Articles | Volume 18, issue 19
https://doi.org/10.5194/amt-18-4949-2025
© 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/amt-18-4949-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Oct 2025
Research article |
| 01 Oct 2025
| 01 Oct 2025
Ship-based lidar measurements for validating ASCAT-derived and ERA5 offshore wind profiles
Fraunhofer Institute for Wind Energy Systems IWES, 27572 Bremerhaven, Germany
ForWind, Institute of Physics, Carl von Ossietzky Universität Oldenburg, Küpkersweg 70, 26129 Oldenburg, Germany
Daniel Hatfield
C2Wind ApS, Vesterballevej 5, 7000 Fredericia, Denmark
Charlotte Bay Hasager
Department of Wind Energy and Systems, Technical University of Denmark, Frederiksborgvej 399, 4000 Roskilde, Denmark
Martin Kühn
ForWind, Institute of Physics, Carl von Ossietzky Universität Oldenburg, Küpkersweg 70, 26129 Oldenburg, Germany
Julia Gottschall
CORRESPONDING AUTHOR
Fraunhofer Institute for Wind Energy Systems IWES, 27572 Bremerhaven, Germany
Faculty of Geosciences, University of Bremen, 28359 Bremen, Germany
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Hugo Rubio, Martin Kühn, and Julia Gottschall
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Frauke Theuer, Andreas Rott, Jörge Schneemann, Lueder von Bremen, and Martin Kühn
Wind Energ. Sci., 7, 2099–2116, https://doi.org/10.5194/wes-7-2099-2022, https://doi.org/10.5194/wes-7-2099-2022, 2022
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Remote-sensing-based approaches have shown potential for minute-scale forecasting and need to be further developed towards an operational use. In this work we extend a lidar-based forecast to an observer-based probabilistic power forecast by combining it with a SCADA-based method. We further aggregate individual turbine power using a copula approach. We found that the observer-based forecast benefits from combining lidar and SCADA data and can outperform persistence for unstable stratification.
Frederik Berger, Lars Neuhaus, David Onnen, Michael Hölling, Gerard Schepers, and Martin Kühn
Wind Energ. Sci., 7, 1827–1846, https://doi.org/10.5194/wes-7-1827-2022, https://doi.org/10.5194/wes-7-1827-2022, 2022
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We proof the dynamic inflow effect due to gusts in wind tunnel experiments with MoWiTO 1.8 in the large wind tunnel of ForWind – University of Oldenburg, where we created coherent gusts with an active grid. The effect is isolated in loads and rotor flow by comparison of a quasi-steady and a dynamic case. The observed effect is not caught by common dynamic inflow engineering models. An improvement to the Øye dynamic inflow model is proposed, matching experiment and corresponding FVWM simulations.
Balthazar Arnoldus Maria Sengers, Matthias Zech, Pim Jacobs, Gerald Steinfeld, and Martin Kühn
Wind Energ. Sci., 7, 1455–1470, https://doi.org/10.5194/wes-7-1455-2022, https://doi.org/10.5194/wes-7-1455-2022, 2022
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Wake steering aims to redirect the wake away from a downstream turbine. This study explores the potential of a data-driven surrogate model whose equations can be interpreted physically. It estimates wake characteristics from measurable input variables by utilizing a simple linear model. The model shows encouraging results in estimating available power in the far wake, with significant improvements over currently used analytical models in conditions where wake steering is deemed most effective.
Haichen Zuo, Charlotte Bay Hasager, Ioanna Karagali, Ad Stoffelen, Gert-Jan Marseille, and Jos de Kloe
Atmos. Meas. Tech., 15, 4107–4124, https://doi.org/10.5194/amt-15-4107-2022, https://doi.org/10.5194/amt-15-4107-2022, 2022
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The Aeolus satellite was launched in 2018 for global wind profile measurement. After successful operation, the error characteristics of Aeolus wind products have not yet been studied over Australia. To complement earlier validation studies, we evaluated the Aeolus Level-2B11 wind product over Australia with ground-based wind profiling radar measurements and numerical weather prediction model equivalents. The results show that the Aeolus can detect winds with sufficient accuracy over Australia.
