Articles | Volume 6, issue 2
https://doi.org/10.5194/wes-6-505-2021
© Author(s) 2021. 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-6-505-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 Apr 2021
Research article |
| 12 Apr 2021
| 12 Apr 2021
Understanding and mitigating the impact of data gaps on offshore wind resource estimates
Julia Gottschall
CORRESPONDING AUTHOR
Fraunhofer Institute for Wind Energy Systems IWES, Am Seedeich 45, 27572 Bremerhaven, Germany
Martin Dörenkämper
Fraunhofer Institute for Wind Energy Systems IWES, Küpkersweg 70, 26129 Oldenburg, Germany
Related authors
Astrid Lampert, Beatriz Cañadillas, Thomas Rausch, Lea Schmitt, Bughsin' Djath, Johannes Schulz-Stellenfleth, Andreas Platis, Kjell zum Berge, Ines Schäfer, Jens Bange, Thomas Neumann, Martin Dörenkämper, Bernhard Stoevesandt, Julia Gottschall, Lukas Vollmer, Stefan Emeis, Mares Barekzai, Simon Siedersleben, Martin Kühn, Gerald Steinfeld, Detlev Heinemann, Joachim Peinke, Hendrik Heißelmann, Jörge Schneemann, Gabriele Centurelli, Philipp Waldmann, and Konrad Bärfuss
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-277, https://doi.org/10.5194/wes-2025-277, 2026
Preprint under review for WES
Short summary
Short summary
Two major aircraft measurement campaigns above the North Sea provide insights into modifications of the wind field and sea surface induced by wind farms: The aircraft performed transects at hub height upstream and downstream of wind farm clusters, and identified different effects, e.g., how long it takes for the wind speed to recover after the wind farm, how changes across the coastline interact with wind energy, and if wind farms are well represented in numerical simulations.
Stefan Ivanell, Bjarke T. Olsen, Antoine Mathieu, Cristina Mulet-Benzo, Abdul Haseeb Syed, Warit Chanprasert, Mikael Sjöholm, Jakob Mann, and Julia Gottschall
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-286, https://doi.org/10.5194/wes-2025-286, 2026
Preprint under review for WES
Short summary
Short summary
Modern GW-scale offshore wind farms face challenges from atmospheric dynamics. This study examines how boundary layer height (BLH) and large-scale turbulence affect efficiency and loads. Using WRF simulations, lidar data, and CFD modeling for a 100-turbine, 15 MW wind farm at three representative sites, we show that low BLH reduces performance. Turbulence-induced low-frequency fluctuations increase fatigue loads, underscoring the need to include BLH and turbulence in design models.
Warren Watson, Gerrit Wolken-Möhlmann, and Julia Gottschall
Wind Energ. Sci., 10, 2791–2820, https://doi.org/10.5194/wes-10-2791-2025, https://doi.org/10.5194/wes-10-2791-2025, 2025
Short summary
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.
Cristina Mulet-Benzo, Andrew Black, and Julia Gottschall
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-155, https://doi.org/10.5194/wes-2025-155, 2025
Revised manuscript under review for WES
Short summary
Short summary
Wind turbine wake modeling requires accurate representation of atmospheric conditions, especially the atmospheric boundary layer height (ABLH). Scanning lidars provide a viable solution for ABLH determination. This study assesses ABLH data from coastal and offshore scanning lidars, comparing it to ABLH derived from models typically used for wake modelling. The results demonstrate a general overestimation of the models compared to the lidar, and a bias sensitivity to the diurnal cycle.
Hugo Rubio, Daniel Hatfield, Charlotte Bay Hasager, Martin Kühn, and Julia Gottschall
Atmos. Meas. Tech., 18, 4949–4968, https://doi.org/10.5194/amt-18-4949-2025, https://doi.org/10.5194/amt-18-4949-2025, 2025
Short summary
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.
