Articles | Volume 14, issue 3
https://doi.org/10.5194/amt-14-2219-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/amt-14-2219-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A 2-year intercomparison of continuous-wave focusing wind lidar and tall mast wind measurements at Cabauw
Royal Netherlands Meteorological Institute (KNMI), Utrechtseweg 297, 3731 GA, De Bilt, the Netherlands
Fred C. Bosveld
Royal Netherlands Meteorological Institute (KNMI), Utrechtseweg 297, 3731 GA, De Bilt, the Netherlands
Marijn J. de Haij
Royal Netherlands Meteorological Institute (KNMI), Utrechtseweg 297, 3731 GA, De Bilt, the Netherlands
Arnoud Apituley
Royal Netherlands Meteorological Institute (KNMI), Utrechtseweg 297, 3731 GA, De Bilt, the Netherlands
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Cited articles
Baas, P., Bosveld, F. C., Klein Baltink, H., and Holtslag, A. A. M.: A Climatology of Nocturnal Low-Level Jets at Cabauw, J. Appl. Meteorol. Clim., 48, 1627–1642, https://doi.org/10.1175/2009JAMC1965.1, 2009. a
Bosveld, F. C.: The Cabauw In-situ Observational Program 2000 – Present:
Instruments, Calibrations and Set-up, KNMI, Technical Report, TR-384,
available at: http://bibliotheek.knmi.nl/knmipubTR/TR384.pdf (last access: 13 January 2021), 2020. a
Bosveld, F. C., Baas, P., Beljaars, A. C. M., Holtslag, A. A. M., Vilà-Guerau de Arellano, J., and van de Wiel, B. J. H.: Fifty Years of Atmospheric Boundary-Layer Research at Cabauw Serving Weather, Air Quality and Climate, Bound.-Lay. Meteorol., 177, 583–612, https://doi.org/10.1007/s10546-020-00541-w, 2020. a
Courtney, M. S., Wagner, R., and Lindelöw, P.: Testing and comparison of
lidars for profile and turbulence measurements in wind energy, IOP Conf. Series: Earth and Environmental Science, 1, 012021, https://doi.org/10.1088/1755-1307/1/1/012021, 2008. a, b, c
Henderson, S. W., Gatt, P., Rees, D., and Huffaker, R. M.: Wind Lidar, Chap. 7, in: Laser remote sensing, edited by: Fujii, T. and Fukuchi, T.,
Taylor and Francis, Boca Raton, FL, USA, 469–722, 2005. a
Kindler, D., Oldroyd, A., MacAskill, A., and Finch, D.: An eight month test campaign of the Qinetiq ZephIR system: Preliminary results, Meteorol. Z., 16, 479–489, https://doi.org/10.1127/0941-2948/2007/0226, 2007. a, b
KNMI: KNMI Data Platform, KNMI, the Netherlands, available at: https://dataplatform.knmi.nl/, last access: 9 March 2021. a
Knoop, S.: A two-year intercomparison of CW focusing wind lidar and tall mast wind measurements at Cabauw, Version v1, Data set, Zenodo, https://doi.org/10.5281/zenodo.3966868, 2020. a
Kristensen, L.: The Cup Anemometer and Other Exciting Instruments, PhD thesis, Risø National Laboratory, Roskilde, Denmark, Risø-R-615(EN), 1993. a
Mikkelsen, T.: Remote sensing of wind, Chap. 1, in: Remote Sensing for Wind Energy, edited by: Peña, A., DTU Wind Energy-E-Report-0084(EN), available at: https://orbit.dtu.dk/en/publications/remote-sensing-for-wind-energy-4
(last access: 13 January 2021), 2015. a
Peña, A., Hasager, C. B., Gryning, S.-E., Courtney, M. S., Antoniou, I., and Mikkelsen, T.: Offshore Wind Profiling Using Light Detection and Ranging Measurements, Wind Energy, 12, 105–124, https://doi.org/10.1002/we.283, 2009. a
Pitter, M., Slinger, C., and Harris, M.: Introduction to continuous-wave Doppler lidar, Chap. 5, in: Remote Sensing for Wind Energy, edited by: Peña, A., DTU Wind Energy-E-Report-0084(EN), available at: https://orbit.dtu.dk/en/publications/remote-sensing-for-wind-energy-4
(last access: 13 January 2021), 2015. a
Sathe, A., Gottschall, J., Mann, J., and Courtney, M.: Can Wind Lidars Measure Turbulence?, J. Atmos. Ocean. Tech., 28, 853–868, https://doi.org/10.1175/JTECH-D-10-05004.1, 2011. a
Smith, D. A., Harris, M., Coffey, A. S., Mikkelsen, T., Jørgensen, H. E., Mann, J., and Danielian, R.: Wind lidar evaluation at the Danish wind test site in Høvsøre, Wind Energy, 9, 87–93, https://doi.org/10.1002/we.193, 2006. a, b
Stoffelen, A., Pailleux, J., Källén, E., Vaughan, J. M., Isaksen, L.,
Flamant, P., Wergen, W., Andersson, E., Schyberg, H., Culoma, A., Meynart, R., Endemann, M., and Ingmann, P.: The atmospheric dynamics mission for global wind field measurement, B. Amer. Meteor. Soc., 86, 73–87, https://doi.org/10.1175/BAMS-86-1-73, 2005. a
Suomi, I., Gryning, S.-E., O'Connor, E. J., and Vihma, T.: Methodology for obtaining wind gusts using Doppler lidar, Q. J. Roy. Meteor. Soc., 143, 2061–2072, https://doi.org/10.1002/qj.3059, 2017. a
van Ulden, A. P. and Wieringa, J.: Atmospheric boundary layer research at Cabauw, Bound.-Lay. Meteorol., 78, 39–69, 1996. a
Verkaik, J. W. and Holtslag, A. A. M.: Wind profiles, momentum fluxes and roughness lengths at Cabauw revisited, Bound.-Lay. Meteorol., 122, 701–719, https://doi.org/10.1007/s10546-006-9121-1, 2007. a
Wauben, W.: Wind Tunnel and Field Test of Three 2D Sonic Anemometers, KNMI, Technical Report, TR-296, available at: http://bibliotheek.knmi.nl/knmipubTR/TR296.pdf (last access: 13 January 2021), 2007. a
Wouters, D. A. J. and Wagenaar, J. W.: Verification of the ZephIR 300 LiDAR
at the ECN LiDAR Calibration Facility for the offshore Europlatform
measurement campaign, ECN-M-16-029, available at: https://publications.ecn.nl/ECN-E-16-029 (last access:
13 January 2021), 2016. a
Short summary
Doppler wind lidars are laser-based remote sensing instruments that measure the wind up to a few hundred metres or even a few kilometres. Their data can improve weather models and help forecasters. To investigate their accuracy and required meteorological conditions, we have carried out a 2-year measurement campaign of a wind lidar at our Cabauw test site and made a comparison with cup anemometers and wind vanes at several levels in a 213 m tall meteorological mast.
Doppler wind lidars are laser-based remote sensing instruments that measure the wind up to a few...