Marijn Floris van Dooren, Anantha Padmanabhan Kidambi Sekar, Lars Neuhaus, Torben Mikkelsen, Michael Hölling, and Martin Kühn
Atmos. Meas. Tech., 15, 1355–1372, https://doi.org/10.5194/amt-15-1355-2022, https://doi.org/10.5194/amt-15-1355-2022, 2022
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The remote sensing technique lidar is widely used for wind speed measurements for both industrial and academic applications. Lidars can measure wind statistics accurately but cannot fully capture turbulent fluctuations in the high-frequency range, since they are partly filtered out. This paper therefore investigates the turbulence spectrum measured by a continuous-wave lidar and analytically models the lidar's measured spectrum with a Lorentzian filter function and a white noise term.
Andreas Rott, Jörge Schneemann, Frauke Theuer, Juan José Trujillo Quintero, and Martin Kühn
Wind Energ. Sci., 7, 283–297, https://doi.org/10.5194/wes-7-283-2022, https://doi.org/10.5194/wes-7-283-2022, 2022
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We present three methods that can determine the alignment of a lidar placed on the transition piece of an offshore wind turbine based on measurements with the instrument: a practical implementation of hard targeting for north alignment, a method called sea surface levelling to determine the levelling of the system from water surface measurements, and a model that can determine the dynamic levelling based on the operating status of the wind turbine.
Paul Hulsman, Martin Wosnik, Vlaho Petrović, Michael Hölling, and Martin Kühn
Wind Energ. Sci., 7, 237–257, https://doi.org/10.5194/wes-7-237-2022, https://doi.org/10.5194/wes-7-237-2022, 2022
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Due to the possibility of mapping the wake fast at multiple locations with the WindScanner, a thorough understanding of the development of the wake is acquired at different inflow conditions and operational conditions. The lidar velocity data and the energy dissipation rate compared favourably with hot-wire data from previous experiments, lending credibility to the measurement technique and methodology used here. This will aid the process to further improve existing wake models.
Frederik Berger, David Onnen, Gerard Schepers, and Martin Kühn
Wind Energ. Sci., 6, 1341–1361, https://doi.org/10.5194/wes-6-1341-2021, https://doi.org/10.5194/wes-6-1341-2021, 2021
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Dynamic inflow denotes the unsteady aerodynamic response to fast changes in rotor loading and leads to load overshoots. We performed a pitch step experiment with MoWiTO 1.8 in the large wind tunnel of ForWind – University of Oldenburg. We measured axial and tangential inductions with a recent method with a 2D-LDA system and performed load and wake measurements. These radius-resolved measurements allow for new insights into the dynamic inflow phenomenon.
Janna Kristina Seifert, Martin Kraft, Martin Kühn, and Laura J. Lukassen
Wind Energ. Sci., 6, 997–1014, https://doi.org/10.5194/wes-6-997-2021, https://doi.org/10.5194/wes-6-997-2021, 2021
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Fluctuations in the power output of wind turbines are one of the major challenges in the integration and utilisation of wind energy. By analysing the power output fluctuations of wind turbine pairs in an offshore wind farm, we show that their correlation depends on their location within the wind farm and their inflow. The main outcome is that these correlation dependencies can be characterised by statistics of the power output of the wind turbines and sorted by a clustering algorithm.
Jörge Schneemann, Frauke Theuer, Andreas Rott, Martin Dörenkämper, and Martin Kühn
Wind Energ. Sci., 6, 521–538, https://doi.org/10.5194/wes-6-521-2021, https://doi.org/10.5194/wes-6-521-2021, 2021
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A wind farm can reduce the wind speed in front of it just by its presence and thus also slightly impact the available power. In our study we investigate this so-called global-blockage effect, measuring the inflow of a large offshore wind farm with a laser-based remote sensing method up to several kilometres in front of the farm. Our results show global blockage under a certain atmospheric condition and operational state of the wind farm; during other conditions it is not visible in our data.