Farkhondeh (Hanie) Rouholahnejad and Julia Gottschall
Wind Energ. Sci., 10, 143–159, https://doi.org/10.5194/wes-10-143-2025, https://doi.org/10.5194/wes-10-143-2025, 2025
Short summary
Short summary
In wind energy, precise wind speed prediction at hub height is vital. Our study in the Dutch North Sea reveals that the on-site-trained random forest model outperforms the global reanalysis data, ERA5, in accuracy and precision. Trained within a 200 km range, the model effectively extends the wind speed vertically but experiences bias. It also outperforms ERA5 corrected with measurements in capturing wind speed variations and fine wind patterns, highlighting its potential for site assessment.
Martin Georg Jonietz Alvarez, Warren Watson, and Julia Gottschall
Wind Energ. Sci., 9, 2217–2233, https://doi.org/10.5194/wes-9-2217-2024, https://doi.org/10.5194/wes-9-2217-2024, 2024
Short summary
Short summary
Offshore wind measurements are often affected by gaps. We investigated how these gaps affect wind resource assessments and whether filling them reduces their effect. We find that the effect of gaps on the estimated long-term wind resource is lower than expected and that data gap filling does not significantly change the outcome. These results indicate a need to reduce current wind data availability requirements for offshore measurement campaigns.
Marta Bertelè, Paul J. Meyer, Carlo R. Sucameli, Johannes Fricke, Anna Wegner, Julia Gottschall, and Carlo L. Bottasso
Wind Energ. Sci., 9, 1419–1429, https://doi.org/10.5194/wes-9-1419-2024, https://doi.org/10.5194/wes-9-1419-2024, 2024
Short summary
Short summary
A neural observer is used to estimate shear and veer from the operational data of a large wind turbine equipped with blade load sensors. Comparison with independent measurements from a nearby met mast and profiling lidar demonstrate the ability of the
rotor as a sensorconcept to provide high-quality estimates of these inflow quantities based simply on already available standard operational data.
Moritz Gräfe, Vasilis Pettas, Julia Gottschall, and Po Wen Cheng
Wind Energ. Sci., 8, 925–946, https://doi.org/10.5194/wes-8-925-2023, https://doi.org/10.5194/wes-8-925-2023, 2023
Short summary
Short summary
Inflow wind field measurements from nacelle-based lidar systems offer great potential for different applications including turbine control, load validation and power performance measurements. On floating wind turbines nacelle-based lidar measurements are affected by the dynamic behavior of the floating foundations. Therefore, the effects on lidar wind speed measurements induced by floater dynamics must be well understood. A new model for quantification of these effects is introduced in our work.
Hugo Rubio, Martin Kühn, and Julia Gottschall
Wind Energ. Sci., 7, 2433–2455, https://doi.org/10.5194/wes-7-2433-2022, https://doi.org/10.5194/wes-7-2433-2022, 2022
Short summary
Short summary
A proper development of offshore wind farms requires the accurate description of atmospheric phenomena like low-level jets. In this study, we evaluate the capabilities and limitations of numerical models to characterize the main jets' properties in the southern Baltic Sea. For this, a comparison against ship-mounted lidar measurements from the NEWA Ferry Lidar Experiment has been implemented, allowing the investigation of the model's capabilities under different temporal and spatial constraints.
Astrid Lampert, Beatriz Cañadillas, Thomas Rausch, Lea Schmitt, Bughsin' Djath, Johannes Schulz-Stellenfleth, Andreas Platis, Kjell zum Berge, Ines Schäfer, Jens Bange, Thomas Neumann, Martin Dörenkämper, Bernhard Stoevesandt, Julia Gottschall, Lukas Vollmer, Stefan Emeis, Mares Barekzai, Simon Siedersleben, Martin Kühn, Gerald Steinfeld, Detlev Heinemann, Joachim Peinke, Hendrik Heißelmann, Jörge Schneemann, Gabriele Centurelli, Philipp Waldmann, and Konrad Bärfuss
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-277, https://doi.org/10.5194/wes-2025-277, 2026
Preprint under review for WES
Short summary
Short summary
Two major aircraft measurement campaigns above the North Sea provide insights into modifications of the wind field and sea surface induced by wind farms: The aircraft performed transects at hub height upstream and downstream of wind farm clusters, and identified different effects, e.g., how long it takes for the wind speed to recover after the wind farm, how changes across the coastline interact with wind energy, and if wind farms are well represented in numerical simulations.
