Articles | Volume 2, issue 1
https://doi.org/10.5194/wes-2-77-2017
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

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Cited articles

ARM (Atmospheric Radiation Measurement): Climate Research Facility, updated daily, Facility-specific multi-level meteorological instrumentation (TWR), Nov. 2012–Jun. 2013, 36°36′18.0′′ N, 97°29′6.0′′ W: Southern Great Plains Central Facility (C1), compiled by: Cook, D. and Kyrouac, J., ARM Data Archive: Oak Ridge, Tennessee, USA, available at: https://www.arm.gov/capabilities/instruments/twr (last access: 11 April 2013), 1993.
ARM (Atmospheric Radiation Measurement): Climate Research Facility, updated daily, Carbon dioxide flux measurement systems (CO2FLX), Nov. 2012–Jun. 2013, 36°36′18.0′′ N, 97°29′6.0′′ W: Southern Great Plains Central Facility (C1), compiled by: Billesbach, D., Biraud, S., and Chan, S., ARM Data Archive: Oak Ridge, Tennessee, USA, available at: https://www.arm.gov/capabilities/instruments/co2flx (last access: 11 April 2013), 2011.
Arya, S. P.: Introduction to Micrometeorology, Academic Press, Cornwall, UK, 2nd Edn., Int. Geophys. Ser., 79, 101–108, 2001.
Barthelmie, R. J., Crippa, P., Wang, H., Smith, C. M., Krishnamurthy, R., Choukulkar, A., Calhoun, R., Valyou, D., Marzocca, P., Matthiesen, D., Brown, G., and Pryor, S. C.: 3D wind and turbulence characteristics of the atmospheric boundary layer, Bull. Amer. Meteor. Soc., 95, 743–756, https://doi.org/10.1175/BAMS-D-12-00111.1, 2013.
Bodine, D., Klein, P. M., Arms, S. C., and Shapiro, A.: Variability of surface air temperature over gently sloped terrain, J. Appl. Meteor. Climatol., 48, 1117–1141, 2009.
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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.
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