Articles | Volume 14, issue 5
https://doi.org/10.5194/amt-14-3501-2021
© Author(s) 2021. 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-14-3501-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 May 2021
Research article |
| 18 May 2021
| 18 May 2021
Eddies in motion: visualizing boundary-layer turbulence above an open boreal peatland using UAS thermal videos
Pavel Alekseychik
CORRESPONDING AUTHOR
Bioeconomy and Environment, Natural Resources Institute Finland,
00790 Helsinki, Finland
Institute for Atmospheric and Earth System Research/Physics,
Faculty of Science, University of Helsinki, P.O. Box 68, 00014 Helsinki, Finland
Gabriel Katul
Nicholas School of the Environment, Duke University, Durham, NC, USA
Department of Civil and Environmental Engineering, Duke University, Durham, NC, USA
Ilkka Korpela
Department of Forest Sciences, University of Helsinki, P.O. Box 27,
00014 Helsinki, Finland
Samuli Launiainen
Bioeconomy and Environment, Natural Resources Institute Finland,
00790 Helsinki, Finland
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Earth Syst. Sci. Data, 13, 3607–3689, https://doi.org/10.5194/essd-13-3607-2021, https://doi.org/10.5194/essd-13-3607-2021, 2021
Short summary
Short summary
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Cited articles
Adrian, R. J.: Hairpin vortex organization in wall turbulence, Phys.
Fluids, 19, 041301, https://doi.org/10.1063/1.2717527, 2007.
Albertson, J. D., Parlange, M. B., Katul, G. G., Chu, C., Stricker, H., and
Tyler, S.: Sensible heat flux from arid regions: A simple flux-variance
method, Water Resour. Res., 31, 969–973, 1995.
Alekseychik, P. K.: Codes to analyze atmospheric boundary layer turbulence based on thermal videos (Version 1.0), Zenodo [code], https://doi.org/10.5281/zenodo.4019155, 2020a.
Alekseychik, P. K.: Georeferenced drone thermal video and related data, recorded on 6 and 28 August 2019 in Siikaneva peatland (Version 1.0), Zenodo [data set], https://doi.org/10.5281/zenodo.4019321, 2020b.
Alekseychik, P. K.: Turbulence on natural peatland surface visualized using thermal videos (Version 1.0), Zenodo, https://doi.org/10.5281/zenodo.4019175, 2020c.
Alekseychik, P. K., Korrensalo, A., Mammarella, I., Vesala, T., and Tuittila,
E. S.: Relationship between aerodynamic roughness length and bulk sedge leaf
area index in a mixed-species boreal mire complex, Geophys. Res.
Lett., 44, 5836–5843, https://doi.org/10.1002/2017GL073884, 2017a.
Alekseychik, P., Mammarella, I., Karpov, D., Dengel, S., Terentieva, I., Sabrekov, A., Glagolev, M., and Lapshina, E.: Net ecosystem exchange and energy fluxes measured with the eddy covariance technique in a western Siberian bog, Atmos. Chem. Phys., 17, 9333–9345, https://doi.org/10.5194/acp-17-9333-2017, 2017b.
Christen, A. and Voogt, J.: Linking atmospheric turbulence and surfate temperature fluctuations in a street canyon, in: Proceedings of the Seventh International Conference on Urban Climate, 29 June–3 July 2009, Yokohama, Japan, 2009.
Christen, A., Meier, F., and Scherer, D.: High-frequency fluctuations of
surface temperatures in an urban environment, Theor. Appl. Climatol., 108,
301–324, 2012.
Chudnovsky, A., Ben-Dor, E., and Saaroni, H.: Diurnal thermal behavior of
selected urban objects using remote sensing measurements, Energ.
Buildings, 36, 1063–1074, https://doi.org/10.1016/j.enbuild.2004.01.052,
2004.
Davison, D. S.: The horizontal cross–sectional shape of convective plumes, Q.
J. Roy. Meteor. Soc., 101, 463–473, 1975.
Drobinski, P., Carlotti, P., Newsom, R. K., Banta, R. M., Foster, R. C., and
Redelsperger, J.: The structure of the near-neutral atmospheric surface
layer, J. Atmos. Sci., 61, 699–714, https://doi.org/10.1175/1520-0469(2004)061<0699:TSOTNA>2.0.CO;2, 2004.
Dugdale, S. J., Kelleher, C., Malcolm, I. A., Caldwell, S., and Hannah, D. M.: Assessing the potential of drone-based thermal infrared
imagery for quantifying river temperature heterogeneity, Hydrol. Process. 33, 1152–1163, https://doi.org/10.1002/hyp.13395, 2019.