Stefan Ivanell, Bjarke T. Olsen, Antoine Mathieu, Cristina Mulet-Benzo, Abdul Haseeb Syed, Warit Chanprasert, Mikael Sjöholm, Jakob Mann, and Julia Gottschall
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-286, https://doi.org/10.5194/wes-2025-286, 2026
Preprint under review for WES
Short summary
Short summary
Modern GW-scale offshore wind farms face challenges from atmospheric dynamics. This study examines how boundary layer height (BLH) and large-scale turbulence affect efficiency and loads. Using WRF simulations, lidar data, and CFD modeling for a 100-turbine, 15 MW wind farm at three representative sites, we show that low BLH reduces performance. Turbulence-induced low-frequency fluctuations increase fatigue loads, underscoring the need to include BLH and turbulence in design models.
Warren Watson, Gerrit Wolken-Möhlmann, and Julia Gottschall
Wind Energ. Sci., 10, 2791–2820, https://doi.org/10.5194/wes-10-2791-2025, https://doi.org/10.5194/wes-10-2791-2025, 2025
Short summary
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.
Cristina Mulet-Benzo, Andrew Black, and Julia Gottschall
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-155, https://doi.org/10.5194/wes-2025-155, 2025
Revised manuscript under review for WES
Short summary
Short summary
Wind turbine wake modeling requires accurate representation of atmospheric conditions, especially the atmospheric boundary layer height (ABLH). Scanning lidars provide a viable solution for ABLH determination. This study assesses ABLH data from coastal and offshore scanning lidars, comparing it to ABLH derived from models typically used for wake modelling. The results demonstrate a general overestimation of the models compared to the lidar, and a bias sensitivity to the diurnal cycle.
Hugo Rubio, Daniel Hatfield, Charlotte Bay Hasager, Martin Kühn, and Julia Gottschall
Atmos. Meas. Tech., 18, 4949–4968, https://doi.org/10.5194/amt-18-4949-2025, https://doi.org/10.5194/amt-18-4949-2025, 2025
Short summary
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.
Johanna Borowski, Sandra Schwegmann, Kerstin Avila, and Martin Dörenkämper
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-117, https://doi.org/10.5194/wes-2025-117, 2025
Revised manuscript accepted for WES
Short summary
Short summary
Assessing the wind resource and mitigating its associated uncertainties are crucial to wind farm profitability. The study quantifies the uncertainty due to inter-annual variability, averaging 6.5 % and ranging from 1 % to 14 %, using long-term, quality-controlled wind measurements from tall met masts in terrain of varying complexity. Further, the results indicate that machine learning models are beneficial to mitigate the impact of inter-annual variability in heterogeneous and complex terrain.
Bjarke T. E. Olsen, Andrea N. Hahmann, Nicolas G. Alonso-de-Linaje, Mark Žagar, and Martin Dörenkämper
Geosci. Model Dev., 18, 4499–4533, https://doi.org/10.5194/gmd-18-4499-2025, https://doi.org/10.5194/gmd-18-4499-2025, 2025
Short summary
Short summary
Low-level jets (LLJs) are strong winds in the lower atmosphere that are important for wind energy as turbines get taller. This study compares a weather model (WRF) with real data across five North and Baltic Sea sites. Adjusting the model improved accuracy over the widely used ERA5. In key offshore regions, LLJs occur 10–15 % of the time and significantly boost wind power, especially in spring and summer, contributing up to 30 % of total capacity in some areas.
