Articles | Volume 18, issue 14
https://doi.org/10.5194/gmd-18-4499-2025
© 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/gmd-18-4499-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
24 Jul 2025
Model evaluation paper |
| 24 Jul 2025
| 24 Jul 2025
Low-level jets in the North and Baltic seas: mesoscale model sensitivity and climatology using WRF V4.2.1
DTU Wind and Energy Systems, Frederiksborgvej 399, 4000 Roskilde, Denmark
Andrea N. Hahmann
DTU Wind and Energy Systems, Frederiksborgvej 399, 4000 Roskilde, Denmark
Nicolas G. Alonso-de-Linaje
DTU Wind and Energy Systems, Frederiksborgvej 399, 4000 Roskilde, Denmark
Mark Žagar
Vestas Wind Systems A/S, Aarhus, Denmark
Martin Dörenkämper
Fraunhofer Institute for Wind Energy Systems, Oldenburg, Germany
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Andreas Bechmann, Juan Pablo M. Leon, Bjarke T. Olsen, and Yavor V. Hristov
Wind Energ. Sci., 5, 1679–1688, https://doi.org/10.5194/wes-5-1679-2020, https://doi.org/10.5194/wes-5-1679-2020, 2020
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When assessing wind resources for wind farm development, the first step is to measure the wind from tall meteorological masts. As met masts are expensive, they are not built at every planned wind turbine position but sparsely while trying to minimize the distance. However, this paper shows that it is better to focus on the
similaritybetween the met mast and the wind turbines than the distance. Met masts at similar positions reduce the uncertainty of wind resource assessments significantly.
Andrea N. Hahmann, Tija Sīle, Björn Witha, Neil N. Davis, Martin Dörenkämper, Yasemin Ezber, Elena García-Bustamante, J. Fidel González-Rouco, Jorge Navarro, Bjarke T. Olsen, and Stefan Söderberg
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Wind energy resource assessment routinely uses numerical weather prediction model output. We describe the evaluation procedures used for picking the suitable blend of model setup and parameterizations for simulating European wind climatology with the WRF model. We assess the simulated winds against tall mast measurements using a suite of metrics, including the Earth Mover's Distance, which diagnoses the performance of each ensemble member using the full wind speed and direction distribution.
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
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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.
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Preprint under review for WES
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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.
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Oscar García-Santiago, Andrea N. Hahmann, Jake Badger, and Alfredo Peña
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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.
Xiaoli Guo Larsén, Marc Imberger, Ásta Hannesdóttir, and Andrea N. Hahmann
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2022-102, https://doi.org/10.5194/wes-2022-102, 2023
Revised manuscript not accepted
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We study how climate change will impact extreme winds and choice of turbine class. We use data from 18 CMIP6 members from a historic and a future period to access the change in the extreme winds. The analysis shows an overall increase in the extreme winds in the North Sea and the southern Baltic Sea, but a decrease over the Scandinavian Peninsula and most of the Baltic Sea. The analysis is inconclusive to whether higher or lower classes of turbines will be installed in the future.
Andrea N. Hahmann, Oscar García-Santiago, and Alfredo Peña
Wind Energ. Sci., 7, 2373–2391, https://doi.org/10.5194/wes-7-2373-2022, https://doi.org/10.5194/wes-7-2373-2022, 2022
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We explore the changes in wind energy resources in northern Europe using output from simulations from the Climate Model Intercomparison Project (CMIP6) under the high-emission scenario. Our results show that climate change does not particularly alter annual energy production in the North Sea but could affect the seasonal distribution of these resources, significantly reducing energy production during the summer from 2031 to 2050.
Graziela Luzia, Andrea N. Hahmann, and Matti Juhani Koivisto
Wind Energ. Sci., 7, 2255–2270, https://doi.org/10.5194/wes-7-2255-2022, https://doi.org/10.5194/wes-7-2255-2022, 2022
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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
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Beatriz Cañadillas, Maximilian Beckenbauer, Juan J. Trujillo, Martin Dörenkämper, Richard Foreman, Thomas Neumann, and Astrid Lampert
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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
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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
Andreas Bechmann, Juan Pablo M. Leon, Bjarke T. Olsen, and Yavor V. Hristov
Wind Energ. Sci., 5, 1679–1688, https://doi.org/10.5194/wes-5-1679-2020, https://doi.org/10.5194/wes-5-1679-2020, 2020
Short summary
Short summary
When assessing wind resources for wind farm development, the first step is to measure the wind from tall meteorological masts. As met masts are expensive, they are not built at every planned wind turbine position but sparsely while trying to minimize the distance. However, this paper shows that it is better to focus on the
similaritybetween the met mast and the wind turbines than the distance. Met masts at similar positions reduce the uncertainty of wind resource assessments significantly.
