Articles | Volume 7, issue 3
https://doi.org/10.5194/wes-7-1183-2022
© 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/wes-7-1183-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Classification and properties of non-idealized coastal wind profiles – an observational study
Christoffer Hallgren
CORRESPONDING AUTHOR
Department of Earth Sciences, Uppsala University, Uppsala, Sweden
Johan Arnqvist
Department of Earth Sciences, Uppsala University, Uppsala, Sweden
Erik Nilsson
Department of Earth Sciences, Uppsala University, Uppsala, Sweden
Stefan Ivanell
Department of Earth Sciences, Uppsala University, Uppsala, Sweden
Metodija Shapkalijevski
Swedish Meteorological and Hydrological Institute, Norrköping, Sweden
August Thomasson
Department of Earth Sciences, Uppsala University, Uppsala, Sweden
Heidi Pettersson
Finnish Meteorological Institute, Helsinki, Finland
Erik Sahlée
Department of Earth Sciences, Uppsala University, Uppsala, Sweden
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Christoffer Hallgren, Jeanie A. Aird, Stefan Ivanell, Heiner Körnich, Ville Vakkari, Rebecca J. Barthelmie, Sara C. Pryor, and Erik Sahlée
Wind Energ. Sci., 9, 821–840, https://doi.org/10.5194/wes-9-821-2024, https://doi.org/10.5194/wes-9-821-2024, 2024
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Knowing the wind speed across the rotor of a wind turbine is key in making good predictions of the power production. However, models struggle to capture both the speed and the shape of the wind profile. Using machine learning methods based on the model data, we show that the predictions can be improved drastically. The work focuses on three coastal sites, spread over the Northern Hemisphere (the Baltic Sea, the North Sea, and the US Atlantic coast) with similar results for all sites.
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Low-level jets (LLJs) are special types of non-ideal wind profiles affecting both wind energy production and loads on a wind turbine. However, among LLJ researchers, there is no consensus regarding which definition to use to identify these profiles. In this work, we compare two different ways of identifying the LLJ – the falloff definition and the shear definition – and argue why the shear definition is better suited to wind energy applications.
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Preprint withdrawn
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Sometimes, the wind changes direction between the bottom and top part of a wind turbine. This affects both the power production and the loads on the turbine. In this study, a climatology of pronounced changes in wind direction across the rotor is created, focusing on Scandinavia. The weather conditions responsible for these changes in wind direction are investigated and the climatology is compared to measurements from two coastal sites, indicating an underestimation by the climatology.
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As wind power becomes more popular, there is a growing demand for accurate power production forecasts. In this paper we investigated different methods to improve wind power forecasts for an offshore location in the Baltic Sea, using both simple and more advanced techniques. The performance of the methods is evaluated for different weather conditions. Smoothing the forecast was found to be the best method in general, but we recommend selecting which method to use based on the forecasted weather.
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Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-166, https://doi.org/10.5194/wes-2025-166, 2025
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The Lattice Boltzmann Method is a new method for very fast and accurate wind farm flow simulations. However, information on this method is scattered and recent developments are unknown amongst the wind energy community. This review structures the different aspects of the method and answers common questions about it for wind energy researchers. We find that many of the building blocks for a wind farm simulation tool are present and that the LBM is accurate and efficient.
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This work presents an investigation into varying modelling choices for large eddy simulation over realistic forests. The focus is on how to represent the impact of upstream forest cover on the wind statistics. The work clearly demonstrates the advantage of using an explicit drag formulation together with forest density maps from airborne laser scans over using roughness length and displacement height, mainly because it leverages observable quantities and minimizes the impact uncertain choices.
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EGUsphere, https://doi.org/10.5194/egusphere-2025-2936, https://doi.org/10.5194/egusphere-2025-2936, 2025
This preprint is open for discussion and under review for Ocean Science (OS).
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Strong motions caused by surface waves can set the material at the bottom in motion. How strong the wave motions need to be depends on the bottom type, for example mud or sand. We estimated how often wave can lift particles from the bottom. Tests with sea floor samples in the laboratory showed that the required wave force can be much larger in reality compared to models that are only based on the grain size of the sea floor. These differences are explained by biological activity at the bottom.
Stefan Ivanell, Warit Chanprasert, Luca Lanzilao, James Bleeg, Johan Meyers, Antoine Mathieu, Søren Juhl Andersen, Rem-Sophia Mouradi, Eric Dupont, Hugo Olivares-Espinosa, and Niels Troldborg
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-88, https://doi.org/10.5194/wes-2025-88, 2025
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Both extreme river discharge and storm surges can interact at the coast and lead to flooding. However, it is difficult to predict flood levels during such compound events because they are rare and complex. Here, we focus on the quantification of uncertainties and investigate the sources of limitations while carrying out such analyses at Halmstad, Sweden. Based on a sensitivity analysis, we emphasize that both the choice of data source and statistical methodology influence the results.