Farkhondeh (Hanie) Rouholahnejad and Julia Gottschall
Wind Energ. Sci., 10, 143–159, https://doi.org/10.5194/wes-10-143-2025, https://doi.org/10.5194/wes-10-143-2025, 2025
Short summary
Short summary
In wind energy, precise wind speed prediction at hub height is vital. Our study in the Dutch North Sea reveals that the on-site-trained random forest model outperforms the global reanalysis data, ERA5, in accuracy and precision. Trained within a 200 km range, the model effectively extends the wind speed vertically but experiences bias. It also outperforms ERA5 corrected with measurements in capturing wind speed variations and fine wind patterns, highlighting its potential for site assessment.
Martin Georg Jonietz Alvarez, Warren Watson, and Julia Gottschall
Wind Energ. Sci., 9, 2217–2233, https://doi.org/10.5194/wes-9-2217-2024, https://doi.org/10.5194/wes-9-2217-2024, 2024
Short summary
Short summary
Offshore wind measurements are often affected by gaps. We investigated how these gaps affect wind resource assessments and whether filling them reduces their effect. We find that the effect of gaps on the estimated long-term wind resource is lower than expected and that data gap filling does not significantly change the outcome. These results indicate a need to reduce current wind data availability requirements for offshore measurement campaigns.
Lukas Vollmer, Balthazar Arnoldus Maria Sengers, and Martin Dörenkämper
Wind Energ. Sci., 9, 1689–1693, https://doi.org/10.5194/wes-9-1689-2024, https://doi.org/10.5194/wes-9-1689-2024, 2024
Short summary
Short summary
This study proposes a modification to a well-established wind farm parameterization used in mesoscale models. The wind speed at the location of the turbine, which is used to calculate power and thrust, is corrected to approximate the free wind speed. Results show that the modified parameterization produces more accurate estimates of the turbine’s power curve.
Marta Bertelè, Paul J. Meyer, Carlo R. Sucameli, Johannes Fricke, Anna Wegner, Julia Gottschall, and Carlo L. Bottasso
Wind Energ. Sci., 9, 1419–1429, https://doi.org/10.5194/wes-9-1419-2024, https://doi.org/10.5194/wes-9-1419-2024, 2024
Short summary
Short summary
A neural observer is used to estimate shear and veer from the operational data of a large wind turbine equipped with blade load sensors. Comparison with independent measurements from a nearby met mast and profiling lidar demonstrate the ability of the
rotor as a sensorconcept to provide high-quality estimates of these inflow quantities based simply on already available standard operational data.
Markus Sommerfeld, Martin Dörenkämper, Jochem De Schutter, and Curran Crawford
Wind Energ. Sci., 8, 1153–1178, https://doi.org/10.5194/wes-8-1153-2023, https://doi.org/10.5194/wes-8-1153-2023, 2023
Short summary
Short summary
This study investigates the performance of pumping-mode ground-generation airborne wind energy systems by determining power-optimal flight trajectories based on realistic, k-means clustered, vertical wind velocity profiles. These profiles, derived from mesoscale weather simulations at an offshore and an onshore site in Europe, are incorporated into an optimal control model that maximizes average cycle power by optimizing the kite's trajectory.
Moritz Gräfe, Vasilis Pettas, Julia Gottschall, and Po Wen Cheng
Wind Energ. Sci., 8, 925–946, https://doi.org/10.5194/wes-8-925-2023, https://doi.org/10.5194/wes-8-925-2023, 2023
Short summary
Short summary
Inflow wind field measurements from nacelle-based lidar systems offer great potential for different applications including turbine control, load validation and power performance measurements. On floating wind turbines nacelle-based lidar measurements are affected by the dynamic behavior of the floating foundations. Therefore, the effects on lidar wind speed measurements induced by floater dynamics must be well understood. A new model for quantification of these effects is introduced in our work.