Andrea N. Hahmann, Tija Sīle, Björn Witha, Neil N. Davis, Martin Dörenkämper, Yasemin Ezber, Elena García-Bustamante, J. Fidel González-Rouco, Jorge Navarro, Bjarke T. Olsen, and Stefan Söderberg
Geosci. Model Dev., 13, 5053–5078, https://doi.org/10.5194/gmd-13-5053-2020, https://doi.org/10.5194/gmd-13-5053-2020, 2020
Short summary
Short summary
Wind energy resource assessment routinely uses numerical weather prediction model output. We describe the evaluation procedures used for picking the suitable blend of model setup and parameterizations for simulating European wind climatology with the WRF model. We assess the simulated winds against tall mast measurements using a suite of metrics, including the Earth Mover's Distance, which diagnoses the performance of each ensemble member using the full wind speed and direction distribution.
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.
Cited articles
Aird, J. A., Barthelmie, R. J., Shepherd, T. J., and Pryor, S. C.: WRF-simulated low-level jets over Iowa: characterization and sensitivity studies, Wind Energ. Sci., 6, 1015–1030, https://doi.org/10.5194/wes-6-1015-2021, 2021. a
Baas, P., Bosveld, F. C., Baltink, H. K., and Holtslag, A. A.: A climatology of nocturnal low-level jets at Cabauw, J. Appl. Meteorol. Climatol., 48, 1627–1642, https://doi.org/10.1175/2009JAMC1965.1, 2009. a, b, c
Bougeault, P. and Lacarrere, P.: Parameterization of Orography-Induced Turbulence in a Mesobeta–Scale Model, Mon. Weather Rev., 117, 1872– 1890, https://doi.org/10.1175/1520-0493(1989)117<1872:POOITI>2.0.CO;2, 1989. a, b
Capon, R. A.: Wind speed-up in the Dover Straits with the Met Office New Dynamics model, Meteorol. Appl., 10, 229–237, https://doi.org/10.1017/S1350482703003037, 2003. a, b, c
Copernicus Land Monitoring Service: CORINE Land Cover, European Environment Agency (EEA), https://land.copernicus.eu/pan-european/corine-land-cover (last access: 15 April 2019), 2019. a
Danielson, J. J. and Gesch, D. B.: Global Multi-Resolution Terrain Elevation Data 2010 (GMTED2010). Report No. OFR 2011-1073, US Geological Survey, https://pubs.usgs.gov/of/2011/1073/pdf/of2011-1073.pdf (last access: 26 May 2025), 2011. a
Darby, L. S., Allwine, K. J., and Banta, R. M.: Nocturnal Low-Level Jet in a Mountain Basin Complex. Part II: Transport and Diffusion of Tracer under Stable Conditions, J. Appl. Meteorol. Climatol., 45, 740–753, https://doi.org/10.1175/JAM2367.1, 2006. a
Davis, N. N., Badger, J., Hahmann, A. N., Hansen, B. O., Mortensen, N. G., Kelly, M., Larsén, X. G., Olsen, B. T., Floors, R., Lizcano, G., Casso, P., Lacave, O., Bosch, A., Bauwens, I., Knight, O. J., van Loon, A. P., Fox, R., Parvanyan, T., Hansen, S. B. K., Heathfield, D., Onninen, M., and Drummond, R.: The Global Wind Atlas: A High-Resolution Dataset of Climatologies and Associated Web-Based Application, B. Am. Meteorol. Soc., 104, E1507–E1525, https://doi.org/10.1175/BAMS-D-21-0075.1, 2023. a
Donlon, C. J., Martin, M., Stark, J. D., Roberts-Jones, J., Fiedler, E., and Wimmer, W.: The Operational Sea Surface Temperature and Sea Ice analysis (OSTIA), Remote Sens. Environ., 116, 140–158, https://doi.org/10.1016/j.rse.2010.10.017, 2012. 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 (data available at: https://map.neweuropeanwindatlas.eu/, last access: 26 May 2025). a, b, c, d, e
Draxl, C., Hahmann, A. N., Peña, A., and Giebel, G.: Evaluating winds and vertical wind shear from Weather Research and Forecasting model forecasts using seven planetary boundary layer schemes, Wind Energy, 17, 39–55, 2014. a