Mohammad Mehdi Mohammadi, Hugo Olivares-Espinosa, Gonzalo Pablo Navarro Diaz, and Stefan Ivanell
Wind Energ. Sci., 9, 1305–1321, https://doi.org/10.5194/wes-9-1305-2024, https://doi.org/10.5194/wes-9-1305-2024, 2024
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This paper has put forward a set of recommendations regarding the actuator sector model implementation details to improve the capability of the model to reproduce similar results compared to those obtained by an actuator line model, which is one of the most common ways used for numerical simulations of wind farms, while providing significant computational savings. This includes among others the velocity sampling method and a correction of the sampled velocities to calculate the blade forces.
Christoffer Hallgren, Jeanie A. Aird, Stefan Ivanell, Heiner Körnich, Ville Vakkari, Rebecca J. Barthelmie, Sara C. Pryor, and Erik Sahlée
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Short summary
Low-level jets (LLJs) are special types of non-ideal wind profiles affecting both wind energy production and loads on a wind turbine. However, among LLJ researchers, there is no consensus regarding which definition to use to identify these profiles. In this work, we compare two different ways of identifying the LLJ – the falloff definition and the shear definition – and argue why the shear definition is better suited to wind energy applications.
Christoffer Hallgren, Heiner Körnich, Stefan Ivanell, Ville Vakkari, and Erik Sahlée
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-129, https://doi.org/10.5194/wes-2023-129, 2023
Preprint withdrawn
Short summary
Short summary
Sometimes, the wind changes direction between the bottom and top part of a wind turbine. This affects both the power production and the loads on the turbine. In this study, a climatology of pronounced changes in wind direction across the rotor is created, focusing on Scandinavia. The weather conditions responsible for these changes in wind direction are investigated and the climatology is compared to measurements from two coastal sites, indicating an underestimation by the climatology.
Gonzalo Pablo Navarro Diaz, Alejandro Daniel Otero, Henrik Asmuth, Jens Nørkær Sørensen, and Stefan Ivanell
Wind Energ. Sci., 8, 363–382, https://doi.org/10.5194/wes-8-363-2023, https://doi.org/10.5194/wes-8-363-2023, 2023
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In this paper, the capacity to simulate transient wind turbine wake interaction problems using limited wind turbine data has been extended. The key novelty is the creation of two new variants of the actuator line technique in which the rotor blade forces are computed locally using generic load data. The analysis covers a partial wake interaction case between two wind turbines for a uniform laminar inflow and for a turbulent neutral atmospheric boundary layer inflow.
Lucía Gutiérrez-Loza, Erik Nilsson, Marcus B. Wallin, Erik Sahlée, and Anna Rutgersson
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Anna Rutgersson, Erik Kjellström, Jari Haapala, Martin Stendel, Irina Danilovich, Martin Drews, Kirsti Jylhä, Pentti Kujala, Xiaoli Guo Larsén, Kirsten Halsnæs, Ilari Lehtonen, Anna Luomaranta, Erik Nilsson, Taru Olsson, Jani Särkkä, Laura Tuomi, and Norbert Wasmund
Earth Syst. Dynam., 13, 251–301, https://doi.org/10.5194/esd-13-251-2022, https://doi.org/10.5194/esd-13-251-2022, 2022
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A natural hazard is a naturally occurring extreme event with a negative effect on people, society, or the environment; major events in the study area include wind storms, extreme waves, high and low sea level, ice ridging, heavy precipitation, sea-effect snowfall, river floods, heat waves, ice seasons, and drought. In the future, an increase in sea level, extreme precipitation, heat waves, and phytoplankton blooms is expected, and a decrease in cold spells and severe ice winters is anticipated.
Jari Walden, Liisa Pirjola, Tuomas Laurila, Juha Hatakka, Heidi Pettersson, Tuomas Walden, Jukka-Pekka Jalkanen, Harri Nordlund, Toivo Truuts, Miika Meretoja, and Kimmo K. Kahma
Atmos. Chem. Phys., 21, 18175–18194, https://doi.org/10.5194/acp-21-18175-2021, https://doi.org/10.5194/acp-21-18175-2021, 2021
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Ship emissions play an important role in the deposition of gaseous compounds and nanoparticles (Ntot), affecting climate, human health (especially in coastal areas), and eutrophication. Micrometeorological methods showed that ship emissions were mainly responsible for the deposition of Ntot, whereas they only accounted for a minor proportion of CO2 deposition. An uncertainty analysis applied to the fluxes and fuel sulfur content results demonstrated the reliability of the results.