Anna von Brandis, Gabriele Centurelli, Jonas Schmidt, Lukas Vollmer, Bughsin' Djath, and Martin Dörenkämper
Wind Energ. Sci., 8, 589–606, https://doi.org/10.5194/wes-8-589-2023, https://doi.org/10.5194/wes-8-589-2023, 2023
Short summary
Short summary
We propose that considering large-scale wind direction changes in the computation of wind farm cluster wakes is of high relevance. Consequently, we present a new solution for engineering modeling tools that accounts for the effect of such changes in the propagation of wakes. The new model is evaluated with satellite data in the German Bight area. It has the potential to reduce uncertainty in applications such as site assessment and short-term power forecasting.
Hugo Rubio, Martin Kühn, and Julia Gottschall
Wind Energ. Sci., 7, 2433–2455, https://doi.org/10.5194/wes-7-2433-2022, https://doi.org/10.5194/wes-7-2433-2022, 2022
Short summary
Short summary
A proper development of offshore wind farms requires the accurate description of atmospheric phenomena like low-level jets. In this study, we evaluate the capabilities and limitations of numerical models to characterize the main jets' properties in the southern Baltic Sea. For this, a comparison against ship-mounted lidar measurements from the NEWA Ferry Lidar Experiment has been implemented, allowing the investigation of the model's capabilities under different temporal and spatial constraints.
Markus Sommerfeld, Martin Dörenkämper, Jochem De Schutter, and Curran Crawford
Wind Energ. Sci., 7, 1847–1868, https://doi.org/10.5194/wes-7-1847-2022, https://doi.org/10.5194/wes-7-1847-2022, 2022
Short summary
Short summary
This research explores the ground-generation airborne wind energy system (AWES) design space and investigates scaling effects by varying design parameters such as aircraft wing size, aerodynamic efficiency and mass. Therefore, representative simulated onshore and offshore wind data are implemented into an AWES trajectory optimization model. We estimate optimal annual energy production and capacity factors as well as a minimal operational lift-to-weight ratio.
Beatriz Cañadillas, Maximilian Beckenbauer, Juan J. Trujillo, Martin Dörenkämper, Richard Foreman, Thomas Neumann, and Astrid Lampert
Wind Energ. Sci., 7, 1241–1262, https://doi.org/10.5194/wes-7-1241-2022, https://doi.org/10.5194/wes-7-1241-2022, 2022
Short summary
Short summary
Scanning lidar measurements combined with meteorological sensors and mesoscale simulations reveal the strong directional and stability dependence of the wake strength in the direct vicinity of wind farm clusters.
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
Short summary
Short summary
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.
Cited articles
Baas, P., Bosveld, F. C., and Burgers, G.: The impact of atmospheric stability on the near-surface wind over sea in storm conditions, Wind Energy, 19, 187–198, https://doi.org/10.1002/we.1825, 2016. a
Carta, J. A., Velázquez, S., and Cabrera, P.: A review of measure-correlate-predict (MCP) methods used to estimate long-term wind characteristics at a target site, Renew. Sust. Energ. Rev., 27, 362–400, https://doi.org/10.1016/j.rser.2013.07.004, 2013. a, b
Chang, T. P.: Performance comparison of six numerical methods in estimating Weibull parameters for wind energy application, Appl. Energ., 88, 272–282, https://doi.org/10.1016/j.apenergy.2010.06.018, 2011. a