Julia Gottschall and Martin Dörenkämper
Wind Energ. Sci., 6, 505–520, https://doi.org/10.5194/wes-6-505-2021, https://doi.org/10.5194/wes-6-505-2021, 2021
Anantha Padmanabhan Kidambi Sekar, Marijn Floris van Dooren, Andreas Rott, and Martin Kühn
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2021-16, https://doi.org/10.5194/wes-2021-16, 2021
Preprint withdrawn
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Turbine-mounted lidars performing inflow scans can be used to optimise wind turbine performance and extend their lifetime. This paper introduces a new method to extract wind inflow information from a turbine-mounted scanning SpinnerLidar based on Proper Orthogonal Decomposition. This method offers a balance between simple reconstruction methods and complicated physics-based solvers. The results show that the model can be used for lidar assisted control, loads validation and turbulence studies.
Frauke Theuer, Marijn Floris van Dooren, Lueder von Bremen, and Martin Kühn
Wind Energ. Sci., 5, 1449–1468, https://doi.org/10.5194/wes-5-1449-2020, https://doi.org/10.5194/wes-5-1449-2020, 2020
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Very short-term wind power forecasts are gaining increasing importance with the rising share of renewables in today's energy system. In this work, we developed a methodology to forecast wind power of offshore wind turbines on minute scales utilising long-range single-Doppler lidar measurements. The model was able to outperform persistence during unstable stratification in terms of deterministic and probabilistic scores, while it showed large shortcomings for stable atmospheric conditions.
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
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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.
Cited articles
Ahsbahs, T., Maclaurin, G., Draxl, C., Jackson, C. R., Monaldo, F., and Badger, M.: US East Coast synthetic aperture radar wind atlas for offshore wind energy, Wind Energ. Sci., 5, 1191–1210, https://doi.org/10.5194/wes-5-1191-2020, 2020. a
Badger, M., Peña, A., Bredesen, R. E., Berge, E., Hahmann, A. N., Badger, J., Karagali, I., Hasager, C. B., and Mikkelsen, T.: Bringing satellite winds to hub-height, in: Proceedings of EWEA 2012 – European Wind Energy Conference and Exhibition European Wind Energy Association (EWEA), Copenhagen, Denmark, 16–19 April 2012, 395 pp.,https://backend.orbit.dtu.dk/ws/portalfiles/portal/7946190/BRINGING_SATELLITE_WINDS_TO_HUB_HEIGHT.pdf (last access: 21 September 2025) 2012. a
Bonavita, M., Hólm, E., Isaksen, L., and Fisher, M.: The evolution of the ECMWF hybrid data assimilation system, Q. J. Roy. Meteor. Soc., 142, 287–303, https://doi.org/10.1002/qj.2652, 2016. a
Capps, S. B. and Zender, C. S.: Global ocean wind power sensitivity to surface layer stability, Geophys. Res. Lett., 36, D12110, https://doi.org/10.1029/2008GL037063, 2009. a
Capps, S. B. and Zender, C. S.: Estimated global ocean wind power potential from QuikSCAT observations, accounting for turbine characteristics and siting, J. Geophys. Res.-Atmos., 115, D12110, https://doi.org/10.1029/2009JD012679, 2010. a
Carbon Trust: Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LIDAR Technology: Technical Report, https://www.carbontrust.com/our-work-and-impact/guides-reports-and-tools/roadmap-for-commercial-acceptance-of-floating-lidar (last access: 9 December 2023), 2018. a
