Articles | Volume 10, issue 11
https://doi.org/10.5194/wes-10-2791-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-10-2791-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Evaluating the impact of motion compensation on turbulence intensity measurements from continuous-wave and pulsed floating lidars
Warren Watson
CORRESPONDING AUTHOR
Fraunhofer Institute for Wind Energy Systems (IWES), Am Seedeich 45, 27572 Bremerhaven, Germany
Faculty of Geosciences, University of Bremen, Klagenfurter Str. 4, 28359 Bremen, Germany
Gerrit Wolken-Möhlmann
Fraunhofer Institute for Wind Energy Systems (IWES), Am Seedeich 45, 27572 Bremerhaven, Germany
Julia Gottschall
Fraunhofer Institute for Wind Energy Systems (IWES), Am Seedeich 45, 27572 Bremerhaven, Germany
Faculty of Geosciences, University of Bremen, Klagenfurter Str. 4, 28359 Bremen, Germany
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Cited articles
Barros Nassif, F., Pimenta, F., Assireu, A., D'Aquino, C., and Passos, J.: Wind measurements using a LIDAR on a buoy, RBRH, 25, https://doi.org/10.1590/2318-0331.252020200053, 2020. a
Browning, K. and Wexler, R.: The Determination of Kinematic Properties of a Wind Field Using Doppler Radar, Journal of Applied Meteorology, 7, 105–113, https://doi.org/10.1175/1520-0450(1968)007<0105:TDOKPO>2.0.CO;2, 1968. a
Bundesamt für Seeschifffahrt und Hydrographie (BSH): Messnetz MARNET, https://www.bsh.de/DE/DATEN/Klima-und-Meer/Meeresumweltmessnetz/messnetz-marnet_node.html (last access: 17 March 2025), 2025. a
Désert, T., Knapp, G., and Aubrun, S.: Quantification and Correction of Wave-Induced Turbulence Intensity Bias for a Floating LIDAR System, Remote Sensing, 13, https://doi.org/10.3390/rs13152973, 2021. a
Edson, J., Hinton, A., Prada, K., Hare, J., and Fairall, C.: Direct Covariance Flux Estimates from Mobile Platforms at Sea, Journal of Atmospheric and Oceanic Technology, 15, 547–562, https://doi.org/10.1175/1520-0426(1998)015<0547:DCFEFM>2.0.CO;2, 1998. a
Gottschall, J., Wolken-Möhlmann, G., and Lange, B.: About offshore resource assessment with floating lidars with special respect to turbulence and extreme events, Journal of Physics: Conference Series, 555, 012043, https://doi.org/10.1088/1742-6596/555/1/012043, 2014a. a
Gottschall, J., Wolken-Möhlmann, G., Viergutz, T., and Lange, B.: Results and Conclusions of a Floating-lidar Offshore Test, Energy Procedia, 53, 156–161, https://doi.org/10.1016/j.egypro.2014.07.224, 2014b. a, b
Gottschall, J., Gribben, B., Stein, D., and Würth, I.: Floating lidar as an advanced offshore wind speed measurement technique: current technology status and gap analysis in regard to full maturity, WIREs Energy and Environment, 6, e250, https://doi.org/10.1002/wene.250, 2017. a, b
Kelberlau, F., Neshaug, V., Lønseth, L., Bracchi, T., and Mann, J.: Taking the Motion out of Floating Lidar: Turbulence Intensity Estimates with a Continuous-Wave Wind Lidar, Remote Sensing, 12, https://doi.org/10.3390/rs12050898, 2020. a, b
NEDO (New Energy and Industrial Technology Development Organization): Offshore Wind Measurement Guidebook, https://www.nedo.go.jp/content/100962731.pdf (last access: 27 January 2025), 2023. a
Newman, J., Clifton, A., Churchfield, M., and Klein, P.: Improving lidar turbulence estimates for wind energy, Journal of Physics: Conference Series, 753, 072010, https://doi.org/10.1088/1742-6596/753/7/072010, 2016a. a
Newman, J. F., Klein, P. M., Wharton, S., Sathe, A., Bonin, T. A., Chilson, P. B., and Muschinski, A.: Evaluation of three lidar scanning strategies for turbulence measurements, Atmos. Meas. Tech., 9, 1993–2013, https://doi.org/10.5194/amt-9-1993-2016, 2016b. a
OpenSeaMap: OpenSeaMap Maps, data under the Open Database License (ODbL), https://www.openseamap.org (last access: 20 November 2025), 2025. a
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 Sensing, 8, https://doi.org/10.3390/rs8090782, 2016. a
Peña, A.: Sensing the wind profile, Ph.D. thesis, ISBN 978-87-550-3709-0, risø-PhD-45(EN), 2009. a
Peña, A., Yankova, G. G., and Mallini, V.: On the lidar-turbulence paradox and possible countermeasures, Wind Energ. Sci., 10, 83–102, https://doi.org/10.5194/wes-10-83-2025, 2025. a
