Articles | Volume 15, issue 19
https://doi.org/10.5194/amt-15-5681-2022
https://doi.org/10.5194/amt-15-5681-2022
Research article
 | 
12 Oct 2022
Research article |  | 12 Oct 2022

Adaptive thermal image velocimetry of spatial wind movement on landscapes using near-target infrared cameras

Benjamin Schumacher, Marwan Katurji, Jiawei Zhang, Peyman Zawar-Reza, Benjamin Adams, and Matthias Zeeman

Related authors

The application and modification of WRF-Hydro/Glacier to a cold-based Antarctic glacier
Tamara Pletzer, Jonathan P. Conway, Nicolas J. Cullen, Trude Eidhammer, and Marwan Katurji
Hydrol. Earth Syst. Sci., 28, 459–478, https://doi.org/10.5194/hess-28-459-2024,https://doi.org/10.5194/hess-28-459-2024, 2024
Short summary
GEO4PALM v1.1: an open-source geospatial data processing toolkit for the PALM model system
Dongqi Lin, Jiawei Zhang, Basit Khan, Marwan Katurji, and Laura E. Revell
Geosci. Model Dev., 17, 815–845, https://doi.org/10.5194/gmd-17-815-2024,https://doi.org/10.5194/gmd-17-815-2024, 2024
Short summary
Investigating multiscale meteorological controls and impact of soil moisture heterogeneity on radiation fog in complex terrain using semi-idealised simulations
Dongqi Lin, Marwan Katurji, Laura E. Revell, Basit Khan, and Andrew Sturman
Atmos. Chem. Phys., 23, 14451–14479, https://doi.org/10.5194/acp-23-14451-2023,https://doi.org/10.5194/acp-23-14451-2023, 2023
Short summary
The surface energy balance during foehn events at Joyce Glacier, McMurdo Dry Valleys, Antarctica
Marte G. Hofsteenge, Nicolas J. Cullen, Carleen H. Reijmer, Michiel van den Broeke, Marwan Katurji, and John F. Orwin
The Cryosphere, 16, 5041–5059, https://doi.org/10.5194/tc-16-5041-2022,https://doi.org/10.5194/tc-16-5041-2022, 2022
Short summary
Use of thermal signal for the investigation of near-surface turbulence
Matthias Zeeman
Atmos. Meas. Tech., 14, 7475–7493, https://doi.org/10.5194/amt-14-7475-2021,https://doi.org/10.5194/amt-14-7475-2021, 2021
Short summary

Related subject area

Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Assessing atmospheric gravity wave spectra in the presence of observational gaps
Mohamed Mossad, Irina Strelnikova, Robin Wing, and Gerd Baumgarten
Atmos. Meas. Tech., 17, 783–799, https://doi.org/10.5194/amt-17-783-2024,https://doi.org/10.5194/amt-17-783-2024, 2024
Short summary
Joint 1DVar retrievals of tropospheric temperature and water vapor from Global Navigation Satellite System radio occultation (GNSS-RO) and microwave radiometer observations
Kuo-Nung Wang, Chi O. Ao, Mary G. Morris, George A. Hajj, Marcin J. Kurowski, Francis J. Turk, and Angelyn W. Moore
Atmos. Meas. Tech., 17, 583–599, https://doi.org/10.5194/amt-17-583-2024,https://doi.org/10.5194/amt-17-583-2024, 2024
Short summary
Mispointing characterization and Doppler velocity correction for the conically scanning WIVERN Doppler radar
Filippo Emilio Scarsi, Alessandro Battaglia, Frederic Tridon, Paolo Martire, Ranvir Dhillon, and Anthony Illingworth
Atmos. Meas. Tech., 17, 499–514, https://doi.org/10.5194/amt-17-499-2024,https://doi.org/10.5194/amt-17-499-2024, 2024
Short summary
Radar and environment-based hail damage estimates using machine learning
Luis Ackermann, Joshua Soderholm, Alain Protat, Rhys Whitley, Lisa Ye, and Nina Ridder
Atmos. Meas. Tech., 17, 407–422, https://doi.org/10.5194/amt-17-407-2024,https://doi.org/10.5194/amt-17-407-2024, 2024
Short summary
A new power-law model for μ–Λ relationships in convective and stratiform rainfall
Christos Gatidis, Marc Schleiss, and Christine Unal
Atmos. Meas. Tech., 17, 235–245, https://doi.org/10.5194/amt-17-235-2024,https://doi.org/10.5194/amt-17-235-2024, 2024
Short summary

Cited articles

Adrian, R., Meinhart, C., and Tomkins, C.: Vortex organization in the outer region of the turbulent boundary layer, J. Fluid Mech., 422, 1–54, https://doi.org/10.1017/S0022112000001580, 2000. a
Alekseychik, P., Katul, G., Korpela, I., and Launiainen, S.: Eddies in motion: visualizing boundary-layer turbulence above an open boreal peatland using UAS thermal videos, Atmos. Meas. Tech., 14, 3501–3521, https://doi.org/10.5194/amt-14-3501-2021, 2021. a
Barthlott, C., Drobinski, P., Fesquet, C., Dubos, T., and Pietras, C.: Long-term study of coherent structures in the atmospheric surface layer, Bound.-Lay. Meteorol., 125, 1–24, https://doi.org/10.1007/s10546-007-9190-9, 2007. a
Bastiaanssen, W., Menenti, M., Feddes, R., and Holtslag, A.: A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation, J. Hydrol., 212–213, 198–212, https://doi.org/10.1016/s0022-1694(98)00253-4, 1998. a
Blender Online Community: Blender – a 3D modelling and rendering package, Blender Foundation, Blender Institute, Amsterdam, http://www.blender.org (last access: 24 June 2022), 2019. a
Download
Short summary
This investigation presents adaptive thermal image velocimetry (A-TIV), a newly developed algorithm to spatially measure near-surface atmospheric velocities using an infrared camera mounted on uncrewed aerial vehicles. A validation and accuracy assessment of the retrieved velocity fields shows the successful application of the algorithm over short-cut grass and turf surfaces in dry conditions. This provides new opportunities for atmospheric scientists to study surface–atmosphere interactions.