Christoffer Hallgren, Stefan Ivanell, Heiner Körnich, Ville Vakkari, and Erik Sahlée
Wind Energ. Sci., 6, 1205–1226, https://doi.org/10.5194/wes-6-1205-2021, https://doi.org/10.5194/wes-6-1205-2021, 2021
Short summary
Short summary
As wind power becomes more popular, there is a growing demand for accurate power production forecasts. In this paper we investigated different methods to improve wind power forecasts for an offshore location in the Baltic Sea, using both simple and more advanced techniques. The performance of the methods is evaluated for different weather conditions. Smoothing the forecast was found to be the best method in general, but we recommend selecting which method to use based on the forecasted weather.
Jan-Victor Björkqvist, Sander Rikka, Victor Alari, Aarne Männik, Laura Tuomi, and Heidi Pettersson
Nat. Hazards Earth Syst. Sci., 20, 3593–3609, https://doi.org/10.5194/nhess-20-3593-2020, https://doi.org/10.5194/nhess-20-3593-2020, 2020
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Wave observations have a fundamental uncertainty due to the randomness of the sea state. Such scatter is absent in model data, and we tried two methods to best account for this difference when combining measured and modelled wave heights. The results were used to estimate how rare a 2019 storm in the Bothnian Sea was. Both methods were found to have strengths and weaknesses, but our best estimate was that, in the current climate, such a storm might on average repeat about once a century.
Søren Juhl Andersen, Simon-Philippe Breton, Björn Witha, Stefan Ivanell, and Jens Nørkær Sørensen
Wind Energ. Sci., 5, 1689–1703, https://doi.org/10.5194/wes-5-1689-2020, https://doi.org/10.5194/wes-5-1689-2020, 2020
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The complexity of wind farm operation increases as the wind farms get larger and larger. Therefore, researchers from three universities have simulated numerous different large wind farms as part of an international benchmark. The study shows how simple engineering models can capture the general trends, but high-fidelity simulations are required in order to quantify the variability and uncertainty associated with power production of the wind farms and hence the potential profitability and risks.
Johan Arnqvist, Julia Freier, and Ebba Dellwik
Biogeosciences, 17, 5939–5952, https://doi.org/10.5194/bg-17-5939-2020, https://doi.org/10.5194/bg-17-5939-2020, 2020
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Data generated by airborne laser scans enable the characterization of surface vegetation for any application that might need it, such as forest management, modeling for numerical weather prediction, or wind energy estimation. In this work we present a new algorithm for calculating the vegetation density using data from airborne laser scans. The new routine is more robust than earlier methods, and an implementation in popular programming languages accompanies the article to support new users.
Cited articles
Aird, J. A., Barthelmie, R. J., Shepherd, T. J., and Pryor, S. C.: Occurrence
of Low-Level Jets over the Eastern US Coastal Zone at Heights Relevant to
Wind Energy, Energies, 15, 445, https://doi.org/10.3390/en15020445, 2022. a, b, c
Andreas, E. L., Claffy, K. J., and Makshtas, A. P.: Low-level atmospheric jets
and inversions over the western Weddell Sea, Bound.-Lay. Meteorol., 97,
459–486, https://doi.org/10.1023/A:1002793831076, 2000. a
Barthelmie, R. J., Badger, J., Pryor, S. C., Hasager, C. B., Christiansen,
M. B., and Jørgensen, B.: Offshore coastal wind speed gradients: Issues for
the design and development of large offshore windfarms, Wind Engineering, 31,
369–382, https://doi.org/10.1260/030952407784079762, 2007. a
