Articles | Volume 15, issue 9
https://doi.org/10.5194/amt-15-2839-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-15-2839-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Scan strategies for wind profiling with Doppler lidar – an large-eddy simulation (LES)-based evaluation
Charlotte Rahlves
CORRESPONDING AUTHOR
Institute of Meteorology and Climatology, Leibniz University Hannover, Hanover, Germany
Frank Beyrich
Meteorological Observatory Lindenberg, Richard-Aßmann-Observatory, German Meteorological Service, Lindenberg, Germany
Siegfried Raasch
Institute of Meteorology and Climatology, Leibniz University Hannover, Hanover, Germany
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Cited articles
Antoniou, I., Courtney, M., Jorgensen, H. E., Mikkelsen, T., Von Hünerbein, S., Bradley, S., Piper, B., Harris, M., Marti, I., Aristu, M., Foussekis, D., and Nielsen, M. P.: Remote sensing the wind using lidars and sodars, in: European Wind Energy Conference and Exhibition 2007, EWEC 2007, 7–10 May 2007, Milan, Italy, vol. 3, 2007. a
Arakawa, A. and Lamb, V. R.: Computational design of the basic dynamical processes of the UCLA general circulation model, in: Methods in Computational Physics – General circulation models of the atmosphere, Academic Press, vol. 17, 173–265, https://doi.org/10.1016/b978-0-12-460817-7.50009-4, 1977. a
Bingöl, F., Mann, J., and Foussekis, D.: Lidar error estimation with WAsP
engineering, in: IOP Conference Series: Earth and Environmental Science, 14th International Symposium for the Advancement of Boundary Layer Remote Sensing, 23–25 June 2008, Roskilde, Denmark, IOP Publishing, vol. 1, https://doi.org/10.1088/1755-1315/1/1/012058, 2008. a
Bingöl, F., Mann, J., and Foussekis, D.: Conically scanning lidar error in complex terrain, Meteorol. Z., 18, 189–195, https://doi.org/10.1127/0941-2948/2009/0368, 2009a. a
Bingöl, F., Mann, J., and Foussekis, D.: Lidar performance in complex
terrain modelled by WAsP Engineering, in: Proceedings of the European Wind
Energy Conference, 16–19 May 2009, Marseille, France, 2009b. a
Bradley, S., Strehz, A., and Emeis, S.: Remote sensing winds in complex
terrain – a review, Meteorol. Z., 24, 547–555, 2015. a
Browning, K. and Wexler, R.: The determination of kinematic properties of a
wind field using Doppler radar, J. Appl. Meteorol. Climatol., 7, 105–113,
1968. a
Cheinet, S. and Siebesma, A. P.: Variability of local structure parameters in
the convective boundary layer, J. Atmos. Sci., 66, 1002–1017, 2009. a
Chow, F. K. and Moin, P.: A further study of numerical errors in large-eddy
simulations, J. Comput. Phys., 184, 366–380, 2003. a
Cottle, P., Strawbridge, K., and McKendry, I.: Long-range transport of Siberian wildfire smoke to British Columbia: Lidar observations and air quality impacts, Atmos. Environ., 90, 71–77, 2014. a
Courtney, M., Wagner, R., and Lindelöw, P.: Testing and comparison of
lidars for profile and turbulence measurements in wind energy, IOP
Conference Series: Earth and Environmental Science, 14th International Symposium for the Advancement of Boundary Layer Remote Sensing, 23–25 June 2008, Roskilde, Denmark, IOP Publishing, 1, 012021, https://doi.org/10.1088/1755-1315/1/1/012021, 2008. a, b, c
Deardorff, J. W.: Stratocumulus-capped mixed layers derived from a
three-dimensional model, Bound.-Lay. Meteorol., 18, 495–527, 1980. a
Emeis, S., Harris, M., and Banta, R. M.: Boundary-layer anemometry by optical remote sensing for wind energy applications, Meteorol. Z., 16, 337–347, https://doi.org/10.1127/0941-2948/2007/0225, 2007. a
Finnigan, J.: Air flow over complex terrain, Springer, https://doi.org/10.1007/978-3-642-73845-6_13, 1988. a
Gasch, P., Wieser, A., Lundquist, J. K., and Kalthoff, N.: An LES-based airborne Doppler lidar simulator and its application to wind profiling in inhomogeneous flow conditions, Atmos. Meas. Tech., 13, 1609–1631, https://doi.org/10.5194/amt-13-1609-2020, 2020. a, b, c, d
Gottschall, J., Courtney, M., Wagner, R., Jørgensen, H. E., and Antoniou,
I.: Lidar profilers in the context of wind energy–a verification procedure
for traceable measurements, Wind Energy, 15, 147–159, 2012. a
Grant, E. R., Ross, A. N., Gardiner, B. A., and Mobbs, S. D.: Field observations of canopy flows over complex terrain, Bound.-Lay. Meteorol.,
156, 231–251, 2015. a
Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor J., Berg, S., Smith, N. J., Kern, R., Picus, M., Hoyer, S., van Kerkwijk, M. H., Brett, M., Haldane, A., Fernandez del Rio, J., Wiebe, M., Peterson, P., Gérard-Marchant, P., Sheppard, K., Reddy, T., Weckesser, W., Abbasi, H., Gohlke, C., and Oliphant, T. E.: Array programming with NumPy, Nature, 585, 357–362, 2020. a
