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.
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
Related authors
No articles found.
Mohammadreza Manami, Guillaume Léa, Jakob Mann, Mikael Sjöholm, and Guillaume Gorju
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-165, https://doi.org/10.5194/wes-2025-165, 2025
Preprint under review for WES
Short summary
Short summary
A simple adaptive variant of the Doppler Beam Swinging (DBS) method is presented to improve the availability of wind velocity measurements in profiling lidars, particularly at higher altitudes. Following validation at the Østerild test site in Denmark, using three profiling lidars compared with cup anemometers and wind vanes, excellent agreement was observed. Availability assessments indicated a maximum increase of 16.9 percentage points over the standard approach.
Mohammadreza Manami, Jakob Mann, Mikael Sjöholm, Guillaume Léa, and Guillaume Gorju
EGUsphere, https://doi.org/10.5194/egusphere-2025-2226, https://doi.org/10.5194/egusphere-2025-2226, 2025
Short summary
Short summary
This research investigates a novel method for directly estimating wind velocity variances from averaged Doppler spectra in the frequency domain. Compared to the conventional time-domain approach, the proposed method offers a substantial improvement. Despite some limitations, this study marks a significant advancement in turbulence estimation using pulsed Doppler lidars, which presents promising potential for wind turbine load assessments.
Isadora L. Coimbra, Jakob Mann, José M. L. M. Palma, and Vasco T. P. Batista
Atmos. Meas. Tech., 18, 287–303, https://doi.org/10.5194/amt-18-287-2025, https://doi.org/10.5194/amt-18-287-2025, 2025
Short summary
Short summary
Dual-lidar measurements are explored here as a cost-effective alternative for measuring the wind at great heights. From measurements at a mountainous site, we showed that this methodology can accurately capture mean wind speeds and turbulence under different flow conditions, and we recommended optimal lidar placement and sampling rates. This methodology allows the construction of vertical wind profiles up to 430 m, surpassing traditional meteorological mast heights and single-lidar capabilities.
Abdul Haseeb Syed and Jakob Mann
Wind Energ. Sci., 9, 1381–1391, https://doi.org/10.5194/wes-9-1381-2024, https://doi.org/10.5194/wes-9-1381-2024, 2024
Short summary
Short summary
Wind flow consists of swirling patterns of air called eddies, some as big as many kilometers across, while others are as small as just a few meters. This paper introduces a method to simulate these large swirling patterns on a flat grid. Using these simulations we can better figure out how these large eddies affect big wind turbines in terms of loads and forces.
Liqin Jin, Mauro Ghirardelli, Jakob Mann, Mikael Sjöholm, Stephan Thomas Kral, and Joachim Reuder
Atmos. Meas. Tech., 17, 2721–2737, https://doi.org/10.5194/amt-17-2721-2024, https://doi.org/10.5194/amt-17-2721-2024, 2024
Short summary
Short summary
Three-dimensional wind fields can be accurately measured by sonic anemometers. However, the traditional mast-mounted sonic anemometers are not flexible in various applications, which can be potentially overcome by drones. Therefore, we conducted a proof-of-concept study by applying three continuous-wave Doppler lidars to characterize the complex flow around a drone to validate the results obtained by CFD simulations. Both methods show good agreement, with a velocity difference of 0.1 m s-1.
Liqin Jin, Jakob Mann, Nikolas Angelou, and Mikael Sjöholm
Atmos. Meas. Tech., 16, 6007–6023, https://doi.org/10.5194/amt-16-6007-2023, https://doi.org/10.5194/amt-16-6007-2023, 2023
Short summary
Short summary
By sampling the spectra from continuous-wave Doppler lidars very fast, the rain-induced Doppler signal can be suppressed and the bias in the wind velocity estimation can be reduced. The method normalizes 3 kHz spectra by their peak values before averaging them down to 50 Hz. Over 3 h, we observe a significant reduction in the bias of the lidar data relative to the reference sonic data when the largest lidar focus distance is used. The more it rains, the more the bias is reduced.