Carvalho, D., Rocha, A., Gomez-Gesteira, M., and Silva Santos, C.: Offshore winds and wind energy production estimates derived from ASCAT, OSCAT, numerical weather prediction models and buoys—A comparative study for the Iberian Peninsula Atlantic coast, Renew. Energ., 102, 433–444, https://doi.org/10.1016/j.renene.2016.10.063, 2017. a
Chelton, D. B., Ries, J. C., Haines, B. J., Fu, L. L., and Callahan, P. S.: Chapter 1 satellite altimetry, International Geophysics, 69, 1–183, https://doi.org/10.1016/S0074-6142(01)80146-7, 2001. a
Clifton, A., Boquet, M., Des Burin Roziers, E., Westerhellweg, A., Hofsass, M., Klaas, T., Vogstad, K., Clive, P., Harris, M., Wylie, S., Osler, E., Banta, B., Choukulkar, A., Lundquist, J., and Aitken, M.: Remote Sensing of Complex Flows by Doppler Wind Lidar: Issues and Preliminary Recommendations, National Renewable Energy Laboratory (NREL), https://doi.org/10.2172/1351595, 2015. a
de Kloe, J., Stoffelen, A., and Verhoef, A.: Improved use of scatterometer measurements by using stress-equivalent reference winds, IEEE J.-STARS, 10, 2340–2347, 2017. a
de Montera, L., Berger, H., Husson, R., Appelghem, P., Guerlou, L., and Fragoso, M.: High-resolution offshore wind resource assessment at turbine hub height with Sentinel-1 synthetic aperture radar (SAR) data and machine learning, Wind Energ. Sci., 7, 1441–1453, https://doi.org/10.5194/wes-7-1441-2022, 2022. a, b
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N., and Vitart, F.: The ERA-interim reanalysis: configuration and performance of the data assimilation system, Q. J. Roy. Meteor. Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011. a
Dekking, F. M.: A Modern Introduction to Probability and Statistics, Springer Texts in Statistics, Springer-Verlag London Limited, New York, https://doi.org/10.1007/1-84628-168-7, 2005. a
Dhirendra, D., Crockford, A., and Holtslag, E.: Uncertainty Assessment Fugro OCEANOR SEAWATCH Wind Lidar Buoy at RWE Meteomast Ijmuiden, Ecofis, https://offshorewind.rvo.nl/file/download/45051422 (last access: 16 September 2025), 2016. a
Dörenkämper, M., Optis, M., Monahan, A., and Steinfeld, G.: On the offshore advection of boundary-layer structures and the influence on offshore wind conditions, Bound.-Lay. Meteorol., 155, 459–482, https://doi.org/10.1007/s10546-015-0008-x, 2015. a
Doubrawa, P., Barthelmie, R. J., Pryor, S. C., Hasager, C. B., Badger, M., and Karagali, I.: Satellite winds as a tool for offshore wind resource assessment: the Great Lakes wind atlas, Remote Sens. Environ., 168, 349–359, https://doi.org/10.1016/j.rse.2015.07.008, 2015. a, b
Duncan, J. B., Wijnant, I. L., and Knoop, S.: DOWA validation against offshore mast and LiDAR measurements, TNO Report 2019 R10062, 2019b. a
E.U. Copernicus Marine Service Information (CMEMS): Global Ocean Daily Gridded Sea Surface Winds from Scatterometer, Marine Data Store (MDS) [data set], https://doi.org/10.48670/moi-00182, 2023. a
Global Wind Energy Council: Global Offshore Wind Report 2024, https://www.gwec.net/reports/globaloffshorewindreport, last access: 25 October 2024. 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, WIREs Energy Environ., 6, e250, https://doi.org/10.1002/wene.250, 2017. a