Blackadar, A. K.: Boundary layer wind maxima and their significance for the
growth of nocturnal inversions,
B. Am. Meteorol. Soc., 38, 283–290, https://doi.org/10.1175/1520-0477-38.5.283, 1957. a, b
Brock, F. V.: A Nonlinear Filter to Remove Impulse Noise from Meteorological
Data, J. Atmos. Ocean. Tech., 3, 51–58,
https://doi.org/10.1175/1520-0426(1986)003<0051:ANFTRI>2.0.CO;2, 1986. a
Brugger, P., Markfort, C., and Porté-Agel, F.: Field measurements of wake meandering at a utility-scale wind turbine with nacelle-mounted Doppler lidars, Wind Energ. Sci., 7, 185–199, https://doi.org/10.5194/wes-7-185-2022, 2022. a
Carpenter, J. R., Sommer, T., and Wüest, A.: Simulations of a double-diffusive
interface in the diffusive convection regime, J. Fluid Mech.,
711, 411–436, https://doi.org/10.1017/jfm.2012.399, 2012. a
COWI: Study on Baltic Offshore Wind Energy Cooperation under BEMIP: Final
Report; Publications Office of the European Union: Luxembourg, Tech. rep., COWI, Directorate-General for Energy (European
Commission), Ea Energy Analyses and THEMA Consulting Group, https://doi.org/10.2833/864823, 2019. a
Debnath, M., Doubrawa, P., Optis, M., Hawbecker, P., and Bodini, N.: Extreme wind shear events in US offshore wind energy areas and the role of induced stratification, Wind Energ. Sci., 6, 1043–1059, https://doi.org/10.5194/wes-6-1043-2021, 2021. a
Dimitrov, N., Natarajan, A., and Kelly, M.: Model of wind shear conditional on
turbulence and its impact on wind turbine loads, Wind Energy, 18, 1917–1931,
https://doi.org/10.1002/we.1797, 2015. a
Drennan, W. M., Kahma, K. K., and Donelan, M. A.: On momentum flux and velocity
spectra over waves, Bound.-Lay. Meteorol., 92, 489–515,
https://doi.org/10.1023/A:1002054820455, 1999. a
Duarte, H. F., Leclerc, M. Y., and Zhang, G.: Assessing the shear-sheltering
theory applied to low-level jets in the nocturnal stable boundary layer,
Theor. Appl. Climatol., 110, 359–371,
https://doi.org/10.1007/s00704-012-0621-2, 2012. a, b, c
Elliott, D. L. and Cadogan, J. B.: Effects of wind shear and turbulence on wind turbine power curves, Tech. rep., Pacific Northwest Lab., Richland, WA, USA, https://www.osti.gov/biblio/6348447 (last access: 3 June 2022), 1990. a
Finnigan, J. and Einaudi, F.: The interaction between an internal gravity wave
and the planetary boundary layer. Part II: Effect of the wave on the
turbulence structure,
Q. J. Roy. Meteor. Soc.,
107, 807–832, https://doi.org/10.1002/qj.49710745405, 1981. a
Finnigan, J., Einaudi, F., and Fua, D.: The interaction between an internal
gravity wave and turbulence in the stably-stratified nocturnal boundary
layer, J. Atmos. Sci., 41, 2409–2436,
https://doi.org/10.1175/1520-0469(1984)041<2409:TIBAIG>2.0.CO;2, 1984. a
Fisher, E. L.: An observational study of the sea breeze, J. Meteorol., 17, 645–660,
https://doi.org/10.1175/1520-0469(1960)017<0645:AOSOTS>2.0.CO;2, 1960. a
Foken, T.: 50 years of the Monin–Obukhov similarity theory,
Bound.-Lay. Meteorol., 119, 431–447, https://doi.org/10.1007/s10546-006-9048-6, 2006. a
Foken, T., Leuning, R., Oncley, S. R., Mauder, M., and Aubinet, M.: Corrections and Data Quality Control, in: Eddy Covariance, Springer Atmospheric Sciences, edited by: Aubinet, M., Vesala, T., and Papale, D., Springer, Dordrecht, https://doi.org/10.1007/978-94-007-2351-1_4, 2012. a
Frost, L.: Klassificering av Low Level Jets och analys av den termiska vinden
över Östergarnsholm [Classification of low-level jets and analysis of the thermal wind over Östergarnsholm], MS thesis, Meteorology and Atmospheric Sciences, Uppsala University,
http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-303769 (last access: 3 June 2022), 2004. a
Gadde, S. N. and Stevens, R. J.: Interaction between low-level jets and wind