Flamary, R., Courty, N., Gramfort, A., Alaya, M. Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N. T., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D. J., Tavenard, R., Tong, A., and Vayer, T.: POT: Python Optimal Transport, J. Mach. Learn. Res., 22, 1–8, https://jmlr.org/papers/volume22/20-451/20-451.pdf (last access: 5 June 2025), 2021. a, b
Gadde, S. N. and Stevens, R. J. A. M.: Effect of low-level jet height on wind farm performance, J. Renew. Sustain. Energy, 13, 013305, https://doi.org/10.1063/5.0026232, 2021. a, b, c
Gaertner, E., Rinker, J., Sethuraman, L., Zahle, F., Anderson, B., Barter, G. E., Abbas, N. J., Meng, F., Bortolotti, P., Skrzypinski, W., Scott, G., Feil, R., Bredmose, H., Dykes, K., Shields, M., Allen, C., and Viselli, A.: IEA wind TCP task 37: definition of the IEA 15-megawatt offshore reference wind turbine, Tech. rep., National Renewable Energy Lab. (NREL), Golden, CO (United States), 36 pp., https://doi.org/10.2172/1603478, 2020. a
Gottschall, J., Catalano, E., Dörenkämper, M., and Witha, B.: The NEWA Ferry Lidar Experiment: Measuring mesoscalewinds in the Southern Baltic Sea, Remote Sens., 10, 1–13, https://doi.org/10.3390/rs10101620, 2018. a
Gutierrez, W., Ruiz-Columbie, A., Tutkun, M., and Castillo, L.: Impacts of the low-level jet's negative wind shear on the wind turbine, Wind Energy Sci., 2, 533–545, https://doi.org/10.5194/wes-2-533-2017, 2017. a
Gutierrez, W., Ruiz-Columbie, A., Tutkun, M., and Castillo, L.: The structural response of a wind turbine under operating conditions with a low-level jet, Renew. Sustain. Energy Rev., 108, 380–391, https://doi.org/10.1016/j.rser.2019.03.058, 2019. 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
Haikin, N. and Castelli, S. T.: On the Effect of a Low-level Jet on Atmospheric Pollutant Dispersion: A Case Study Over a Coastal Complex Domain, Employing High-Resolution Modelling, Bound.-Lay. Meteorol., 182, 471–495, https://doi.org/10.1007/s10546-021-00661-x, 2022. a
Hallgren, C., Arnqvist, J., Ivanell, S., Körnich, H., Vakkari, V., and Sahlée, E.: Looking for an offshore low-level jet champion among recent reanalyses: A tight race over the baltic sea, Energies, 13, 14, https://doi.org/10.3390/en13143670, 2020. a
Hallgren, C., Arnqvist, J., Nilsson, E., Ivanell, S., Shapkalijevski, M., Thomasson, A., Pettersson, H., and Sahlée, E.: Classification and properties of non-idealized coastal wind profiles – an observational study, Wind Energ. Sci., 7, 1183–1207, https://doi.org/10.5194/wes-7-1183-2022, 2022. a, b
Hallgren, C., Aird, J. A., Ivanell, S., Körnich, H., Barthelmie, R. J., Pryor, S. C., and Sahlée, E.: Brief communication: On the definition of the low-level jet, Wind Energ. Sci., 8, 1651–1658, https://doi.org/10.5194/wes-8-1651-2023, 2023. a, b
Hallgren, C., Aird, J. A., Ivanell, S., Körnich, H., Vakkari, V., Barthelmie, R. J., Pryor, S. C., and Sahlée, E.: Machine learning methods to improve spatial predictions of coastal wind speed profiles and low-level jets using single-level ERA5 data, Wind Energ. Sci., 9, 821–840, https://doi.org/10.5194/wes-9-821-2024, 2024. a
Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R., Picus, M., Hoyer, S., van Kerkwijk, M. H., Brett, M., Haldane, A., del Río, J. F., Wiebe, M., Peterson, P., Gérard-Marchant, P., Sheppard, K., Reddy, T., Weckesser, W., Abbasi, H., Gohlke, C., and Oliphant, T. E.: Array programming with NumPy, Nature, 585, 357–362, https://doi.org/10.1038/s41586-020-2649-2, 2020. 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. D., 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. d., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: Complete ERA5 from 1940: Fifth generation of ECMWF atmospheric reanalyses of the global climate, copernicus Climate Change Service (C3S) Data Store (CDS) [data set], https://doi.org/10.24381/cds.143582cf, 2017. 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., De 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., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteorol. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a, b
