• Albers, A., , and Janssen A. W. , 2008: Evaluation of Windcube. Deutsche WindGuard Consulting GmbH, Internal Project VC 08007, Rep. PP 08007, 29 pp. [Available online at http://www.leosphere.com/file/deusche_windguard_report_windcube_evaluation.pdf.]

  • Andreas, E. L, 1988: Estimating over snow and sea ice from meteorological data. J. Opt. Soc. Amer., 5A, 481495.

  • Charles, L. A., , Chaw S. , , Vladutescu V. , , Wu Y. , , Moshary F. , , Gross B. , , Gedzelman S. D. , , and Ahmed S. , 2007: Application of CCNY lidar and ceilometers to the study of aerosol transport and PM2.5 monitoring. Extended Abstracts, Third Symp. on Lidar Atmospheric Applications, San Antonio, TX, Amer. Meteor. Soc., P1.14. [Available online at http://ams.confex.com/ams/87ANNUAL/techprogram/paper_119919.htm.]

  • Foote, G. B., , and du Toit P. S. , 1969: Terminal velocity of raindrops aloft. J. Appl. Meteor., 8, 249253.

  • Frehlich, R., 1996: Simulation of coherent Doppler lidar performance in the weak-signal regime. J. Atmos. Oceanic Technol., 13, 646658.

    • Search Google Scholar
    • Export Citation
  • Fujii, T., , and Fukuchi T. , 2005: Laser Remote Sensing. Taylor & Francis Group, 912 pp.

  • Gomes, L., , Mallet M. , , Roger J. C. , , and Dubuisson P. , 2008: Effects of the physical and optical properties of urban aerosols measured during the CAPITOUL summer campaign on the local direct radiative forcing. Meteor. Atmos. Phys., 102 (3–4), 289306.

    • Search Google Scholar
    • Export Citation
  • Gottschall, J., , and Courtney M. , 2010: Verification test for three WindCubeTM WLS7 LiDARs at the Høvsøre test site. Risø-R-Rep. Risø-R-1732(EN), 43 pp. [Available online at http://130.226.56.153/rispubl/reports/ris-r-1732.pdf.]

  • Im, J.-S., , Saxena V. K. , , and Wenny B. N. , 2001: An assessment of hygroscopic growth factors for aerosols in the surface boundary layer for computing direct radiative forcing. J. Geophys. Res., 106 (D17), 20 21320 224.

    • Search Google Scholar
    • Export Citation
  • Lawson, J. K., , and Carrano C. J. , 2006: Using historic models of to predict r0 and regimes affected by atmospheric turbulence for horizontal, slant, and topological paths. Atmospheric Optical Modeling, Measurement, and Simulation II, S. M. Hammel and A. Kohnle, Eds., International Society for Optical Engineering (SPIE Proceedings, Vol. 6303), 630304-1 to 630304-12. [Available online at https://e-reports-ext.llnl.gov/pdf/335213.pdf.]

  • Lindelöw, P., 2007: Fiber based coherent lidars for remote wind sensing. Ph.D. dissertation, Technical University of Denmark, 186 pp.

  • Sharma, R., , Pervez Y. , , and Pervez S. , 2005: Seasonal evaluation and spatial variability of suspended particulate matter in the vicinity of a large coal-fired power station in India—A case study. Environ. Monit. Assess., 102, 113.

    • Search Google Scholar
    • Export Citation
  • Sonnenschein, C. M., , and Horrigan F. A. , 1971: Signal-to-noise relationships for coaxial systems that heterodyne backscatter from the atmosphere. Appl. Opt., 10, 16001604.

    • Search Google Scholar
    • Export Citation
  • Tunick, A., 2003: CN2 model to calculate the micrometeorological influences on the refractive index structure parameter. Environ. Modell. Software, 18, 165171.

    • Search Google Scholar
    • Export Citation
  • U.S. Environmental Protection Agency, 2010: Our nation’s air: Status and trends through 2008. Office of Air Quality Planning and Standards EPA Publ. EPA-454/R-09-002, 54 pp.