farms in a stable atmospheric boundary layer, Physical Review Fluids, 6,
014603, https://doi.org/10.1103/PhysRevFluids.6.014603, 2021. a
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, https://doi.org/10.2172/1603478, 2020. a, b
Grachev, A. A. and Fairall, C. W.: Upward momentum transfer in the marine
boundary layer, J. Phys. Oceanogr., 31, 1698–1711,
https://doi.org/10.1175/1520-0485(2001)031<1698:UMTITM>2.0.CO;2, 2001. a
Grisogono, B., Kraljević, L., and Jeričević, A.: The low-level katabatic jet
height versus Monin–Obukhov height, Q. J. Roy. Meteor. Soc., 133, 2133–2136, https://doi.org/10.1002/qj.190, 2007. 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 Energ. Sci., 2, 533–545, https://doi.org/10.5194/wes-2-533-2017, 2017. a
Gutiérrez-Loza, L., Wallin, M. B., Sahlée, E., Nilsson, E., Bange, H. W.,
Kock, A., and Rutgersson, A.: Measurement of air-sea methane fluxes in the
Baltic Sea using the eddy covariance method, Front. Earth Sci., 7,
93, https://doi.org/10.3389/feart.2019.00093, 2019. a, b, c
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, 3670,
https://doi.org/10.3390/en13143670, 2020. a, b, c, d
Hanazaki, H. and Hunt, J. C. R.: Structure of unsteady stably stratified
turbulence with mean shear, J. Fluid Mech., 507, 1–42,
https://doi.org/10.1017/S0022112004007888, 2004. a
Hanley, K. E. and Belcher, S. E.: Wave-driven wind jets in the marine
atmospheric boundary layer, J. Atmos. Sci., 65,
2646–2660, https://doi.org/10.1175/2007JAS2562.1, 2008. a, b
Hanley, K. E., Belcher, S. E., and Sullivan, P. P.: A global climatology of
wind–wave interaction, J. Phys. Oceanogr., 40, 1263–1282,
https://doi.org/10.1175/2010JPO4377.1, 2010. a
Holmboe, J.: On the behavior of symmetric waves in stratified shear layers,
Geofysiske Publikasjoner, 24, 67–113, 1962. a
Hunt, J. C. R. and Durbin, P. A.: Perturbed vortical layers and shear
sheltering, Fluid Dyn. Res., 24, 375, https://doi.org/10.1016/s0169-5983(99)00009-x, 1999. a, b, c, d
Högström, U.: Review of some basic characteristics of the atmospheric surface
layer, Bound.-Lay. Meteorol., 78, 215–246, https://doi.org/10.1007/BF00120937,
1996. a
Högström, U., Rutgersson, A., Sahlée, E., Smedman, A.-S., Hristov, T. S.,
Drennan, W., and Kahma, K.: Air–sea interaction features in the Baltic Sea
and at a Pacific trade-wind site: An inter-comparison study, Bound.-Lay. Meteorol., 147, 139–163, https://doi.org/10.1007/s10546-012-9776-8, 2013. a
Högström, U., Sahlée, E., Smedman, A.-S., Rutgersson, A., Nilsson, E.,
Kahma, K. K., and Drennan, W. M.: The transition from downward to upward
air–sea momentum flux in swell-dominated light wind conditions, J. Atmos. Sci., 75, 2579–2588, https://doi.org/10.1175/JAS-D-17-0334.1,
2018. a
IEA: Offshore Wind Outlook 2019,
https://www.iea.org/reports/offshore-wind-outlook-2019 (last access: 3 June 2022), 2019. a
Iungo, G. V. and Porté-Agel, F.: Volumetric lidar scanning of wind turbine
wakes under convective and neutral atmospheric stability regimes, J. Atmos. Ocean. Technol., 31, 2035–2048,
https://doi.org/10.1175/JTECH-D-13-00252.1, 2014. a
Janssen, P. A. and Komen, G. J.: Effect of atmospheric stability on the growth
of surface gravity waves, Bound.-Lay. Meteorol., 32, 85–96,
https://doi.org/10.1007/BF00120715, 1985. a
Kaimal, J. C. and Finnigan, J. J.: Atmospheric Boundary Layer Flows: Their
Structure and Measurement, Oxford University Press, New York, Oxford
Scholarship Online, 2020, https://doi.org/10.1093/oso/9780195062397.001.0001, 1994. a
Kaimal, J. C., Wyngaard, J. C. J., Izumi, Y., and Coté, O. R.: Spectral
characteristics of surface-layer turbulence, Q. J. Roy. Meteor. Soc., 98, 563–589, https://doi.org/10.1002/qj.49709841707, 1972. a, b
