Improving Lidar Turbulence Estimates for Wind Energy: Preprint

Jennifer Newman, Matthew Churchfield, Petra Klein, Andrew Clifton

Research output: Contribution to conferencePaper


Remote sensing devices (e.g., lidars) are quickly becoming a cost-effective and reliable alternative to meteorological towers for wind energy applications. Although lidars can measure mean wind speeds accurately, these devices measure different values of turbulence intensity (TI) than an instrument on a tower. In response to these issues, a lidar TI error reduction model was recently developed for commercially available lidars. The TI error model first applies physics-based corrections to the lidar measurements, then uses machine-learning techniques to further reduce errors in lidar TI estimates. The model was tested at two sites in the Southern Plains where vertically profiling lidars were collocated with meteorological towers. Results indicate that the model works well under stable conditions but cannot fully mitigate the effects of variance contamination under unstable conditions. To understand how variance contamination affects lidar TI estimates, a new set of equations was derived in previous work to characterize the actual variance measured by a lidar. Terms in these equations were quantified using a lidar simulator and modeled wind field, and the new equations were then implemented into the TI error model.
Original languageAmerican English
Number of pages14
StatePublished - 2016
EventScience of Making Torque from Wind (TORQUE 2016) - Munich, Germany
Duration: 5 Oct 20167 Oct 2016


ConferenceScience of Making Torque from Wind (TORQUE 2016)
CityMunich, Germany

NREL Publication Number

  • NREL/CP-5000-66994


  • lidar
  • turbulence
  • turbulence intensity
  • wind energy


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