Hofsäß, M., Clifton, A., and Cheng, P. W.: Reducing the uncertainty of lidar measurements in complex terrain using a linear model approach, Remote Sens., 10, 1465, https://doi.org/10.3390/rs10091465, 2018. a, b
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
Knist, C., Kayser, M., and Lehmann, V.: Das Vorhaben “Pilotstation bodengebundener Fernerkundung”, in: MOL-RAO Aktuell, Deutscher Wetterdienst,
2018. a
Kropfli, R.: Single Doppler radar measurements of turbulence profiles in the
convective boundary layer, J. Atmos. Ocean. Technol., 3, 305–314, 1986. a
Lhermitte, R.: Note on the Observation of Small-Scale Atmospheric Turbulence by Doppler Radar Techniques, Radio Sci., 4, 1241–1246, https://doi.org/10.1029/rs004i012p01241, 1969. a
Lundquist, J. K., Churchfield, M. J., Lee, S., and Clifton, A.: Quantifying error of lidar and sodar Doppler beam swinging measurements of wind turbine wakes using computational fluid dynamics, Atmos. Meas. Tech., 8, 907–920, https://doi.org/10.5194/amt-8-907-2015, 2015. a, b, c, d
Maronga, B., Gryschka, M., Heinze, R., Hoffmann, F., Kanani-Sühring, F., Keck, M., Ketelsen, K., Letzel, M. O., Sühring, M., and Raasch, S.: The Parallelized Large-Eddy Simulation Model (PALM) version 4.0 for atmospheric and oceanic flows: model formulation, recent developments, and future perspectives, Geosci. Model Dev., 8, 2515–2551, https://doi.org/10.5194/gmd-8-2515-2015, 2015. a
Nechaj, P., Gaál, L., Bartok, J., Vorobyeva, O., Gera, M., Kelemen, M., and Polishchuk, V.: Monitoring of low-level wind shear by ground-based 3D lidar for increased flight safety, protection of human lives and health, Int. J. Environ. Res. Public Health, 16, 4584, https://doi.org/10.3390/ijerph16224584, 2019. a
Päschke, E., Leinweber, R., and Lehmann, V.: An assessment of the performance of a 1.5 µm Doppler lidar for operational vertical wind profiling based on a 1-year trial, Atmos. Meas. Tech., 8, 2251–2266, https://doi.org/10.5194/amt-8-2251-2015, 2015. a, b
Pauscher, L., Vasiljevic, N., Callies, D., Lea, G., Mann, J., Klaas, T.,
Hieronimus, J., Gottschall, J., Schwesig, A., Kühn, M., and Courtney, M.: An
inter-comparison study of multi-and DBS lidar measurements in complex
terrain, Remote Sens., 9, 667, https://doi.org/10.3390/rs9070667, 2017. a
Raasch, S. and Schröter, M.: PALM – A large-eddy simulation model
performing on massively parallel computers, Meteorol. Z., 10, 363–372,
https://doi.org/10.1127/0941-2948/2001/0010-0363, 2001. a
Salesky, S. T., Chamecki, M., and Bou-Zeid, E.: On the Nature of the Transition Between Roll and Cellular Organization in the Convective Boundary Layer, Bound.-Lay. Meteorol., 163, 41–68, https://doi.org/10.1007/s10546-016-0220-3,
2017. a, b, c, d
Smalikho, I. N. and Banakh, V. A.: Measurements of wind turbulence parameters by a conically scanning coherent Doppler lidar in the atmospheric boundary layer, Atmos. Meas. Tech., 10, 4191–4208, https://doi.org/10.5194/amt-10-4191-2017, 2017. a
Stawiarski, C., Träumner, K., Knigge, C., and Calhoun, R.: Scopes and
challenges of dual-Doppler lidar wind measurements – An error analysis, J.
Atmos. Ocean. Technol., 30, 2044–2062, 2013. a
Stawiarski, C., Träumner, K., Kottmeier, C., Knigge, C., and Raasch, S.: Assessment of Surface-Layer Coherent Structure Detection in Dual-Doppler
Lidar Data Based on Virtual Measurements, Bound.-Lay. Meteorol., 156,
371–393, https://doi.org/10.1007/s10546-015-0039-3, 2015.
a, b
Teschke, G. and Lehmann, V.: Mean wind vector estimation using the velocity–azimuth display (VAD) method: an explicit algebraic solution, Atmos. Meas. Tech., 10, 3265–3271, https://doi.org/10.5194/amt-10-3265-2017, 2017. a, b
Werner, C.: Doppler Wind Lidar, in: Lidar: range-resolved optical remote sensing of the atmosphere, vol. 102, Springer Science & Business, https://doi.org/10.1007/0-387-25101-4_12, 2006. a
Wyngaard, J. C.: Turbulence in the Atmosphere, Cambridge University Press, https://doi.org/10.1017/cbo9780511840524, 2010. a
Short summary
Lidars can measure the wind profile in the lower part of the atmosphere, provided that the wind field is horizontally uniform and does not change during the time of the measurement. These requirements are mostly not fulfilled in reality, and the lidar wind measurement will thus hold a certain error. We investigate different strategies for lidar wind profiling using a lidar simulator implemented in a numerical simulation of the wind field. Our findings can help to improve wind measurements.
Lidars can measure the wind profile in the lower part of the atmosphere, provided that the wind...