Kalverla, P. C., Steeneveld, G.-J., Ronda, R. J., and Holtslag, A. A. M.: An
observational climatology of anomalous wind events at offshore meteomast
IJmuiden (North Sea), J. Wind Eng. Ind. Aerod., 165, 86–99, https://doi.org/10.1016/j.jweia.2017.03.008, 2017. a
Kalverla, P. C., Duncan Jr., J. B., Steeneveld, G.-J., and Holtslag, A. A. M.: Low-level jets over the North Sea based on ERA5 and observations: together they do better, Wind Energ. Sci., 4, 193–209, https://doi.org/10.5194/wes-4-193-2019, 2019a. a
Kalverla, P. C., Steeneveld, G.-J., Ronda, R. J., and Holtslag, A. A. M.:
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, 2019b. a
Kalverla, P. C., Holtslag, A. A. M., Ronda, R. J., and Steeneveld, G.-J.:
Quality of wind characteristics in recent wind atlases over the North Sea,
Q. J. Roy. Meteor. Soc., 146, 1498–1515,
https://doi.org/10.1002/qj.3748, 2020. a
Karipot, A., Leclerc, M. Y., Zhang, G., Lewin, K. F., Nagy, J., Hendrey, G. R.,and Starr, G.: Influence of nocturnal low-level jet on turbulence structure and CO2 flux measurements over a forest canopy, J. Geophys. Res.-Atmos., 113, D10102, https://doi.org/10.1029/2007JD009149, 2008. a
Kettle, A. J.: Unexpected vertical wind speed profiles in the boundary layer
over the southern North Sea, J. Wind Eng. Ind. Aerod., 134, 149–162, https://doi.org/10.1016/j.jweia.2014.07.012, 2014. a
Kotroni, V. and Lagouvardos, K.: Low-level jet streams associated with
atmospheric cold fronts: Seven case studies from the Fronts 87 Experiment,
Geophys. Res. Lett., 20, 1371–1374, https://doi.org/10.1029/93GL01701, 1993. a
Lehmann, A., Myrberg, K., and Höflich, K.: A statistical approach to
coastal upwelling in the Baltic Sea based on the analysis of satellite data
for 1990–2009, Oceanologia, 54, 369–393, https://doi.org/10.5697/oc.54-3.369, 2012. a
Li, H., Claremar, B., Wu, L., Hallgren, C., Körnich, H., Ivanell, S., and
Sahlée, E.: A sensitivity study of the WRF model in offshore wind modeling
over the Baltic Sea, Geosci. Front., 12, 101229,
https://doi.org/10.1016/j.gsf.2021.101229, 2021. a
Mahrt, L.: Flux Sampling Errors for Aircraft and Towers, J. Atmos. Ocean. Technol., 15, 416–429,
https://doi.org/10.1175/1520-0426(1998)015<0416:FSEFAA>2.0.CO;2, 1998. a, b, c
Mahrt, L., Nilsson, E., Rutgersson, A., and Pettersson, H.: Vertical divergence
of the atmospheric momentum flux near the sea surface at a coastal site,
J. Phys. Oceanogr., 51, 3529–3537,
https://doi.org/10.1175/JPO-D-21-0081.1, 2021. a
Mann, J., Peña, A., Bingöl, F., Wagner, R., and Courtney, M. S.: Lidar
scanning of momentum flux in and above the atmospheric surface layer, J. Atmos. Ocean. Technol., 27, 959–976,
https://doi.org/10.1175/2010JTECHA1389.1, 2010. a
Mauder, M. and Foken, T.: Documentation and instruction manual of the Eddy
Covariance software package TK2,
https://epub.uni-bayreuth.de/884/1/ARBERG026.pdf (last access: 3 June 2022), 2004. a
McGill, R., Tukey, J. W., and Larsen, W. A.: Variations of Box Plots, Am. Stat., 32, 12–16, https://doi.org/10.1080/00031305.1978.10479236,
1978. a
Møller, M., Domagalski, P., and Sætran, L. R.: Comparing abnormalities in onshore and offshore vertical wind profiles, Wind Energ. Sci., 5, 391–411, https://doi.org/10.5194/wes-5-391-2020, 2020. a, b
Nilsson, E., Bergström, H., Rutgersson, A., Podgrajsek, E., Wallin, M. B.,
Bergström, G., Dellwik, E., Landwehr, S., and Ward, B.: Evaluating Humidity
and Sea Salt Disturbances on CO2 Flux Measurements,
J. Atmos. Ocean. Technol., 35, 859–875, https://doi.org/10.1175/JTECH-D-17-0072.1,
2018. a, b
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
Obukhov, A. M.: Turbulentnost'v temperaturnoj-neodnorodnoj atmosfere
[Turbulence in an Atmosphere with a Non-uniform Temperature], Trudy Inst.
