu). A quasi-2-year oscillation (QBO) of ±1 K is observed mainly during the maximum of the solar cycle. Unlike the stratospheric QBO the variation has a period of or below 24 months.
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Ground-based OH* airglow measurements have been carried out for almost 100 years. Advanced detector technology has greatly simplified the automatic operation of OH* airglow observing instruments and significantly improved the temporal and/or spatial resolution. Studies based on long-term measurements or including a network of instruments are reviewed, especially in the context of deriving gravity wave properties. Scientific and technical challenges for the next few years are described.
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High-resolution images of the OH* airglow layer (ca. 87 km height) acquired at Otlica Observatory, Slovenia, have been analysed. A statistical analysis of small-scale wave structures with horizontal wavelengths up to 4.5 km suggests strong presence of instability features in the upper mesosphere or lower thermosphere. The dissipated energy of breaking gravity waves is derived from observations of turbulent vortices. It is concluded that dynamical heating plays a vital role in the atmosphere.
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With airglow spectrometers, the temperature in the upper mesosphere/lower thermosphere can be derived each night. The data allow to estimate the amount of energy which is transported by small-scale atmospheric waves, known as gravity waves. In order to do this, information about the Brunt–Väisälä frequency and its evolution during the year is necessary. This is provided here for low and midlatitudes based on 18 years of satellite data.
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
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Gravity wave (GW) activity in the UMLT in the period range 6-480 min is calculated by applying a wavelet analysis to nocturnal temperature time series derived from OH* airglow spectrometers. We analyse measurements from eight different locations at different latitudes.
GW activity shows strong period dependence. We find hardly any seasonal variability for periods below 60 min and a semi-annual cycle for periods longer than 60 min that evolves into an annual cycle around a period of 200 min.
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.
Hannawald, P., Schmidt, C., Wüst, S., and Bittner, M.: A fast SWIR imager for observations of transient features in OH airglow, Atmos. Meas. Tech., 9, 1461–1472, https://doi.org/10.5194/amt-9-1461-2016, 2016.
Hannawald, P., Schmidt, C., Sedlak, R., Wüst, S., and Bittner, M.: Seasonal and intra-diurnal variability of small-scale gravity waves in OH airglow at two Alpine stations, Atmos. Meas. Tech., 12, 457–469, https://doi.org/10.5194/amt-12-457-2019, 2019.
Hecht, J. H., Wan, K., Gelinas, L. J., Fritts, D. C., Walterscheid, R. L.,
Rudy, R. J., Liu, A. Z., Franke, S. J., Vargas, F. A., Pautet, P. D.,
Taylor, M. J., and Swenson, G. R.: The life cycle of instability features
measured from the Andes Lidar Observatory over Cerro Pachon on 24 March
2012, J. Geophys. Res.-Atmos., 119, 8872–8898, 2014.
Hecht, J. H., Fritts, D. C., Gelinas, L. J., Rudy, R. J., Walterscheid, R.
L., and Liu, A. Z.: Kelvin-Helmholtz Billow Interactions and Instabilities
in the Mesosphere Over the Andes Lidar Observatory: 1. Observations, J.
Geophys. Res.-Atmos., 126, e2020JD033414,
https://doi.org/10.1029/2020JD033414, 2021.
Hocking, W. K.: Measurement of turbulent energy dissipation rates in the
middle atmosphere by radar techniques: A review, Radio Sci., 20, 1403–1422,
1985.
Hocking, W. K.: The dynamical parameters of turbulence theory as they apply
to middle atmosphere studies, Earth Planets Space, 51, 525–541, 1999.
Horak, K. and Sablatnig, R.: Deep learning concepts and datasets for image
recognition: overview 2019, Proc. SPIE 11179, Eleventh International
Conference on Digital Image Processing (ICDIP 2019), 111791S, 14 August
2019, https://doi.org/10.1117/12.2539806, 2019.
Kingma, D. P. and Ba, J.: Adam: A Method for Stochastic Optimization, arXiv [preprint],
https://arxiv.org/abs/1803.01271 (last access: 21 June 2023), 2014.
Kolmogorov, A.: The local structure of turbulence in incompressible viscous fluid for very large Reynolds numbers, P. R. Soc. A, 434, 9–13, https://doi.org/10.1098/rspa.1991.0075, 1991.
Leinert, C., Bowyer, S., Haikala, L. K., Hanner, M. S., Hauser, M. G.,
Levasseur-Regourd, A.-C., Mann, I., Mattila, K., Reach, W. T., Schlosser,
W., Staude, H. J., Toller, G. N., Weiland, J. L., Weinberg, J. L., and Witt,
A. N.: The 1997 reference of diffuse night sky brightness, Astron.
Astrophys. Sup., 127, 1–99, 1998.
Li, J., Li, T., Dou, X., Fang, X., Cao, B., She, C.-Y., Nakamura, T.,
Manson, A., Meek, C., and Thorsen, D.: Characteristics of ripple structures
revealed in OH airglow images, J. Geophys. Res.-Space, 122, 3748–3759,
https://doi.org/10.1002/2016JA023538, 2017.
Marsh, D. R.: Chemical-Dynamical Coupling in the Mesosphere and Lower
Thermosphere, Aeronomy of the Earth's Atmosphere and Ionosphere, IAGA
Special Sopron Book Series 2, edited by: Abdu, M. A. and Pancheva, D., Springer Science + Business Media B. V., Dordrecht, the
Netherlands, https://doi.org/10.1007/978-94-007-0326-1_1,
2011.
Murphy, K. P.: Machine Learning: A Probabilistic Perspective, The MIT
Press, 57, ISBN 978-0262018020, 2012.
