Articles | Volume 14, issue 2
https://doi.org/10.5194/amt-14-1457-2021
https://doi.org/10.5194/amt-14-1457-2021
Research article
 | 
24 Feb 2021
Research article |  | 24 Feb 2021

Spectral correction of turbulent energy damping on wind lidar measurements due to spatial averaging

Matteo Puccioni and Giacomo Valerio Iungo

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Cited articles

Baars, W. J. and Marusic, I.: Data-driven decomposition of the streamwise turbulence kinetic energy in boundary layers. Part 2. Integrated energy and A1, J. Fluid Mech., 882, A26, https://doi.org/10.1017/jfm.2019.835, 2020. a
Balasubramaniam, B. J.: Nature of turbulence in wall bounded flows, PhD thesis, University of Illinois at Urbana-Champaign, 2005. a, b
Banakh, V. A. and Werner C.: Computer simulation of coherent Doppler lidar measurement of wind velocity and retrieval of turbulent wind statistics, Opt. Eng., 44, 071205, https://doi.org/10.1117/1.1955167, 2005. a, b
Banerjee, T., Katul, G. G., Salesky, S. T., and Chamecki, M.: Revisiting the formulations for the longitudinal velocity variance in the unstable atmospheric surface layer, Q. J. Roy. Meteor. Soc., 141, 1699–1711, https://doi.org/10.1002/qj.2472, 2015. a
Bodini, N., Zardi, D., and Lundquist, J. K.: Three-dimensional structure of wind turbine wakes as measured by scanning lidar, Atmos. Meas. Tech., 10, 2881–2896, https://doi.org/10.5194/amt-10-2881-2017, 2017. a
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Short summary
A procedure for correcting the turbulent-energy damping connected with spatial averaging of wind lidars is proposed. This effect of the lidar measuring process is modeled through a low-pass filter, whose order and cut-off frequency are estimated directly from the lidar data. The proposed procedure is first assessed through simultaneous and colocated lidar and sonic-anemometer measurements. Then it is applied to several datasets collected at sites with different terrain roughness.
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