Articles | Volume 14, issue 12
https://doi.org/10.5194/amt-14-7475-2021
https://doi.org/10.5194/amt-14-7475-2021
Research article
 | 
01 Dec 2021
Research article |  | 01 Dec 2021

Use of thermal signal for the investigation of near-surface turbulence

Matthias Zeeman

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Cited articles

Abram, C., Fond, B., Heyes, A. L., and Beyrau, F.: High-speed planar thermometry and velocimetry using thermographic phosphor particles, Appl. Phys. B, 111, 155–160, https://doi.org/10.1007/s00340-013-5411-8, 2013. a
Anaconda: Anaconda Software Distribution, Version 4–4.8.3, available at: https://anaconda.com (last access: 14 April 2021), 2020. a
Antonia, R. A., Chambers, A. J., Friehe, C. A., and Atta, C. W. V.: Temperature Ramps in the Atmospheric Surface Layer, J. Atmos. Sci., 36, 99–108, https://doi.org/10.1175/1520-0469(1979)036<0099:tritas>2.0.co;2, 1979. a
Aubinet, M., Vesala, T., and Papale, D. (Eds.): Eddy Covariance: A Practical Guide to Measurement and Data Analysis, Springer, https://doi.org/10.1007/978-94-007-2351-1, 2012. a
Bou-Zeid, E., Anderson, W., Katul, G. G., and Mahrt, L.: The Persistent Challenge of Surface Heterogeneity in Boundary-Layer Meteorology: A Review, Bound.-Lay. Meteorol., 177, 227–245, https://doi.org/10.1007/s10546-020-00551-8, 2020. a
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Short summary
Understanding turbulence near the surface is important for many applications. In this work, methods for observing and analysing temperature structures in a near-surface volume were explored. Experiments were conducted to identify modes of organised motion. These help explain interactions between the vegetation and the atmosphere that are not currently well understood. Techniques used include fibre-optic sensing, thermal infrared imaging, signal decomposition, and machine learning.