Articles | Volume 15, issue 18
https://doi.org/10.5194/amt-15-5323-2022
© 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-15-5323-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 Sep 2022
Research article |
| 20 Sep 2022
| 20 Sep 2022
Quantification of motion-induced measurement error on floating lidar systems
Fugro Norway AS, Havnegata 9, 7462 Trondheim, Norway
Jakob Mann
DTU Wind, Technical University of Denmark, 4000 Roskilde, Denmark
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Cited
18 citations as recorded by crossref.
- Motion-Induced Errors in Buoy-Based Wind Measurements: Mechanisms, Compensation Methods, and Future Perspectives for Offshore Applications D. Cao et al. https://doi.org/10.3390/s26030920
- Assessment of Factors Influencing Motion Compensation Strategies for Turbulence-Intensity Measurements in Floating Lidar Applications W. Watson et al. https://doi.org/10.1088/1742-6596/3346/1/012013
- Offshore wind profile extrapolation effects on energy production estimates in Southeast Brazil Y. Sakagami et al. https://doi.org/10.1063/5.0252558
- Wind and turbulence conditions encountered by vessels and wind-assisted propulsion systems at sea R. Bölsterli et al. https://doi.org/10.1016/j.oceaneng.2026.128005
- Quantitative comparison of power production and power quality onshore and offshore: a case study from the eastern United States R. Foody et al. https://doi.org/10.5194/wes-9-263-2024
- Experimental Evaluation of the Motion-Induced Effects for Turbulent Fluctuations Measurement on Floating Lidar Systems M. Thiébaut et al. https://doi.org/10.3390/rs16081337
- The LOLland offshore Lidar EXperiment (LOLLEX): a novel observational approach for the study of wind farm flow and entrainment S. Malekmohammadi et al. https://doi.org/10.5194/amt-19-6267-2026
- A Motion-Correction Method for Turbulence Estimates from Floating Lidars A. Peña et al. https://doi.org/10.3390/rs14236065
- Quantification and correction of motion influence for nacelle-based lidar systems on floating wind turbines M. Gräfe et al. https://doi.org/10.5194/wes-8-925-2023
- Analysis of wind speed deviations between floating lidars, fixed lidar and cup anemometry based on experimental data G. Wolken-Möhlmann et al. https://doi.org/10.1088/1742-6596/2362/1/012042
- Underestimation of strong wind speeds offshore in ERA5: evidence, discussion and correction R. Gandoin & J. Garza https://doi.org/10.5194/wes-9-1727-2024
- Understanding the Impact of Turbulence on Floating Lidar Measurements A. Salcedo-Bosch et al. https://doi.org/10.1109/TGRS.2025.3586298
- Characterization of the offshore wind dynamics for wind energy production in the Gulf of Lion, Western Mediterranean Sea M. Thiébaut et al. https://doi.org/10.1016/j.weer.2024.100002
- Research on attitude correction algorithm for mobile wind lidars S. Zhao & Y. Shan https://doi.org/10.1088/1361-6501/ad2150
- Evaluating the Performance of Pulsed and Continuous-Wave Lidar Wind Profilers with a Controlled Motion Experiment S. Malekmohammadi et al. https://doi.org/10.3390/rs16173191
- Recent advances in structural health monitoring for offshore wind turbine blades: A review of physics-based models, data-driven methods, and surface defect detection Y. Cheng et al. https://doi.org/10.1177/14759217261487750
- Estimation of Wind Conditions in the Offshore Direction Using Multiple Numerical Models and In Situ Observations M. Konagaya et al. https://doi.org/10.3390/en18113000
- Uncertainty and bias on velocities determined from an arc-scanning lidar J. Mann et al. https://doi.org/10.1088/1742-6596/2767/4/042017
18 citations as recorded by crossref.
- Motion-Induced Errors in Buoy-Based Wind Measurements: Mechanisms, Compensation Methods, and Future Perspectives for Offshore Applications D. Cao et al. https://doi.org/10.3390/s26030920
- Assessment of Factors Influencing Motion Compensation Strategies for Turbulence-Intensity Measurements in Floating Lidar Applications W. Watson et al. https://doi.org/10.1088/1742-6596/3346/1/012013
- Offshore wind profile extrapolation effects on energy production estimates in Southeast Brazil Y. Sakagami et al. https://doi.org/10.1063/5.0252558
- Wind and turbulence conditions encountered by vessels and wind-assisted propulsion systems at sea R. Bölsterli et al. https://doi.org/10.1016/j.oceaneng.2026.128005
- Quantitative comparison of power production and power quality onshore and offshore: a case study from the eastern United States R. Foody et al. https://doi.org/10.5194/wes-9-263-2024
- Experimental Evaluation of the Motion-Induced Effects for Turbulent Fluctuations Measurement on Floating Lidar Systems M. Thiébaut et al. https://doi.org/10.3390/rs16081337
- The LOLland offshore Lidar EXperiment (LOLLEX): a novel observational approach for the study of wind farm flow and entrainment S. Malekmohammadi et al. https://doi.org/10.5194/amt-19-6267-2026
- A Motion-Correction Method for Turbulence Estimates from Floating Lidars A. Peña et al. https://doi.org/10.3390/rs14236065
- Quantification and correction of motion influence for nacelle-based lidar systems on floating wind turbines M. Gräfe et al. https://doi.org/10.5194/wes-8-925-2023
- Analysis of wind speed deviations between floating lidars, fixed lidar and cup anemometry based on experimental data G. Wolken-Möhlmann et al. https://doi.org/10.1088/1742-6596/2362/1/012042
- Underestimation of strong wind speeds offshore in ERA5: evidence, discussion and correction R. Gandoin & J. Garza https://doi.org/10.5194/wes-9-1727-2024
- Understanding the Impact of Turbulence on Floating Lidar Measurements A. Salcedo-Bosch et al. https://doi.org/10.1109/TGRS.2025.3586298
- Characterization of the offshore wind dynamics for wind energy production in the Gulf of Lion, Western Mediterranean Sea M. Thiébaut et al. https://doi.org/10.1016/j.weer.2024.100002
- Research on attitude correction algorithm for mobile wind lidars S. Zhao & Y. Shan https://doi.org/10.1088/1361-6501/ad2150
- Evaluating the Performance of Pulsed and Continuous-Wave Lidar Wind Profilers with a Controlled Motion Experiment S. Malekmohammadi et al. https://doi.org/10.3390/rs16173191
- Recent advances in structural health monitoring for offshore wind turbine blades: A review of physics-based models, data-driven methods, and surface defect detection Y. Cheng et al. https://doi.org/10.1177/14759217261487750
- Estimation of Wind Conditions in the Offshore Direction Using Multiple Numerical Models and In Situ Observations M. Konagaya et al. https://doi.org/10.3390/en18113000
- Uncertainty and bias on velocities determined from an arc-scanning lidar J. Mann et al. https://doi.org/10.1088/1742-6596/2767/4/042017
Saved (final revised paper)
Latest update: 07 Nov 2026
Short summary
Floating lidar systems are used for measuring wind speeds offshore, and their motion influences the measurements. This study describes the motion-induced bias on mean wind speed estimates by simulating the lidar sampling pattern of a moving lidar. An analytic model is used to validate the simulations. The bias is low and depends on amplitude and frequency of motion as well as on wind shear. It has been estimated for the example of the Fugro SEAWATCH wind lidar buoy carrying a ZX 300M lidar.
Floating lidar systems are used for measuring wind speeds offshore, and their motion influences...
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