Copernicus CDS: Copernicus Climate Data Store, available at: https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels?tab=overview, last access: 8 April 2021. a
Copernicus CMS: Copernicus Marine Service, available at: https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=SST_GLO_SST_L4_NRT_OBSERVATIONS_010_001, last access: 8 April 2021. a
Donlon, C. J., Martin, M., Stark, J., Roberts-Jones, J., Fiedler, E., and Wimmer, W.: The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system, Remote Sens. Environ., 116, 140–158, https://doi.org/10.1016/j.rse.2010.10.017, 2012. a, b
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, b
Dörenkämper, M., Stoevesandt, B., and Heinemann, D.: Derivation of an offshore wind index for the German bight from high-resolution mesoscale simulation data, Proceedings of DEWEK – German Offshore Wind Energy Conference, 5, 17–18 October 2017, available at: http://publica.fraunhofer.de/documents/N-484817.html (last access: 8 April 2021), 2017. a
Dörenkämper, M., Olsen, B. T., Witha, B., Hahmann, A. N., Davis, N. N., Barcons, J., Ezber, Y., García-Bustamante, E., González-Rouco, J. F., Navarro, J., Sastre-Marugán, M., Sīle, T., Trei, W., Žagar, M., Badger, J., Gottschall, J., Sanz Rodrigo, J., and Mann, J.: The Making of the New European Wind Atlas – Part 2: Production and evaluation, Geosci. Model Dev., 13, 5079–5102, https://doi.org/10.5194/gmd-13-5079-2020, 2020. a, b, c, d
FGW e.V.: Technical Guidelines for Wind Turbines – Part 6 (TG6) Determination of Wind Potential and Energy Yield, Richtlinie, Fördergesellschaft Windenergie und andere Dezentrale Energien, Berlin, Germany, 2017. a
FINO2: FINO2 measurement platform – Installation Protocol, Tech. Rep., 152 pp., Wind Consult, Bargeshagen, Germany, 2007. a
FINO3: FINO3 measurement platform – Technical Note, Tech. Rep., 57 pp., GL – Garrad Hassan, GLGH-4257 12 08840 266-T-0001-A, Hamburg, Germany, 2012. 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, 5, 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., 10, 1620, https://doi.org/10.3390/rs10101620, 2018. a
Gryning, S.-E., Badger, J., Hahmann, A. N., and Batchvarova, E.: Current Status and Challenges in Wind Energy Assessment, in: Weather Matters for Energy, edited by Troccoli, A., Dubus, L., and Haupt, S. E., pp. 275–293, Springer, New York, NY, https://doi.org/10.1007/978-1-4614-9221-4_13, 2014. a
Gryning, S.-E., Floors, R., Peña, A., Batchvarova, E., and Brümmer, B.: Weibull Wind-Speed Distribution Parameters Derived from a Combination of Wind-Lidar and Tall-Mast Measurements Over Land, Coastal and Marine Sites, Bound.-Lay. Meteorol., 159, 329–348, https://doi.org/10.1007/s10546-015-0113-x, 2016. a
Hahmann, A. N., Sīle, T., Witha, B., Davis, N. N., Dörenkämper, M., Ezber, Y., García-Bustamante, E., González-Rouco, J. F., Navarro, J., Olsen, B. T., and Söderberg, S.: The making of the New European Wind Atlas – Part 1: Model sensitivity, Geosci. Model Dev., 13, 5053–5078, https://doi.org/10.5194/gmd-13-5053-2020, 2020. a, b, c
Hanslian, D.: The matrix of measure-correlate-predict methods, Proceedings of ICEM 2017, 27–29 June 2017, Bari, Italy, available at: https://www.wemcouncil.org/wp/wp-content/uploads/2017/10/icem_hanslian_20170628_1240_sala_2.pdf (last access: 8 April 2021), 2017. a
Hersbach, H., Bell, B., Berrisford, P., et al.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, qj.3803, https://doi.org/10.1002/qj.3803, 2020. a, b
