Articles | Volume 2, issue 1
Wind Energ. Sci., 2, 77–95, 2017
https://doi.org/10.5194/wes-2-77-2017
Wind Energ. Sci., 2, 77–95, 2017
https://doi.org/10.5194/wes-2-77-2017

Research article 10 Feb 2017

Research article | 10 Feb 2017

An error reduction algorithm to improve lidar turbulence estimates for wind energy

Jennifer F. Newman and Andrew Clifton

Data sets

Facility-specific multi-level meteorological instrumentation (TWR) ARM (Atmospheric Radiation Measurement) https://www.arm.gov/capabilities/instruments/twr

Carbon dioxide flux measurement systems (CO2FLX) ARM (Atmospheric Radiation Measurement) https://www.arm.gov/capabilities/instruments/co2flx

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Short summary
Remote-sensing devices such as lidars are often used for wind energy studies. Lidars measure mean wind speeds accurately but measure different values of turbulence than an instrument on a tower. In this paper, a model is described that improves lidar turbulence estimates. The model can be applied to commercially available lidars in real time or post-processing. Results indicate that the model performs well under most atmospheric conditions but retains some errors under daytime conditions.