Fang, J. and Porté-Agel, F.: Large-eddy simulation of very-large-scale
motions in the neutrally stratified atmospheric boundary layer,
Bound.-Lay. Meteorol., 155, 397–416, 2015.
Frisch, A. S. and Businger, J. A.: A Study of Convective Elements in
the Atmospheric Boundary Layer, Bound.-Lay. Meteorol., 3, 301–328, 1973.
Ganapathisubramani, B., Longmire, E. K., and Marusic, I.: Characteristics of
vortex packets in turbulent boundary layers, J. Fluid Mech. 478, 35–46,
2003.
Garai, A. and Kleissl, J.: Air and surface temperature coupling in the
convective atmospheric boundary layer, J. Atmos. Sci., 68, 2945–2954, 2011.
Garai, A. and Kleissl, J.: Interaction between coherent structures and
surface temperature and its effect on ground heat flux in an unstably
stratified boundary layer, J. Turbul., 14, 1–23,
https://doi.org/10.1080/14685248.2013.806812, 2013.
Gonzalez, R. C. and Woods, R. E.: Digital Image Processing, Addison-Wesley, New York, USA, 1992.
Guala, M., Hommema, S. E., and Adrian, R. J.: Large-scale and very-largescale
motions in turbulent pipe flow, J. Fluid Mech., 554, 521–542, 2006.
Hoyano, A., Asano, K., and Kanamaru, T.: Analysis of the sensible heat flux
from the exterior surface of buildings using time sequential thermography,
Atmos. Environ., 33, 3941–3951, 1999.
Inagaki, A., Kanda, M., Onomura, S., and Kumemura, H.: Thermal Image
Velocimetry, Bound.-Lay. Meteorol., 149, 1–18,
https://doi.org/10.1007/s10546-013-9832-z, 2013.
Jeong, J., Hussain, F., Schoppa, W., and Kim, J.: Coherent structure near the
wall in a turbulent channel flow, J. Fluid Mech., 332, 185–214, 1997.
Kaimal, J. C.: Translation Speed of Convective Plumes in the ASL, Q. J. Roy. Meteor. Soc., 100, 46–52 1974.
Kaimal, J. C. and Businger, J. A.: Case Studies of a Convective Plume and a
DustDevil, J. Appl. Meteorol., 9, 612–620, 1970.
Kaimal, J. C., Wyngaard, J. C., HAugusten, D. A., Cotè, O. R., Izumi,
Y., CAugusthy, S. J., and Readings, C. J.: Turbulence structure in
theconvective boundary layer, J. Atmos. Sci., 33, 2152–2169, 1976.
Katul, G. G., Schieldge, J., Hsieh, C. I., and Vidakovic, B.: Skin
temperature perturbations induced by surface layer turbulence above a grass
surface, Water Resour. Res., 34, 1265–1274, 1998.
Khalsa, J. S.: Surface-layer intermittency investigated with conditional
sampling, Bound.-Lay. Meteorol., 19, 135–153, 1980.
Kim, K. C. and Adrian, R. J.: Very large-scale motion in the outer layer,
Phys. Fluids, 11, 417, https://doi.org/10.1063/1.869889, 1999.
Kline, S. J., Reynolds, W. C., Schraub, R. A., and Runstadler, P. W.: The
structure of turbulent boundary layers, J. Fluid Mech., 30, 741–773, 1967.
Kormann, R. and Meixner, F. X.: An Analytical Footprint ModelFor Non-Neutral
Stratification, Bound.-Lay. Meteorol., 99, 207–224,
https://doi.org/10.1023/a:1018991015119, 2001.
Laima, S., Ren, H., Li, H., and Ou, J.: Numerical Simulation of Coherent
Structures in the Turbulent Boundary Layer under Diferent Stability
Conditions, Energies, 13, 1068, https://doi.org/10.3390/en13051068, 2020.
Margairaz, F., Pardyjak, E. R., and Calaf, M.: Surface thermal
heterogeneities and the atmospheric boundary layer: the relevance of
dispersive fluxes, Bound.-Lay. Meteorol, 175, 369–395, 2020.
Mattes, D., Haynor, D. R., Vesselle, H., Lewellen, T., and Eubank. W.
Non-rigid multimodality image registration, in: Proceedings of SPIE 4322,
Medical Imaging 2001: Image Processing, 3 July 2001, San Diego, CA, USA,
1609–1620, https://doi.org/10.1117/12.431046, 2001.