Gottschall, J., Catalano, E., Dörenkämper, M., and Witha, B.: The NEWA ferry lidar experiment: measuring mesoscale winds in the Southern Baltic Sea, Remote Sens.-Basel, 10, 1620, https://doi.org/10.3390/rs10101620, 2018. a, b, c
Gualtieri, G.: Reliability of ERA5 reanalysis data for wind resource assessment: a comparison against tall towers, Energies, 14, 4169, https://doi.org/10.3390/EN14144169, 2021. a
Hasager, C. B., Badger, M., Peña, A., Larsén, X. G., and Bingöl, F.: SAR-based wind resource statistics in the Baltic Sea, Remote Sens.-Basel, 3, 117–144, https://doi.org/10.3390/rs3010117, 2011. a
Hatfield, D., Hasager, C. B., and Karagali, I.: Comparing offshore ferry lidar measurements in the Southern Baltic Sea with ASCAT, FINO2 and WRF, Remote Sens.-Basel, 14, 1427, https://doi.org/10.3390/rs14061427, 2022. a, b
Hatfield, D., Hasager, C. B., and Karagali, I.: Vertical extrapolation of Advanced Scatterometer (ASCAT) ocean surface winds using machine-learning techniques, Wind Energ. Sci., 8, 621–637, https://doi.org/10.5194/wes-8-621-2023, 2023. a, b
Hersbach, H.: CMOD5.N: A C-band Geophysical Model Function for Equivalent Neutral Wind, European Center for Medium-Range Weather Forecast (ECMWF), https://doi.org/10.21957/mzcfm6jfl, 2008. a
Hersbach, H., Stoffelen, A., and de Haan, S.: An improved C-band scatterometer ocean geophysical model function: CMOD5, J. Geophys. Res.-Atmos., 112, 1965, https://doi.org/10.1029/2006JC003743, 2007. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
Högström, U., Smedman, A.-S., and Bergström, H.: Calculation of wind speed variation with height over the sea, Wind Energy, 30, 269–286, https://doi.org/10.1260/030952406779295480, 2006. a
International Renewable Energy Agency: Renewable Energy Capacity Statistics 2024, International Renewable Energy Agency (IRENA), ISBN 978-92-9260-587-2, 2024. a
Johannessen, J. A.: On radar imaging of current features: 2. Mesoscale eddy and current front detection, J. Geophys. Res.-Atmos., 110, 245, https://doi.org/10.1029/2004JC002802, 2005. a, b
Kalverla, P. C.: Characterisation of offshore winds for energy applications, PhD thesis, Wageningen University, the Netherlands, https://doi.org/10.18174/498797, 2019. a
Karagali, I., Peña, A., Badger, M., and Hasager, C. B.: Wind characteristics in the North and Baltic Seas from the QuikSCAT satellite, Wind Energy, 17, 123–140, https://doi.org/10.1002/we.1565, 2014. a
Karagali, I., Hahmann, A. N., Badger, M., Hasager, C., and Mann, J.: New European wind atlas offshore, J. Phys. Conf. Ser., 1037, 052007, https://doi.org/10.1088/1742-6596/1037/5/052007, 2018. a
Knoop, S., Ramakrishnan, P., and Wijnant, I.: Dutch offshore wind atlas validation against Cabauw meteomast wind measurements, Energies, 13, 6558, https://doi.org/10.3390/en13246558, 2020. a
Kudryavtsev, V.: On radar imaging of current features: 1. Model and comparison with observations, J. Geophys. Res.-Atmos., 110, 10529, https://doi.org/10.1029/2004JC002505, 2005. a, b
Lange, B., Larsen, S., Højstrup, J., and Barthelmie, R.: The influence of thermal effects on the wind speed profile of the coastal marine boundary layer, Bound.-Lay. Meteorol., 112, 587–617, https://doi.org/10.1023/B:BOUN.0000030652.20894.83, 2004. a