Theor. Geofiz. AN SSSR, 1, 95–115, 1946. a
Pasquill, F.: The estimation of the dispersion of windborne material,
Meteorol. Mag., 90, 33, 1961. a
Platis, A., Hundhausen, M., Lampert, A., Emeis, S., and Bange, J.: The Role of
Atmospheric Stability and Turbulence in Offshore Wind-Farm Wakes in the
German Bight, Bound.-Lay. Meteorol., 182, 441–469,
https://doi.org/10.1007/s10546-021-00668-4, 2022. a
Prabha, T. V., Leclerc, M. Y., Karipot, A., Hollinger, D. Y., and
Mursch-Radlgruber, E.: Influence of nocturnal low-level jets on
eddy-covariance fluxes over a tall forest canopy, Bound.-Lay. Meteorol.,
126, 219–236, https://doi.org/10.1007/s10546-007-9232-3, 2008. a, b
Ranjha, R., Svensson, G., Tjernström, M., and Semedo, A.: Global distribution
and seasonal variability of coastal low-level jets derived from ERA-Interim
reanalysis, Tellus A, 65, 20412,
https://doi.org/10.3402/tellusa.v65i0.20412, 2013. a
Roy, S., Sentchev, A., Schmitt, F. G., Augustin, P., and Fourmentin, M.: Impact of the Nocturnal Low-Level Jet and Orographic Waves on Turbulent Motions and Energy Fluxes in the Lower Atmospheric Boundary Layer, Bound.-Lay. Meteorol., 180, 527–542, https://doi.org/10.1007/s10546-021-00629-x, 2021. a, b
Rutgersson, A., Pettersson, H., Nilsson, E., Bergström, H., Wallin, M. B.,
Nilsson, E. D., Sahlée, E., Wu, L., and Mårtensson, E. M.: Using land-based
stations for air–sea interaction studies, Tellus A, 72, 1–23, https://doi.org/10.1080/16000870.2019.1697601, 2020. a, b, c, d
Sahlée, E., Smedman, A.-S., Rutgersson, A., and Högström, U.: Spectra of CO2 and water vapour in the marine atmospheric surface layer,
Bound.-Lay. Meteorol., 126, 279–295, https://doi.org/10.1007/s10546-007-9230-5, 2008. a
Sanz Rodrigo, J., Cantero, E., García, B., Borbón, F., Irigoyen, U., Lozano,
S., Fernande, P., and Chávez, R. A.: Atmospheric stability assessment for
the characterization of offshore wind conditions, J. Phys. Conf. Ser., 625, 012044, https://doi.org/10.1088/1742-6596/625/1/012044, 2015. a
Segalini, A. and Arnqvist, J.: A spectral model for stably stratified
turbulence, J. Fluid Mech., 781, 330–352,
https://doi.org/10.1017/jfm.2015.502, 2015. a, b
Semedo, A., Saetra, Ø., Rutgersson, A., Kahma, K. K., and Pettersson, H.:
Wave-induced wind in the marine boundary layer, J. Atmos. Sci., 66, 2256–2271, https://doi.org/10.1175/2009JAS3018.1, 2009. a, b, c, d
Semedo, A., Vettor, R., Breivik, Ø., Sterl, A., Reistad, M., Soares, C. G.,
and Lima, D.: The wind sea and swell waves climate in the Nordic seas, Ocean
Dynam., 65, 223–240, https://doi.org/10.1007/s10236-014-0788-4, 2015. a
Sezer-Uzol, N. and Uzol, O.: Effect of steady and transient wind shear on the
wake structure and performance of a horizontal axis wind turbine rotor, Wind
Energy, 16, 1–17, https://doi.org/10.1002/we.514, 2013. a
Smedman, A.-S., Tjernström, M., and Högström, U.: Analysis of the turbulence
structure of a marine low-level jet, Bound.-Lay. Meteorol., 66,
105–126, https://doi.org/10.1007/BF00705462, 1993. 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, https://doi.org/10.1007/BF00709352,
1995. a, b
Smedman, A.-S., Högström, U., and Bergström, H.: Low level jets – a
decisive factor for off-shore wind energy siting in the Baltic Sea, Wind
Engineering, 137–147, 1996. a
Smedman, A.-S., Högström, U., and Bergström, H.: The turbulence regime of a
very stable marine airflow with quasi-frictional decoupling, J. Geophys. Res.-Oceans, 102, 21049–21059, https://doi.org/10.1029/97JC01070,
1997. a, b
Smedman, A.-S., Högström, U., Sahlée, E., Drennan, W. M., Kahma, K. K.,
Pettersson, H., and Zhang, F.: Observational Study of Marine Atmospheric
Boundary Layer Characteristics during Swell, J. Atmos. Sci., 66, 2747–2763, https://doi.org/10.1175/2009JAS2952.1, 2009. a, b, c
Sorbjan, Z. and Grachev, A. A.: An evaluation of the flux–gradient
relationship in the stable boundary layer, Bound.-Lay. Meteorol., 135,
385–405, https://doi.org/10.1007/s10546-010-9482-3, 2010. a
Sproson, D. and Sahlée, E.: Modelling the impact of Baltic Sea upwelling on
the atmospheric boundary layer, Tellus A, 66, 24041, https://doi.org/10.3402/tellusa.v66.24041, 2014. a
St. Pé, A., Sperling, M., Brodie, J. F., and Delgado, R.: Classifying
rotor-layer wind to reduce offshore available power uncertainty, Wind Energy,
21, 461–473, https://doi.org/10.1002/we.2159, 2018. a