Meier, F., Scherer, D., Richters, J., and Christen, A.: Atmospheric correction of thermal-infrared imagery of the 3-D urban environment acquired in oblique viewing geometry, Atmos. Meas. Tech., 4, 909–922, https://doi.org/10.5194/amt-4-909-2011, 2011.
Morrison, T. J., Calaf, M., Fernando, H. J. S., Price, T. A., and Pardyjak, E. R.:
A methodology for computing spatiallyand temporally varying surface sensible
heat flux from thermal imagery, Q. J. Roy. Meteor. Soc., 143, 2616–2624,
https://doi.org/10.1002/qj.3112, 2017.
Newsom, R., Calhoun, R., Ligon, D., and Allwine, J.: Linearly Organized Turbulence Structures Observed Over a Suburban Area by Dual-Doppler Lidar, Bound.-Lay. Meteorol., 127, 111–130, 2008.
Owen, P. R. and Thompson, W. R.: Heat Transfer Across Rough Surfaces, J.
Fluid Mech., 15, 321–334, 1963.
Paw U, K. T., Qiu, J., Su, H. B., Watanabe, T., and Brunet, Y.: Surface renewal analysis: a new method to obtain scalar ?uxes without velocity data,
Agr. Forest Meteorol., 74, 119–137, 1995.
Priestley, C. H. B.: Turbulent Transfer in the Lower Atmosphere, University
of Chicago Press, Chicago, USA, 130 pp., 1959.
Renieblas, G. P., Nogués, A. T., González, A. M., Gómez-Leon, N., and del Castillo, E. G.: Structural similarity index family for image
quality assessment in radiological images, J. Med. Imag., 4, 035501, https://doi.org/10.1117/1.JMI.4.3.035501, 2017.
Salesky, S. T., Katul, G. G., and Chamecki, M.: Buoyancy effects on the
integral lengthscales and mean velocity profile in atmospheric surface layer
flows, Phys. Fluids, 25, 105101, https://doi.org/10.1063/1.4823747, 2013.
Stull, R. B.: An Introduction to Boundary Layer Meteorology, in: Atmospheric Sciences Library, Springer, the Netherlands, https://doi.org/10.1007/978-94-009-3027-8, 2011.
Styner, M., C. Brechbuehler, G. Székely, and G. Gerig: Parametric
estimate of intensity inhomogeneities applied to MRI, IEEE T.
Med. Imaging, 19, 153–165, 2000.
Sugawara, H., Narita, K., and Mikami, T.: Estimation of effective thermal
property parameter on a heterogeneous urban surface, J. Meteorol. Soc. Jpn., 79, 1169–1181, 2001.
Taylor, R. J.: Thermal Structures in the Lowest Layers of the Atmosphere,
Aust. J. Phys. 11, 168–176, 1958.
Thielicke, W. and Stamhuis, E. J.: PIVlab – Towards User-Friendly,
Affordable and Accurate Digital Particle Image Velocimetry in MATLAB, J.
Open Res. Software, 2, e30, https://doi.org/10.5334/jors.bl, 2014.
Tomkins, C. D. and Adrian, R. J.: Spanwise structure and scale growth in
turbulent boundary layers, J. Fluid Mech., 490, 37–74, 2003.
Vogt, R.: Visualisation of turbulent exchange using a thermal camera, 18th
symposium on boundary layer and turbulence, 9–13 June 2008, Stockholm, Sweden, Paper no. 8B.1, 2008.
Webb, E. K.: Convection mechanisms of atmospheric heat transfer from surface
to global scales, in: Proc. Second Australasian Conf. on Heat and Mass
Transfer, February 1977, Sydney, Australia, NTIS 77N30685/2, 523–539, 1977.
Wilczak, J. M. and Tillman, J. E.: The three-dimensional structure of
convection in the atmospheric surface layer, J. Atmos. Sci., 37, 2424–2443,
1980.
Short summary
Drones with thermal cameras are powerful new tools with the potential to provide new insights into atmospheric turbulence and heat fluxes. In a pioneering experiment, a Matrice 210 drone with a Zenmuse XT2 thermal camera was used to record 10–20 min thermal videos at 500 m a.g.l. over the Siikaneva peatland in southern Finland. A method to visualize the turbulent structures and derive their parameters from thermal videos is developed. The study provides a novel approach for turbulence analysis.
Drones with thermal cameras are powerful new tools with the potential to provide new insights...