Salcedo-Bosch, A., Rocadenbosch, F., and Sospedra, J.: Enhanced Dual Filter for Floating Wind Lidar Motion Correction: The Impact of Wind and Initial Scan Phase Models, Remote Sensing, 14, https://doi.org/10.3390/rs14194704, 2022. a
Salcedo-Bosch, A., Rocadenbosch, F., Peña, A., Mann, J., and Lolli, S.: Understanding the Impact of Turbulence on Floating Lidar Measurements, IEEE Transactions on Geoscience and Remote Sensing, 63, 1–14, https://doi.org/10.1109/TGRS.2025.3586298, 2025. a
Sathe, A. and Mann, J.: A review of turbulence measurements using ground-based wind lidars, Atmos. Meas. Tech., 6, 3147–3167, https://doi.org/10.5194/amt-6-3147-2013, 2013. a
Sempreviva, A. M., Barthelmie, R., and Pryor, S.: Review of Methodologies for Offshore Wind Resource Assessment in European Seas, Surveys in Geophysics, 29, 471–497, https://doi.org/10.1007/s10712-008-9050-2, 2008. a
St. Pé, A., Weyer, E., Campbell, I., Arntsen, A. E., Kondabala, N., Mibus, M., Coulombe-Pontbriand, P., Black, A. H., Parker, Z., Swytink-Binnema, N., Jolin, N., Goudeau, B. T., Meklenborg Miltersen Slot, R., Svenningsen, L., Lee, J. C. Y., Debnath, M., Wylie, S., Apgar, D., Fric, T., Michaud, D., Smith, E., Mazoyer, P., Tocco, M., Guillemin, F., Teoh, K., and Meyers, M.: CFARS Site Suitability Initiative: An Open Source Approach to Evaluate the Performance of Remote Sensing Device (RSD) Turbulence Intensity Measurements & Accelerate Industry Adoption of RSDs for Turbine Suitability Assessment, Zenodo [data set], https://doi.org/10.5281/zenodo.5529750, 2021. a, b, c, d
The Carbon Trust: Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology, The Carbon Trust, https://www.carbontrust.com/our-work-and-impact/guides-reports-and-tools/roadmap-for-commercial-acceptance-of-floating-lidar (last access: 20 November 2025), 2018. a, b, c
Thiébaut, M., Cathelain, M., Yahiaoui, S., and Esmail, A.: Deriving atmospheric turbulence intensity from profiling pulsed lidar measurements, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2022-53, 2022. a
Thiébaut, M., Thebault, N., Le Boulluec, M., Guillaume, D., Maisondieu, C., and Benzo, C.: Experimental Evaluation of the Motion-Induced Effects for Turbulent Fluctuations Measurement on Floating Lidar Systems, Remote Sensing, 16, 1337, https://doi.org/10.3390/rs16081337, 2024. a, b
Uchiyama, S., Ohsawa, T., Asou, H., Konagaya, M., Misaki, T., Araki, R., and Hamada, K.: Accuracy Verification of Multiple Floating LiDARs at the Mutsu-Ogawara Site, Energies, 17, https://doi.org/10.3390/en17133164, 2024. a, b, c
Watson, W., Wolken-Möhlmann, G., and Gottschall, J.: Dataset: Evaluating the Impact of Motion Compensation on Turbulence Intensity Measurements from Continuous-Wave and Pulsed Floating Lidars, IWES Fraunhofer-Institut für Windenergiesysteme [data set], https://doi.org/10.24406/fordatis/413, 2025. a
WindEurope: Latest wind energy data for Europe – Autumn 2024, https://windeurope.org/intelligence-platform/product/latest-wind-energy-data-for-europe-autumn-2024/ (last access: 20 November 2025), 2024. a
Wolken-Möhlmann, G., Lilov, H., and Lange, B.: Simulation of motion induced measurement errors for wind measurements using LIDAR on floating platforms, Proceedings 15th International Symposium for the Advancement of Boundary-layer Remote Sensing (ISARS), Paris, France, 28–30 June 2010, 28–30, 2010. a, b, c
Wolken-Möhlmann, G., Gottschall, J., and Lange, B.: First Verification Test and Wake Measurement Results Using a SHIP-LIDAR System, Energy Procedia, 53, 146–155, https://doi.org/10.1016/j.egypro.2014.07.223, 2014. a, b, c
ZX Lidars: ZX 300M Wind Lidar Specifications, https://www.zxlidars.com/wind-lidars/zx-300m/ (last access: 10 March 2024), 2024. a
Short summary
In this study, we compare turbulence intensity measurements from two buoy-mounted wind lidars with data from a fixed lidar and a meteorological mast. Turbulence intensity is essential for understanding wind conditions but is often overestimated by floating systems due to wave motion. We applied a physics-based compensation to reduce these effects. Our findings show that motion compensation significantly improves accuracy, making floating lidar systems suitable for offshore wind site assessments.
In this study, we compare turbulence intensity measurements from two buoy-mounted wind lidars...
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