  • Wagner, R., , Antoniou I. , , Pedersen S. M. , , Courtney M. S. , , and Jørgensen H. E. , 2009: The influence of the wind speed profile on wind turbine performance measurements. Wind Energy, 12, 348362.

    • Search Google Scholar
    • Export Citation
  • Wiser, R., , and Bolinger M. , 2010: 2009 wind technologies market report. National Renewable Energy Laboratory Rep. NREL/TP-6A2-48666, 88 pp.

  • Wulfmeyer, V., , and Feingold G. , 2000: On the relationship between relative humidity and particle backscattering coefficient in the marine boundary layer determined with differential absorption lidar. J. Geophys. Res., 105 (D4), 47294741.

    • Search Google Scholar
    • Export Citation
  • Yura, H. T., 1979: Signal-to-noise ratio of heterodyne lidar systems in the presence of atmospheric turbulence. J. Mod. Opt., 26, 627644.

    • Search Google Scholar
    • Export Citation
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Performance of a Wind-Profiling Lidar in the Region of Wind Turbine Rotor Disks

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  • 1 University of Colorado, Boulder, Colorado
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Abstract

As the wind energy sector continues to grow, so does the need for reliable vertical wind profiles in the assessment of wind resources and turbine performance. In situ instrumentation mounted on meteorological towers can rarely probe the atmosphere across the full span of modern turbine rotor disks, which typically extend from 40 to 120 m above the surface. However, by measuring the Doppler shift of laser light backscattered by particles in the atmosphere, remote sensing lidar is capable of estimating wind speeds and turbulence at several altitudes in this range and above. Consequently, lidar has proven a promising technology for both wind resource assessment and turbine response characterization. The aim of this study is to quantify data availability for a coherent detection wind-profiling lidar—namely, the Leosphere Windcube.

To determine situations of suitable data return rates, a Windcube, collocated with a Vaisala CL31 ceilometer, was deployed as part of the Skywatch Observatory at the University of Colorado at Boulder. Aerosol backscatter, as measured by the ceilometer, and lidar carrier-to-noise ratio (CNR) are strongly correlated. Additionally, lidar CNR was found to depend on atmospheric turbulence characteristics and relative humidity in another deployment at a location in the United States Great Plains. These relationships suggest an ability to predict lidar performance based on widely available air quality assessments (such as PM2.5 concentration) and other climatic conditions, thus providing guidance for determining the utility of lidar deployments at wind farms to characterize turbine performance.

Corresponding author address: Matthew Aitken, Department of Physics, 390 UCB, University of Colorado, Boulder, CO 80309-0390. E-mail: matthew.aitken@colorado.edu

Abstract

As the wind energy sector continues to grow, so does the need for reliable vertical wind profiles in the assessment of wind resources and turbine performance. In situ instrumentation mounted on meteorological towers can rarely probe the atmosphere across the full span of modern turbine rotor disks, which typically extend from 40 to 120 m above the surface. However, by measuring the Doppler shift of laser light backscattered by particles in the atmosphere, remote sensing lidar is capable of estimating wind speeds and turbulence at several altitudes in this range and above. Consequently, lidar has proven a promising technology for both wind resource assessment and turbine response characterization. The aim of this study is to quantify data availability for a coherent detection wind-profiling lidar—namely, the Leosphere Windcube.

To determine situations of suitable data return rates, a Windcube, collocated with a Vaisala CL31 ceilometer, was deployed as part of the Skywatch Observatory at the University of Colorado at Boulder. Aerosol backscatter, as measured by the ceilometer, and lidar carrier-to-noise ratio (CNR) are strongly correlated. Additionally, lidar CNR was found to depend on atmospheric turbulence characteristics and relative humidity in another deployment at a location in the United States Great Plains. These relationships suggest an ability to predict lidar performance based on widely available air quality assessments (such as PM2.5 concentration) and other climatic conditions, thus providing guidance for determining the utility of lidar deployments at wind farms to characterize turbine performance.

Corresponding author address: Matthew Aitken, Department of Physics, 390 UCB, University of Colorado, Boulder, CO 80309-0390. E-mail: matthew.aitken@colorado.edu
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