Holton, J. R.: The diurnal boundary layer wind oscillation above sloping terrain1, Tellus A, 19, 199, https://doi.org/10.3402/tellusa.v19i2.9766, 1967. a
Hong, S.-Y., Dudhia, J., and Chen, S.-H.: A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation, Mon. Weather Rev., 132, 103–120, https://doi.org/10.1175/1520-0493(2004)132<0103:ARATIM>2.0.CO;2, 2004. a
Hong, S.-Y., Noh, Y., and Dudhia, J.: A new vertical diffusion package with an explicit treatment of entrainment processes, Mon. Weather Rev., 134, 2318–2341, https://doi.org/10.1175/MWR3199.1, 2006. a, b
Hoyer, S. and Hamman, J.: xarray: N-D labeled arrays and datasets in Python, J. Open Res. Softw., 5, 10-10, https://doi.org/10.5334/jors.148, 2017. a
Hunt, J. C. R., Orr, A., Rottman, J. W., and Capon, R.: Coriolis effects in mesoscale flows with sharp changes in surface conditions, Q. J. Roy. Meteorol. Soc., 130, 2703–2731, https://doi.org/10.1256/qj.04.14, 2004. a, b
Hunter, J. D.: Matplotlib: A 2D graphics environment, Comput. Sci. Eng., 9, 90–95, https://doi.org/10.1109/MCSE.2007.55, 2007. a, b
Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S. A., and Collins, W. D.: Radiative forcing by long–lived greenhouse gases: Calculations with the AER radiative transfer models, J. Geophys. Res., 113, D13103, https://doi.org/10.1029/2008JD009944, 2008. a
Janjić, Z. I.: The Step-Mountain Eta Coordinate Model: Further Developments of the Convection, Viscous Sublayer, and Turbulence Closure Schemes, Mon. Weather Rev., 122, 927–945, https://doi.org/10.1175/1520-0493(1994)122<0927:TSMECM>2.0.CO;2, 1994. a, b
Jimenez, P. A., Dudhia, J., Gonzalez-Rouco, J. F., Navarro, J., Montavez, J. P., and Garcia-Bustamante, E.: A Revised Scheme for the WRF Surface Layer Formulation, Mon. Weather Rev., 140, 898–918, https://doi.org/10.1175/MWR-D-11-00056.1, 2012. a, b, c
Jong, E. D., Quon, E., and Zalkind, D.: Idealized Offshore Low-Level Jets for Turbine Structural Impact Considerations Idealized Offshore Low-Level Jets for Turbine Structural Impact Considerations, Wind Energy, https://www.authorea.com/doi/full/10.22541/au.170664363.31958897 (last access: 26 May 2025), 2024. a
Kain, J. S.: The Kain–Fritsch convective parameterization: An update, J. Appl. Meteorol. Climatol., 43, 170–181, https://doi.org/10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2, 2004. a
Kalverla, P. C., Steeneveld, G.-J., Ronda, R. J., and Holtslag, A. A.: An observational climatology of anomalous wind events at offshore meteomast IJmuiden (North Sea), J. Wind Eng. Indust. Aerodynam., 165, 86–99, 2017. a
Kantorovich, L. V.: Mathematical methods of organizing and planning production, Manage. Sci., 6, 366–422, 1960. a
Kelso, N. V. and Patterson, T.: Introducing natural earth data – http://www.naturalearthdata.com/, Geogr. Tech. Special Issue 2010, 82–89, https://technicalgeography.org/pdf/sp_i_2010/12_introducing_natural_earth_data__naturaleart.pdf (last access: 26 May 2025), 2010. a
Kleczek, M. A., Steeneveld, G.-J., and Holtslag, A. A.: Evaluation of the weather research and forecasting mesoscale model for GABLS3: impact of boundary-layer schemes, boundary conditions and spin-up, Bound.-Lay. Meteorol., 152, 213–243, 2014. a
Lam, S. K., Pitrou, A., and Seibert, S.: Numba: A llvm-based python jit compiler, in: Proceedings of the Second Workshop on the LLVM Compiler Infrastructure in HPC, 1–6 pp., https://doi.org/10.1145/2833157.2833162, 2015. a