Starkenburg, D., Metzger, S., Fochesatto, G. J., Alfieri, J. G., Gens, R.,
Prakash, A., and Cristóbal, J.: Assessment of Despiking Methods for
Turbulence Data in Micrometeorology,
J. Atmos. Ocean. Technol., 33, 2001–2013, https://doi.org/10.1175/JTECH-D-15-0154.1, 2016. a
Stull, R. B. (Ed.): An introduction to boundary layer meteorology, vol. 13, Springer Science & Business Media, Dordrecht, Boston, London,
https://doi.org/10.1007/978-94-009-3027-8, 1988. a, b
Sullivan, P. P., Edson, J. B., Hristov, T., and McWilliams, J. C.: Large-eddy
simulations and observations of atmospheric marine boundary layers above
nonequilibrium surface waves, J. Atmos. Sci., 65,
1225–1245, https://doi.org/10.1175/2007JAS2427.1, 2008. a, b, c
Svensson, N., Arnqvist, J., Bergström, H., Rutgersson, A., and Sahlée, E.:
Measurements and Modelling of Offshore Wind Profiles in a Semi-Enclosed Sea,
Atmosphere, 10, 194, https://doi.org/10.3390/atmos10040194, 2019. a, b, c, d
Townsend, A. (Ed.): The structure of turbulent shear flow, Cambridge University Press, ISBN 0-521-20710, 1980. a
Tuononen, M., O’Connor, E. J., Sinclair, V. A., and Vakkari, V.: Low-level
jets over Utö, Finland, based on Doppler lidar observations, J. Appl. Meteorol. Climatol., 56, 2577–2594,
https://doi.org/10.1175/JAMC-D-16-0411.1, 2017. a
Van de Wiel, B. J. H., Moene, A. F., Steeneveld, G. J., Baas, P., Bosveld,
F. C., and Holtslag, A. A. M.: A conceptual view on inertial oscillations and
nocturnal low-level jets, J. Atmos. Sci., 67,
2679–2689, https://doi.org/10.1175/2010JAS3289.1, 2010. a
Vihma, T. and Brümmer, B.: Observations and modelling of the on-ice and
off-ice air flow over the Northern Baltic Sea, Bound.-Lay. Meteorol.,
103, 1–27, https://doi.org/10.1023/a:1014566530774, 2002. a
Vollmer, L., Lee, J. C., Steinfeld, G., and Lundquist, J.: A wind turbine wake in changing atmospheric conditions: LES and lidar measurements, J. Phys. Conf. Ser., 854, 012050, https://doi.org/10.1088/1742-6596/854/1/012050, 2017. a
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
Wagner, R., Courtney, M., Gottschall, J., and Lindelöw-Marsden, P.: Accounting
for the speed shear in wind turbine power performance measurement, Wind
Energy, 14, 993–1004, https://doi.org/10.1002/we.509, 2011. a
Wind Europe: Significant developments on offshore wind in the Baltic Sea,
https://windeurope.org/newsroom/significant-developments-on-offshore-wind-in-the-baltic-sea/,
last access: 5 October 2021.
a
Wu, L., Rutgersson, A., Sahlée, E., and Guo Larsén, X.: Swell impact on
wind stress and atmospheric mixing in a regional coupled atmosphere-wave
model, J. Geophys. Res.-Oceans, 121, 4633–4648,
https://doi.org/10.1002/2015JC011576, 2016. a
Wu, L., Rutgersson, A., and Nilsson, E.: Atmospheric boundary layer turbulence
closure scheme for wind-following swell conditions, J. Atmos. Sci., 74, 2363–2382, https://doi.org/10.1175/JAS-D-16-0308.1, 2017. a
Wu, L., Shao, M., and Sahlée, E.: Impact of Air–Wave–Sea Coupling on the
Simulation of Offshore Wind and Wave Energy Potentials, Atmosphere, 11, 327,
https://doi.org/10.3390/atmos11040327, 2020. a, b, c
Yus-Díez, J., Udina, M., Soler, M. R., Lothon, M., Nilsson, E., Bech, J., and Sun, J.: Nocturnal boundary layer turbulence regimes analysis during the BLLAST campaign, Atmos. Chem. Phys., 19, 9495–9514, https://doi.org/10.5194/acp-19-9495-2019, 2019. a
Zhan, L., Letizia, S., and Valerio Iungo, G.: LiDAR measurements for an onshore
wind farm: Wake variability for different incoming wind speeds and
atmospheric stability regimes, Wind Energy, 23, 501–527,
https://doi.org/10.1002/we.2430, 2020. a
Zhang, S., Wu, L., Arnqvist, J., Hallgren, C., and Rutgersson, A.: Coastal
upwelling in the Baltic Sea from 2002 to 2020 using remote sensing data,
submitted, 2022. a
Short summary
Non-idealized wind profiles with negative shear in part of the profile (e.g., low-level jets) frequently occur in coastal environments and are important to take into consideration for offshore wind power. Using observations from a coastal site in the Baltic Sea, we analyze in which meteorological and sea state conditions these profiles occur and study how they alter the turbulence structure of the boundary layer compared to idealized profiles.
Non-idealized wind profiles with negative shear in part of the profile (e.g., low-level jets)...
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