Nair, V. and Hinton, G.: Rectified linear units improve restricted
Boltzmann machines, Proceedings of International Conference on Machine
Learning, 27, 807–814, 2010.
Nakamura, T., Higashikawa, A., Tsuda, T., and Matsuhita, Y.: Seasonal
variations of gravity wave structures in OH airglow with a CCD imager at
Shigaraki, Earth Planets Space, 51, 897–906, 1999.
Pautet, P. D., Taylor, M. J., Pendleton, W. R., Zhao, Y., Yuan, T., Esplin,
R., and McLain, D.: Advanced mesospheric temperature mapper for
high-latitude airglow studies, Appl. Opt., 53, 5934–5943, 2014.
Peterson, A. W.: Airglow events visible to the naked eye, Appl. Optics, 18,
3390–3393, https://doi.org/10.1364/AO.18.003390, 1979.
Rémy, P.: Temporal Convolutional Networks for Keras, GitHub, https://github.com/philipperemy/keras-tcn (last access: 21 June 2023), 2020.
Rousselot, P., Lidman, C., Cuby, J.-G., Moreels, G., und Monnet, G.: Night-sky
spectral atlas of OH emission lines in the near-infrared, Astron.
Astrophys., 354, 1134–1150, 1999.
Salimans, T. and Kingma, D. P.: Weight normalization: A simple
reparameterization to accelerate training of deep neural networks, Curran
Associates, Inc., 29, 901–909, 2016.
Sedlak, R., Hannawald, P., Schmidt, C., Wüst, S., and Bittner, M.: High-resolution observations of small-scale gravity waves and turbulence features in the OH airglow layer, Atmos. Meas. Tech., 9, 5955–5963, https://doi.org/10.5194/amt-9-5955-2016, 2016.
Sedlak, R., Hannawald, P., Schmidt, C., Wüst, S., Bittner, M., and Stanic, S.: Gravity wave instability structures and turbulence from more than 1.5 years of OH* airglow imager observations in Slovenia, Atmos. Meas. Tech., 14, 6821–6833, https://doi.org/10.5194/amt-14-6821-2021, 2021.
Sedlak, R., Hannawald, P., Wüst, S., and Bittner, M.: Two-dimensional image data of FAIM 3 (Fast Airglow IMager) operated at the German Aerospace Center (DLR) site at Oberpfaffenhofen, Germany, 11 June 2019 to 25 February 2020, WDC-RSAT [data set], https://www.wdc.dlr.de/, last access: 23 June 2023a.
Sedlak, R., Welscher, A., Wüst, S., and Bittner, M.: 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, Logo TIB AV-Portal [video], https://doi.org/10.5446/60729, 2023b.
Smith, L. N.: Cyclical Learning Rates for Training Neural Networks, IEEE
Winter Conference on Applications of Computer Vision (WACV), 464–472,
https://doi.org/10.1109/WACV.2017.58, 2017.
Srivastava, N., Hinton, G. E., Krizhevsky, A., Sutskever, I., and
Salakhutdinov, R.: Dropout: A simple way to prevent neural networks from
overfitting, J. Mach. Learn. Res., 15, 1929–1958, 2014.
Taylor, M. J.: A review of advances in imaging techniques for measuring
short period gravity waves in the mesosphere and lower thermosphere, Adv.
Space Res., 19, 667–676, 1997.
Taylor, M. J. and Hapgood, M. A.: On the origin of ripple-type wave
structure in the OH nightglow emission, Planet. Space Sci., 38, 1421–1430,
1990.
von Savigny, C.: Variability of OH(3-1) emission altitude from 2003 to 2011:
Long-term stability and universality of the emission rate-altitude
relationship, J. Atmos. Sol.-Terr. Phy., 127, 120–128,
https://doi.org/10.1016/j.jastp.2015.02.001, 2015.
Wüst, S., Bittner, M., Yee, J.-H., Mlynczak, M. G., and Russell III, J. M.: Variability of the Brunt–Väisälä frequency at the OH* layer height, Atmos. Meas. Tech., 10, 4895–4903, https://doi.org/10.5194/amt-10-4895-2017, 2017.
Wüst, S., Schmidt, C., Hannawald, P., Bittner, M., Mlynczak, M. G., and Russell III, J. M.: Observations of OH airglow from ground, aircraft, and satellite: investigation of wave-like structures before a minor stratospheric warming, Atmos. Chem. Phys., 19, 6401–6418, https://doi.org/10.5194/acp-19-6401-2019, 2019.
Wüst, S., Bittner, M., Yee, J.-H., Mlynczak, M. G., and Russell III, J. M.: Variability of the Brunt–Väisälä frequency at the OH*-airglow layer height at low and midlatitudes, Atmos. Meas. Tech., 13, 6067–6093, https://doi.org/10.5194/amt-13-6067-2020, 2020.
Wüst, S., Bittner, M., Espy, P. J., French, W. J. R., and Mulligan, F. J.: Hydroxyl airglow observations for investigating atmospheric dynamics: results and challenges, Atmos. Chem. Phys., 23, 1599–1618, https://doi.org/10.5194/acp-23-1599-2023, 2023.
Zhou, J., Guo, R. Y., Sun, M., Di, T. T., Wang, S., Zhai, J., and Zhao, Z.: The Effects of GLCM parameters on LAI
estimation using texture values from Quickbird Satellite Imagery, Sci. Rep., 7,
7366, https://doi.org/10.1038/s41598-017-07951-w, 2017.
Zubair, A. R. and Alo, S.: Grey Level Co-occurrence Matrix (GLCM) Based
Second Order Statistics for Image Texture Analysis, International Journal of
Science and Engineering Investigations, 8, 64–73, 2019.
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,
dynamicepisodes 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.
We show that machine learning can help in classifying images of the OH* airglow, a thin layer in...