Luiz, E. W. and Fiedler, S.: Global Climatology of Low‐Level‐Jets: Occurrence, Characteristics, and Meteorological Drivers, J. Geophys. Res.-Atmos., 129, 9, https://doi.org/10.1029/2023JD040262, 2024. a, b, c
Miller, S., Keim, B., Talbot, R., and Mao, H.: Sea breeze: Structure, forecasting, and impacts, Rev. Geophys., 41, 1011, https://doi.org/10.1029/2003RG000124, 2003. a
Nakanishi, M. and Niino, H.: Development of an improved turbulence closure model for the atmospheric boundary layer., J. Meteor. Soc. Japan, 87, 895–912, https://doi.org/10.2151/jmsj.87.895, 2009. a, b, c
Nunalee, C. G. and Basu, S.: Mesoscale modeling of coastal low-level jets: implications for offshore wind resource estimation, Wind Energy, 17, 1199–1216, https://doi.org/10.1002/we.1628, 2014. a
Olsen, B. T., Hahmann, A. N., and Gonzalez Alonso de Linaje, N. D.: Code and data for “Low-level jets in the North and Baltic Seas: Mesoscale Model Sensitivity and Climatology using WRF V4.2.1”, Zenodo [code], https://doi.org/10.5281/zenodo.14354487, 2025. a
Olsen, B. T. E., Hahmann, A. N., and Alonso-de Linaje, N. G.: Mesoscale Low-Level Jet Climatology for the North and Baltic Seas, Zenodo [data set], https://doi.org/10.5281/zenodo.13154214, 2024. a, b
Olson, J. B., Kenyon, J. S., Angevine, W., Brown, J. M., Pagowski, M., and Sušelj, K.: A description of the MYNN-EDMF scheme and the coupling to other components in WRF–ARW, Tech. rep., NOAA, 42 pp., https://doi.org/10.25923/n9wm-be49, 2019. a, b, c, d
Peña, A.: Østerild: A natural laboratory for atmospheric turbulence, J. Renew. Sustain. Energy, 11, 063302, https://doi.org/10.1063/1.5121486, 2019. a, b
Porté-Agel, F., Bastankhah, M., and Shamsoddin, S.: Wind-Turbine and Wind-Farm Flows: A Review, Bound.-Lay. Meteorol., 174, 1–59, https://doi.org/10.1007/s10546-019-00473-0, 2020. a
Poulos, G. S., Blumen, W., Fritts, D. C., Lundquist, J. K., Sun, J., Burns, S. P., Nappo, C., Banta, R., Newsom, R., Cuxart, J., Terradellas, E., Balsley, B., and Jensen, M.: CASES-99: A Comprehensive Investigation of the Stable Nocturnal Boundary Layer, B. Am. Meteorol. Soc., 83, 555–582, https://doi.org/10.1175/1520-0477(2002)083<0555:CACIOT>2.3.CO;2, 2002. a
Poulter, B., MacBean, N., Hartley, A., Khlystova, I., Arino, O., Betts, R., Bontemps, S., Boettcher, M., Brockmann, C., Defourny, P., Hagemann, S., Herold, M., Kirches, G., Lamarche, C., Lederer, D., Ottlé, C., Peters, M., and Peylin, P.: Plant functional type classification for earth system models: results from the European Space Agency's Land Cover Climate Change Initiative, Geosci. Model Dev., 8, 2315–2328, https://doi.org/10.5194/gmd-8-2315-2015, 2015. a
Redfern, S., Optis, M., Xia, G., and Draxl, C.: Offshore wind energy forecasting sensitivity to sea surface temperature input in the Mid-Atlantic, Wind Energ. Sci., 8, 1–23, https://doi.org/10.5194/wes-8-1-2023, 2023. a
Rife, D. L., Pinto, J. O., Monaghan, A. J., Davis, C. A., and Hannan, J. R.: Global Distribution and Characteristics of Diurnally Varying Low-Level Jets, J. Climate, 23, 5041–5064, https://doi.org/10.1175/2010JCLI3514.1, 2010. a
Rijo, N., Semedo, A., Miranda, P. M., Lima, D., Cardoso, R. M., and Soares, P. M.: Spatial and temporal variability of the Iberian Peninsula coastal low-level jet, Int. J. Climatol., 38, 1605–1622, https://doi.org/10.1002/joc.5303, 2018. a
Rubner, Y., Tomasi, C., and Guibas, L. J.: A metric for distributions with applications to image databases, in: Sixth international conference on computer vision (IEEE Cat. No. 98CH36271), 59–66 pp., IEEE, https://doi.org/10.1109/ICCV.1998.710701, 1998. a
