Lidar are potentially very useful tools for the validation of flow models, with or without turbine wakes, in simple or complex flows [1]-[4]. The challenge of measurements in complex flow was explored in the first phase of Task 32, but there was no opportunity to look at how the data were then used. Therefore, Task 32 will look into how measurement methods can be optimized to the data that are required for model validation, and vice-versa.
The development of lidar techniques can be viewed as progress from 1st generation methods, typified by met masts and remote sensing profilers, which acquired datasets from which wind conditions could be extrapolated vertically and horizontally, to 2nd generation devices such as scanning lidars, which acquired datasets from which wind conditions could be inferred in horizontally and vertically disparate locations from the measured line of sight radial velocities and sophisticated flow models as described above, to 3rd generation techniques where wind conditions are directly observed throughout the volume of interest. 3rd generation capabilities are beginning to be exhibited by some systems that combing multiple scanning lidars to implement convergent scan geometries. These allow wind velocity vectors to be fully characterized rather than inferred at arbitrary points within the volume of interest. However, trade-offs between space- and time-resolution are still required as the multiple points necessary for high space resolution would incur a scanning time overhead that reduces the time resolution. Nevertheless, convergent scan geometries have been seen to be the only lidar method to accurately replicate the performance of met masts in relation to the measurement of turbulence intensity (TI) [5].


The further development of well-documented lidar use cases will be undertaken to ensure that lidar methods that are fit-for-purpose can be applied with the consistency on which investor confidence relies [6], including the development of common protocols for recording and describing lidar measurement configurations and scan geometries.
The extension of Task 32 will give the possibility to gather at the same table experts from the lidar and the wind modeling community, in order to precisely define which parameters should be extracted from the lidar measurements and the proper methodology to obtain them. Moreover, the results could provide a valuable support to the joint activity of IEA Wind Task 31 and 30 in which a benchmark of wake and full system simulations on the basis of lidar measurements is attempted.


  • Understand the needs of measurements of complex flow in wind energy and describe the limitations of lidar systems to provide recommendations for adjustments.
  • Find metrics to compare flow simulations and lidar field measurements: Related to IEA Wind Task 31.



  1. K. Lundquist, M. J. Churchfield, S. Lee, and A. Clifton, “Quantifying error of lidar and sodar Doppler beam swinging measurements of wind turbine wakes using computational fluid dynamics”, Atmospheric Measurement Techniques, vol. 8, no. 2, p. 907-920, 2015.
  2. L. Aitken, R. M. Banta, Y. L. Pichugina and J. K. Lundquist, “Quantifying Wind Turbine Wake Characteristics from Scanning Remote Sensor Data”, Journal of Atmospheric and Oceanic Technology, American Meteorological Society, vol. 31, no. 4, p. 765-787, 2014.
  3. Machefaux, G. C. Larsen, N. Troldborg, M. Gaunaa and A. Rettenmeier, “Empirical modeling of single-wake advection and expansion using full-scale pulsed lidar-based measurements”, Wind Energy, Wiley, DOI: 10.1002/we.1805, 2014.
  4. Beck, D. Trabucchi, M. Bitter and M. Kühn, “The Ainslie wake model – an update for multi megawatt turbines based on state-of-the-art wake scanning techniques”, Presentation at EWEA Conference, Barcelona, Spain, 2014.
  5. J.M Clive, “Lidar observations of the compression zone and capabilities as a turbulence instrument”, Proceedings of the 9th Meeting of the Power Curve Working Group, Glasgow, 16 December 2014.
  6. J.M. Clive “Lidar use cases for the acquisition of high value datasets”, EWEA Offshore, 2015.