Lindsley, R. D., Blodgett, J. R., and Long, D. G.: Analysis and validation of high-resolution wind from ASCAT, IEEE T. Geosci. Remote, 54, 5699–5711, https://doi.org/10.1109/TGRS.2016.2570245, 2016. a
Martin, S.: An Introduction to Ocean Remote Sensing, Cambridge University Press, New York, 2nd edn., https://doi.org/10.1017/CBO9781139094368, 2014. a
Optis, M., Bodini, N., Debnath, M., and Doubrawa, P.: New methods to improve the vertical extrapolation of near-surface offshore wind speeds, Wind Energ. Sci., 6, 935–948, https://doi.org/10.5194/wes-6-935-2021, 2021. a, b, c, d
Peña, A. and Hahmann, A. N.: Atmospheric stability and turbulence fluxes at Horns Rev-an intercomparison of sonic, bulk and WRF model data, Wind Energy, 15, 717–731, https://doi.org/10.1002/we.500, 2012. a
Peña, A., Gryning, S.-E., and Hasager, C. B.: Measurements and modelling of the wind speed profile in the marine atmospheric boundary layer, Bound.-Lay. Meteorol., 129, 479–495, https://doi.org/10.1007/s10546-008-9323-9, 2008. a
Pichugina, Y. L., Brewer, W. A., Banta, R. M., Choukulkar, A., Clack, C. T. M., Marquis, M. C., McCarty, B. J., Weickmann, A. M., Sandberg, S. P., Marchbanks, R. D., and Hardesty, R. M.: Properties of the offshore low level jet and rotor layer wind shear as measured by scanning Doppler lidar, Wind Energy, 20, 987–1002, https://doi.org/10.1002/we.2075, 2017. a
Remmers, T., Cawkwell, F., Desmond, C., Murphy, J., and Politi, E.: The potential of advanced scatterometer (ASCAT) 12.5 km coastal observations for offshore wind farm site selection in Irish waters, Energies, 12, 206, https://doi.org/10.3390/en12020206, 2019. a
Rubio, H. and Gottschall, J.: Development of an analytical uncertainty model for ship-based lidar measurements, J. Phys. Conf. Ser., 2362, 012034, https://doi.org/10.1088/1742-6596/2362/1/012034, 2022. a, b
Savazzi, A. C. M., Nuijens, L., Sandu, I., George, G., and Bechtold, P.: The representation of the trade winds in ECMWF forecasts and reanalyses during EUREC4A, Atmos. Chem. Phys., 22, 13049–13066, https://doi.org/10.5194/acp-22-13049-2022, 2022. a
Smedman, A.-S., Bergström, H., and Grisogono, B.: Evolution of stable internal boundary layers over a cold sea, J. Geophys. Res.-Atmos., 102, 1091–1099, https://doi.org/10.1029/96JC02782, 1997. a
Stoffelen, A., Portabella, M., Verhoef, A., Verspeek, J., and Vogelzang, J.: High-resolution ASCAT scatterometer winds near the coast, IEEE T. Geosci. Remote, 50, 2481–2487, 2008. a
Stoffelen, A., Verspeek, J. A., Vogelzang, J., and Verhoef, A.: The CMOD7 geophysical model function for ASCAT and ERS wind retrievals, IEEE J. Sel. Top. Appl., 10, 2123–2134, 2017. a
Stull, R. B.: An Introduction to Boundary Layer Meteorology, Vol. 13 of Atmospheric and Oceanographic Sciences Library, Springer, Berlin, https://doi.org/10.1007/978-94-009-3027-8, 1988. a
Svensson, N.: Mesoscale Processes over the Baltic Sea, PhD thesis, Department for Earth Sciences, Uppsala University, ISBN 978-91-513-0331-4, 2018. a
Takeyama, Y., Ohsawa, T., Shimada, S., Kozai, K., Kawaguchi, K., and Kogaki, T.: Assessment of the offshore wind resource in Japan with the ASCAT microwave scatterometer, Int. J. Remote Sens., 40, 1200–1216, https://doi.org/10.1080/01431161.2018.1524588, 2019. a