Nikolas Angelou, Jakob Mann, and Camille Dubreuil-Boisclair
Wind Energ. Sci., 8, 1511–1531, https://doi.org/10.5194/wes-8-1511-2023, https://doi.org/10.5194/wes-8-1511-2023, 2023
Short summary
Short summary
This study presents the first experimental investigation using two nacelle-mounted wind lidars that reveal the upwind and downwind conditions relative to a full-scale floating wind turbine. We find that in the case of floating wind turbines with small pitch and roll oscillating motions (< 1°), the ambient turbulence is the main driving factor that determines the propagation of the wake characteristics.
Wei Fu, Alessandro Sebastiani, Alfredo Peña, and Jakob Mann
Wind Energ. Sci., 8, 677–690, https://doi.org/10.5194/wes-8-677-2023, https://doi.org/10.5194/wes-8-677-2023, 2023
Short summary
Short summary
Nacelle lidars with different beam scanning locations and two types of systems are considered for inflow turbulence estimations using both numerical simulations and field measurements. The turbulence estimates from a sonic anemometer at the hub height of a Vestas V52 turbine are used as references. The turbulence parameters are retrieved using the radial variances and a least-squares procedure. The findings from numerical simulations have been verified by the analysis of the field measurements.
Abdul Haseeb Syed, Jakob Mann, Andreas Platis, and Jens Bange
Wind Energ. Sci., 8, 125–139, https://doi.org/10.5194/wes-8-125-2023, https://doi.org/10.5194/wes-8-125-2023, 2023
Short summary
Short summary
Wind turbines extract energy from the incoming wind flow, which needs to be recovered. In very large offshore wind farms, the energy is recovered mostly from above the wind farm in a process called entrainment. In this study, we analyzed the effect of atmospheric stability on the entrainment process in large offshore wind farms using measurements recorded by a research aircraft. This is the first time that in situ measurements are used to study the energy recovery process above wind farms.
Wei Fu, Alfredo Peña, and Jakob Mann
Wind Energ. Sci., 7, 831–848, https://doi.org/10.5194/wes-7-831-2022, https://doi.org/10.5194/wes-7-831-2022, 2022
Short summary
Short summary
Measuring the variability of the wind is essential to operate the wind turbines safely. Lidars of different configurations have been placed on the turbines’ nacelle to measure the inflow remotely. This work found that the multiple-beam lidar is the only one out of the three employed nacelle lidars that can give detailed information about the inflow variability. The other two commercial lidars, which have two and four beams, respectively, measure only the fluctuation in the along-wind direction.
Nikolas Angelou, Jakob Mann, and Ebba Dellwik
Atmos. Chem. Phys., 22, 2255–2268, https://doi.org/10.5194/acp-22-2255-2022, https://doi.org/10.5194/acp-22-2255-2022, 2022
Short summary
Short summary
In this study we use state-of-the-art scanning wind lidars to investigate the wind field in the near-wake region of a mature, open-grown tree. Our measurements provide for the first time a picture of the mean and the turbulent spatial fluctuations in the flow in the wake of a tree in its natural environment. Our observations support the hypothesis that even simple models can realistically simulate the turbulent fluctuations in the wake and thus predict the effect of trees in flow models.
Pedro Santos, Jakob Mann, Nikola Vasiljević, Elena Cantero, Javier Sanz Rodrigo, Fernando Borbón, Daniel Martínez-Villagrasa, Belén Martí, and Joan Cuxart
Wind Energ. Sci., 5, 1793–1810, https://doi.org/10.5194/wes-5-1793-2020, https://doi.org/10.5194/wes-5-1793-2020, 2020
Short summary
Short summary
This study presents results from the Alaiz experiment (ALEX17), featuring the characterization of two cases with flow features ranging from 0.1 to 10 km in complex terrain. We show that multiple scanning lidars can capture in detail a type of atmospheric wave that can happen up to 10 % of the time at this site. The results are in agreement with multiple ground observations and demonstrate the role of atmospheric stability in flow phenomena that need to be better captured by numerical models.