Sheridan, L. M., Krishnamurthy, R., Gustafson Jr., W. I., Liu, Y., Gaudet, B. J., Bodini, N., Newsom, R. K., and Pekour, M.: Offshore low-level jet observations and model representation using lidar buoy data off the California coast, Wind Energ. Sci., 9, 741–758, https://doi.org/10.5194/wes-9-741-2024, 2024. a, b
Shin, H. H. and Hong, S.-Y.: Intercomparison of planetary boundary-layer parametrizations in the WRF model for a single day from CASES-99, Bound.-Lay. Meteorol., 139, 261–281, 2011. a
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Liu, Z., Berner, J., Wang, W., Powers, J. G., Duda, M. G., Barker, D. M., and Huang, X.-Y.: A description of the advanced research WRF model version 4, National Center for Atmospheric Research: Boulder, CO, USA, 145, 145, https://doi.org/10.5065/1dfh-6p97, 2019. a, b
Smedman, A.-S., Bergström, H., and Högström, U.: Spectra, variances and length scales in a marine stable boundary layer dominated by a low level jet, Bound.-Lay. Meteorol., 76, 211–232, 1995. a
Smedman, A. S., Bergström, H., and Grisogono, B.: Evolution of stable internal boundary layers over a cold sea, J. Geophys. Res.-Oceans, 102, 1091–1099, https://doi.org/10.1029/96jc02782, 1997. a
Stensrud, D. J.: Importance of low-level jets to climate: A review, J. Climate, 9, 1698–1711, https://doi.org/10.1175/1520-0442(1996)009<1698:IOLLJT>2.0.CO;2, 1996. a, b
Tastula, E. M., Galperin, B., Dudhia, J., Lemone, M. A., Sukoriansky, S., and Vihma, T.: Methodical assessment of the differences between the QNSE and MYJ PBL schemes for stable conditions, Q. J. Roy. Meteorol. Soc., 141, 2077–2089, https://doi.org/10.1002/qj.2503, 2015. a
Tewari, M., Chen, F., Wang, W., Dudhia, J., LeMone, M. A., Mitchell, K., Ek, M., Gayno, G., Wegiel, J., and Cuenca, R. H.: Implementation and verification of the unified Noah land surface model in the WRF model, in: 20th conference on weather analysis and forecasting/16th conference on numerical weather prediction, Seattle, 12–16 January 2004, AMS, https://n2t.org/ark:/85065/d7fb523p (last access: 26 May 2025), 2004. a
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., Van der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., Carey, C. J., Polat, I., Feng, Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman, R., Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Riberio, A. H., Pedregosa, F., and Mulbregt, P. v.: SciPy 1.0: fundamental algorithms for scientific computing in Python, Nat. Methods, 17, 261–272, 2020. a, b
Wagner, D., Steinfeld, G., Witha, B., Wurps, H., and Reuder, J.: Low level jets over the southern North Sea, Meteorol. Z., 28, 389–415, https://doi.org/10.1127/metz/2019/0948, 2019. a, b
Wagner, R., Cañadillas, B., Clifton, A., Feeney, S., Nygaard, N., Poodt, M., St Martin, C., Tüxen, E., and Wagenaar, J.: Rotor equivalent wind speed for power curve measurement–comparative exercise for IEA Wind Annex 32, in: Journal of Physics: Conference Series, Vol. 524, p. 012108, IOP Publishing, https://doi.org/10.1088/1742-6596/524/1/012108, 2014. a
Wijnant, I., van Ulft, B., van Stratum, B., Barkmeijer, J., Onvlee, J., de Valk, C., Knoop, S., Kok, S., Marseille, G., Baltink, H. K., and Stepek, A.: The dutch offshore wind atlas (dowa): Description of the dataset, Royal Netherlands Meteorological Institute, Ministry of Infrastructure and Water Management, De Bilt, https://cdn.knmi.nl/knmi/pdf/bibliotheek/knmipubTR/TR380.pdf (last access: 26 May 2025), 2019. a
Zhang, X., Bao, J.-W., Chen, B., and Grell, E. D.: A Three-Dimensional Scale-Adaptive Turbulent Kinetic Energy Scheme in the WRF-ARW Model, Mon. Weather Rev., 146, 2023–2045, https://doi.org/10.1175/MWR-D-17-0356.1, 2018. a, b
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.
Low-level jets (LLJs) are strong winds in the lower atmosphere that are important for wind...