Verhoef, A. and Stoffelen, A.: Validation of ASCAT 12.5-km Winds, EUMETSAT, https://knmi-scatterometer-website-prd.s3-eu-west-1.amazonaws.com/publications/validation_of_ascat_12.5km_winds_1.3.pdf (last access: 21 September 2025), 2013. a
Verhoef, A. and Stoffelen, A.: ASCAT Wind Product User Manual: Technical Report, EUMETSAT, https://knmi-scatterometer-website-prd.s3-eu-west-1.amazonaws.com/publications/ss3_pm_ascat_1.16.pdf (last access: 21 September 2025), 2019. a
Verspeek, J., Stoffelen, A., Verhoef, A., and Portabella, M.: Improved ASCAT wind retrieval using NWP ocean calibration, IEEE T. Geosci. Remote, 50, 2488–2494, https://doi.org/10.1109/TGRS.2011.2180730, 2012. a
Wijnant, I. L., van Ulft, B., van Stratum, B., Barkmeijer, J., Onvlee, J., de Valk, C., Knoop, S., Kok, S., Marseille, G. J., Klein Baltik, H., and Stepek, A.: The Dutch Offshore Wind Atlas (DOWA): description of the dataset, KNMI, Report number: TR-380, 2019. a
Wind Europe: Scaling up Floating Offshore Wind towards competitiveness, https://windeurope.org/data/products/scaling-up-floating-offshore-wind-towards-competitiveness/ (last access: 20 September 2025), 2021. a
Witha, B., Dörenkämper, M., Frank, H., García-Bustamante, E., González-Rouco, F., Navarro, J., Schneider, M., Steeneveld, G.-J., Svensson, N., and Gottschall, J.: The NEWA Ferry Lidar Benchmark: Comparing mesoscale models with lidar measurements along a ship route, Wind Energy Science Conference, 17–20 June 2019, Cork, Ireland, Zenodo [presentation], https://doi.org/10.5281/zenodo.3372693, 2019a. a
Witha, B., Hahmann, A., Sile, T., Dörenkämper, M., Ezber, Y., García-Bustamante, E., González-Rouco, J. F., Leroy, G., and Navarro, J.: WRF model sensitivity studies and specifications for the NEWA mesoscale wind atlas production runs, Zenodo [presentation], https://doi.org/10.5281/zenodo.2682604, 2019b. a, b
Wolken-Möhlmann, G. and Gottschall, J.: Ship-based lidar measurement in the wake of an offshore wind farm, J. Phys. Conf. Ser., 555, 012043, https://doi.org/10.1088/1742-6596/555/1/012043, 2014. a, b
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
Wood, D. H.: Internal boundary layer growth following a step change in surface roughness, Bound.-Lay. Meteorol., 22, 241–244, https://doi.org/10.1007/BF00118257, 1982. a
Zentek, R., Kohnemann, S. H. E., and Heinemann, G.: Analysis of the performance of a ship-borne scanning wind lidar in the Arctic and Antarctic, Atmos. Meas. Tech., 11, 5781–5795, https://doi.org/10.5194/amt-11-5781-2018, 2018. a
Zhai, X., Wu, S., Liu, B., Song, X., and Yin, J.: Shipborne Wind Measurement and Motion-induced Error Correction of a Coherent Doppler Lidar over the Yellow Sea in 2014, Atmos. Meas. Tech., 11, 1313–1331, https://doi.org/10.5194/amt-11-1313-2018, 2018. a
Short summary
Due to the scarcity of offshore in situ observations, alternative data sources are essential for a reliable understanding of offshore winds. Therefore, this study delves into the world of satellite remote sensing (ASCAT) and numerical models (ERA5), exploring their capabilities and limitations in characterising offshore winds. This investigation evaluates these two datasets against measurements from a floating ship-based lidar, collected during a novel measurement campaign in the Baltic Sea.
Due to the scarcity of offshore in situ observations, alternative data sources are essential for...