Kalverla, P., Steeneveld, G.-J., Ronda, R., and Holtslag, A. A.: Evaluation of three mainstream numerical weather prediction models with observations from meteorological mast IJmuiden at the North Sea, Wind Energy, 22, 34–48, https://doi.org/10.1002/we.2267, 2019. a, b
Körner, P., Kronenberg, R., Genzel, S., and Bernhofer, C.: Introducing Gradient Boosting as a universal gap filling tool for meteorological time series, Meteorol. Z., 27, 369–376, https://doi.org/10.1127/metz/2018/0908, 2018. a, b
Leiding, T., Tinz, B., Gates, L., Rosenhagen, G., Herklotz, K., Senet, C., Outzen, O., Lindenthal, A., Neumann, T.,
Frühman, R., Wilts, F., Bégué, F., Schwenk, P., Stein, D., Bastigkeit, I., Lange, B., Hagemann, S., Müller, S., and Schwabe, J.: Standardisierung und vergleichende Analyse der meteorologischen FINO-Messdaten (FINO123), Tech. Rep., Final Report – FINOWind Research Project, Hamburg, Germany, available at: https://www.dwd.de/DE/forschung/projekte/fino_wind/fino_wind_node.html (last access: 8 April 2021), 2012. a
NCAR: WRF Model User’s Page, WRF Version 4.0.1, https://doi.org/10.5065/D6MK6B4K, 2021. a
Olauson, J.: ERA5: The new champion of wind power modelling?, Renew. Energ., 126, 322–331, https://doi.org/10.1016/j.renene.2018.03.056, 2018. a
Pappas, C., Papalexiou, S., and Koutsoyiannis, D.: A quick gap filling of missing hydrometeorological data, J. Geophys. Res.-Atmos., 119, 9290–9300, https://doi.org/10.1127/metz/2018/0908, 2014. a
Peña, A., Gryning, S.-E., and Floors, R.: Lidar observations of marine boundary-layer winds and heights: a preliminary study, Meteorol. Z., 24, 581–589, https://doi.org/10.1127/metz/2015/0636, 2015.
a
Poveda, J. M., Wouters, D., and Nederland, S.: Wind measurements at meteorological mast IJmuiden, Tech. Rep., ECN – Energy Center of the Netherlands, Petten, the Netherlands, available at: https://publicaties.ecn.nl/PdfFetch.aspx?nr=ECN-E--14-058 (last access: 25 October 2019), 2015. a
Rohrig, K., Berkhout, V., Callies, D., Durstewitz, M., Faulstich, S., Hahn, B., Jung, M., Pauscher, L., Seibel, A., Shan, M., Siefert, M., Steffen, J., Collmann, M., Czichon, S., Dörenkämper, M., Gottschall, J., Lange, B., Ruhle, A., Sayer, F., Stoevesandt, B., and Wenske, J.: Powering the 21st century by wind energy–Options, facts, figures, Appl. Phys. Rev., 6, 031 303, https://doi.org/10.1063/1.5089877, 2019. a, b
Skamarock, W., Klemp, J., Dudhia, J., Gill, D., Liu, Z., Berner, J., Wang, W., Powers, J., Duda, M. G., Barker, D., and Huang, X.-Y.: A description of the advanced research WRF version 3, Technical Report, 162 pages NCAR/TN-556+ STR, NCAR – National Center for Atmospheric Research, Boulder, CO, USA, https://doi.org/10.5065/1dfh-6p97, 2019. a, b
Thøgersen, M., Svenningsen, L., and Sørensen, T.: ERA5 – The (Not So) Long Term Reference Wind Data – years 2010–2016, available at: http://www.emd.dk/files/windpro/20170829_ERA5_WindPRO_ReleaseNote.pdf (last access: 8 April 2021), 2017. a
van Bebber, W. J.: Die Zugstrassen der barometrischen Minima, Meteorol. Z., 8, 361–366, 1891. a
Wessel, P. and Smith, W. H. F.: A global, self-consistent, hierarchical, high-resolution shoreline database, J. Geophys. Res.-Sol. Ea., 101, 8741–8743, https://doi.org/10.1029/96JB00104, 1996. a, b
WRF Users Page: WRF Model Physics Options and References, available at: https://www2.mmm.ucar.edu/wrf/users/physics/phys_references.html (last access: 8 April 2021), 2020. a
Altmetrics
Final-revised paper
Preprint