Martin Dörenkämper, Bjarke T. Olsen, Björn Witha, Andrea N. Hahmann, Neil N. Davis, Jordi Barcons, Yasemin Ezber, Elena García-Bustamante, J. Fidel González-Rouco, Jorge Navarro, Mariano Sastre-Marugán, Tija Sīle, Wilke Trei, Mark Žagar, Jake Badger, Julia Gottschall, Javier Sanz Rodrigo, and Jakob Mann
Geosci. Model Dev., 13, 5079–5102, https://doi.org/10.5194/gmd-13-5079-2020, https://doi.org/10.5194/gmd-13-5079-2020, 2020
Short summary
Short summary
This is the second of two papers that document the creation of the New European Wind Atlas (NEWA). The paper includes a detailed description of the technical and practical aspects that went into running the mesoscale simulations and the microscale downscaling for generating the climatology. A comprehensive evaluation of each component of the NEWA model chain is presented using observations from a large set of tall masts located all over Europe.
Pedro Santos, Alfredo Peña, and Jakob Mann
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-960, https://doi.org/10.5194/acp-2020-960, 2020
Preprint withdrawn
Short summary
Short summary
We show that the vector of vertical flux of horizontal momentum and the vector of the mean vertical gradient of horizontal velocity are not aligned, based on Doppler wind lidar observations up to 500 m, both offshore and onshore. We illustrate that a mesoscale model output matches the observed mean wind speed and momentum fluxes well, but that this model output as well as idealized large-eddy simulations have deviations with the observations when looking at the turning of the wind.
Cited articles
Bischoff, O., Schlipf, D., Würth, I., and Cheng, P. W.: Dynamic Motion Effects
and Compensation Methods of a Floating Lidar Buoy, EERA DeepWind 2015 Deep
Sea Offshore Wind Conference, Trondheim, Norway, 4–6 February 2015, https://doi.org/10.7567/JJAP.54.07JA03, 2015. a
Bischoff, O., Yu, W., Gottschall, J., and Cheng, P. W.: Validating a simulation
environment for floating lidar systems, in: J. Phys.: Conference
Series, 1037, 052036, https://doi.org/10.1088/1742-6596/1037/5/052036, 2018. a
Bischoff, O., Wolken-Möhlmann, G., and Cheng, P. W.: An approach and
discussion of a simulation based measurement uncertainty estimation for a
floating lidar system, J. Phys.: Conference Series, 2265, 022077,
https://doi.org/10.1088/1742-6596/2265/2/022077, 2022. a
Désert, T., Knapp, G., and Aubrun, S.: Quantification and correction of
wave-induced turbulence intensity bias for a floating lidar system, Remote
Sens., 13, 2973, https://doi.org/10.3390/rs13152973, 2021. a, b
DNV GL: Floating Lidar Stage 2 Type Validation DNV GL Type Validation of the
ACCURASEA Floating Lidar, https://tinyurl.com/ACCURASEA (last access: 15 September 2022),
2019. a
Elkinton, M. R., Rogers, A. L., and McGowan, J. G.: An investigation of
wind-shear models and experimental data trends for different terrains, Wind
Eng., 30, 341–350, https://doi.org/10.1260/030952406779295417, 2006. a, b
Emeis, S., Harris, M., and Banta, R. M.: Boundary-layer anemometry by optical
remote sensing for wind energy applications, Meteorol. Z., 16,
https://doi.org/10.1127/0941-2948/2007/0225, 2007. a
Gottschall, J., Courtney, M. S., Wagner, R., Jørgensen, H. E., and Antoniou,
I.: Lidar profilers in the context of wind energy-a verification procedure
for traceable measurements, Wind Energ., 15, 147–159, https://doi.org/10.1002/we.518, 2012. a
Gottschall, J., Wolken-Möhlmann, G., Viergutz, T., and Lange, B.: Results and
conclusions of a floating-lidar offshore test, Energ. Proc., 53, 156–161,
https://doi.org/10.1016/j.egypro.2014.07.224, 2014. a
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, Wiley Interdisciplinary Reviews:
Energy and Environment, 6, e250, https://doi.org/10.1002/wene.250, 2017. a
Gutiérrez-Antuñano, M. A., Tiana-Alsina, J., Salcedo, A., and Rocadenbosch,
F.: Estimation of the motion-induced horizontal-wind-speed standard deviation
in an offshore Doppler lidar, Remote Sens., 10, 2037, https://doi.org/10.3390/rs10122037,
2018. a
Heier, S.: Grid Integration of Wind Energy, John Wiley & Sons, Ltd, 3rd Edn., 494 pp., https://doi.org/10.1002/9781118703274, 2014. a
Hellevang, J. O. and Reuder, J.: Effect of wave motion on wind lidar
measurements – Comparison testing with controlled motion applied, in:
DeepWind 2013 – 10th Deep Sea Offshore Wind R&D Conference, 24–25 January 2013, Trondheim, Norway, 2013. a
Kelberlau, F. and Mann, J.: Better turbulence spectra from velocity–azimuth display scanning wind lidar, Atmos. Meas. Tech., 12, 1871–1888, https://doi.org/10.5194/amt-12-1871-2019, 2019. a
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 Sens., 12, 898, https://doi.org/10.3390/rs12050898,
2020. a, b, c
Mangat, M., Roziers, E. B. D., Medley, J., Pitter, M., Barker, W., and Harris,
M.: The impact of tilt and inflow angle on ground based Lidar wind
measurements, in: European Wind Energy Association Conference and Exhibition, 10–13 March 2014, Barcelona, Spain, EWEA 2014, 2014. a
Pitter, M., des Roziers, E. B., Medley, J., Mangat, M., Slinger, C., and
Harris, M.: Performance Stability of ZepIR in High Motion Environments:
Floating and Turbine Mounted, https://www.zxlidars.com/wp-content/uploads/2014/12/Performance-stability-of-ZephIR-in-high-motion-environments.pdf (last access: 15 September 2022),
2014. a
Rutherford, A., Pitter, M., Slinger, C., des Roziers, E. B., Barker, W., and
Harris, M.: The effect of motion on continuous wave lidar wind measurements,
in: Windpower 2013, Conference Windpower 2013, 5–8 May 2013, Chicago, USA, 2013. a
Salcedo-Bosch, A., Farre-Guarne, J., Sala-Alvarez, J., Villares-Piera, J.,
Tanamachi, R., and Rocadenbosch, F.: Floating Doppler Wind Lidar Motion
Simulator for Horizontal Wind Speed Measurement Error Assessment, in: 2021
IEEE International Geoscience and Remote Sensing Symposium IGARSS,
https://doi.org/10.1109/igarss47720.2021.9555023, 12–16 July 2021, Brussels, Belgium, 2021. a
Schlipf, D., Rettenmeier, A., and Haizmann, F.: Model Based Wind Vector Field
Reconstruction from LIDAR Data, Proceedings of the 11th German Wind Energy
Conference DEWEK, 7–8 November 2012, Bremen, Germany, 2012. 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 Energ., 9, 87–93, https://doi.org/10.1002/we.193, 2006. a
Stein, D., Faghani, D., Beeken, A., and Schwenk, P.: Assessment of the
Fugro/OCEANOR SEAWATCH Floating LiDAR Verification at RWE IJmuiden met mast, https://tinyurl.com/SWLB0 (last access: 15 September 2022), 2015. a
Sverdrup, H. and Munk, W.: Wind, Sea, and Swell, Theory of Relations For
Forecasting, Office, 44 pp., 1947. a
Tiana-Alsina, J., Gutierrez, M. A., Wurth, I., Puigdefabregas, J., and
Rocadenbosch, F.: Motion compensation study for a floating Doppler wind
LiDAR, in: 2015 IEEE International Geoscience and Remote Sensing Symposium
(IGARSS), 26–31 July 2015, Milan, Italy, vol. 2015-November, https://doi.org/10.1109/IGARSS.2015.7327051, 2015. a
Wolken-Möhlmann, G. and Gottschall, J.: Dependence of Floating LiDAR
performance on external parameters – Are existing onshore classification
methods applicable?, J. Phys.: Conference Series, 1669,
https://doi.org/10.1088/1742-6596/1669/1/012025, 2020. a
Wolken-Möhlmann, G., Lilov, H., and Lange, B.: Simulation of motion induced
measurement errors for wind measurements using LIDAR on floating platforms,
Isars 2010, 28–30 June 2010, Versailles, France, 2010. a
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...