Articles | Volume 6, issue 3
https://doi.org/10.5194/wes-6-645-2021
© 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-6-645-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 May 2021
Research article |
| 07 May 2021
| 07 May 2021
Evaluation of idealized large-eddy simulations performed with the Weather Research and Forecasting model using turbulence measurements from a 250 m meteorological mast
DTU Wind Energy, Technical University of Denmark, Roskilde, Denmark
Branko Kosović
National Center for Atmospheric Research, Boulder, USA
Jeffrey D. Mirocha
Lawrence Livermore National Laboratory, Livermore, USA
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Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-267, https://doi.org/10.5194/wes-2025-267, 2026
Revised manuscript under review for WES
Short summary
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We studied how winds and ocean waves affect each other during a North Sea storm. Using a multiscale approach that captures processes from kilometers down to meters, we linked wind and wave models and compared the results with real measurements. Our aim was to improve current simulation methods, and the findings show that this detailed approach provides more accurate storm predictions up to 100 m height.
Timothy W. Juliano, Florian Tornow, Ann M. Fridlind, Andrew S. Ackerman, Gregory S. Elsaesser, Bart Geerts, Christian P. Lackner, David Painemal, Israel Silber, Mikhail Ovchinnikov, Gunilla Svensson, Michael Tjernström, Peng Wu, Alejandro Baró Pérez, Peter Bogenschutz, Dmitry Chechin, Kamal Kant Chandrakar, Jan Chylik, Andrey Debolskiy, Rostislav Fadeev, Anu Gupta, Luisa Ickes, Michail Karalis, Martin Köhler, Branko Kosović, Peter Kuma, Weiwei Li, Evgeny Mortikov, Hugh Morrison, Roel A. J. Neggers, Anna Possner, Tomi Raatikainen, Sami Romakkaniemi, Niklas Schnierstein, Shin-ichiro Shima, Nikita Silin, Mikhail Tolstykh, Lulin Xue, Meng Zhang, and Xue Zheng
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Preprint archived
Short summary
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Models struggle to capture cloud and precipitation processes and their radiative effects in marine cold-air outbreaks. We use a quasi-Lagrangian framework to compare large-eddy simulation (LES) and single-column model (SCM) output with field and satellite observations. With fixed droplet and ice numbers, LES and SCM agree in liquid-only tests. In mixed-phase conditions, LES plausibly capture cloud thinning and breakup, while SCMs largely remain overcast and thereby miss cloud radiative effects.
Eric A. Hendricks, Timothy W. Juliano, Branko Kosović, Sue Haupt, Brian J. Gaudet, and Geng Xia
EGUsphere, https://doi.org/10.5194/egusphere-2025-4862, https://doi.org/10.5194/egusphere-2025-4862, 2026
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Patrick Hawbecker, William Lassman, Timothy W. Juliano, Branko Kosović, and Sue Ellen Haupt
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Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-246, https://doi.org/10.5194/wes-2025-246, 2025
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Etienne Cheynet, Jan Markus Diezel, Hilde Haakenstad, Øyvind Breivik, Alfredo Peña, and Joachim Reuder
Wind Energ. Sci., 10, 733–754, https://doi.org/10.5194/wes-10-733-2025, https://doi.org/10.5194/wes-10-733-2025, 2025
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Wind Energ. Sci., 8, 1893–1907, https://doi.org/10.5194/wes-8-1893-2023, https://doi.org/10.5194/wes-8-1893-2023, 2023
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Wind Energ. Sci., 8, 1795–1808, https://doi.org/10.5194/wes-8-1795-2023, https://doi.org/10.5194/wes-8-1795-2023, 2023
Short summary
Short summary
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Miguel Sanchez Gomez, Julie K. Lundquist, Jeffrey D. Mirocha, and Robert S. Arthur
Wind Energ. Sci., 8, 1049–1069, https://doi.org/10.5194/wes-8-1049-2023, https://doi.org/10.5194/wes-8-1049-2023, 2023
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The wind slows down as it approaches a wind plant; this phenomenon is called blockage. As a result, the turbines in the wind plant produce less power than initially anticipated. We investigate wind plant blockage for two atmospheric conditions. Blockage is larger for a wind plant compared to a stand-alone turbine. Also, blockage increases with atmospheric stability. Blockage is amplified by the vertical transport of horizontal momentum as the wind approaches the front-row turbines in the array.
Maarten Paul van der Laan, Oscar García-Santiago, Mark Kelly, Alexander Meyer Forsting, Camille Dubreuil-Boisclair, Knut Sponheim Seim, Marc Imberger, Alfredo Peña, Niels Nørmark Sørensen, and Pierre-Elouan Réthoré
Wind Energ. Sci., 8, 819–848, https://doi.org/10.5194/wes-8-819-2023, https://doi.org/10.5194/wes-8-819-2023, 2023
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Andrea N. Hahmann, Oscar García-Santiago, and Alfredo Peña
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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.
William J. Shaw, Larry K. Berg, Mithu Debnath, Georgios Deskos, Caroline Draxl, Virendra P. Ghate, Charlotte B. Hasager, Rao Kotamarthi, Jeffrey D. Mirocha, Paytsar Muradyan, William J. Pringle, David D. Turner, and James M. Wilczak
Wind Energ. Sci., 7, 2307–2334, https://doi.org/10.5194/wes-7-2307-2022, https://doi.org/10.5194/wes-7-2307-2022, 2022
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This paper provides a review of prominent scientific challenges to characterizing the offshore wind resource using as examples phenomena that occur in the rapidly developing wind energy areas off the United States. The paper also describes the current state of modeling and observations in the marine atmospheric boundary layer and provides specific recommendations for filling key current knowledge gaps.
Alessandro Sebastiani, Alfredo Peña, Niels Troldborg, and Alexander Meyer Forsting
Wind Energ. Sci., 7, 875–886, https://doi.org/10.5194/wes-7-875-2022, https://doi.org/10.5194/wes-7-875-2022, 2022
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The power performance of a wind turbine is often tested with the turbine standing in a row of several wind turbines, as it is assumed that the performance is not affected by the neighbouring turbines. We test this assumption with both simulations and measurements, and we show that the power performance can be either enhanced or lowered by the neighbouring wind turbines. Consequently, we also show how power performance testing might be biased when performed on a row of several wind turbines.
Wei Fu, Alfredo Peña, and Jakob Mann
Wind Energ. Sci., 7, 831–848, https://doi.org/10.5194/wes-7-831-2022, https://doi.org/10.5194/wes-7-831-2022, 2022
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Measuring the variability of the wind is essential to operate the wind turbines safely. Lidars of different configurations have been placed on the turbines’ nacelle to measure the inflow remotely. This work found that the multiple-beam lidar is the only one out of the three employed nacelle lidars that can give detailed information about the inflow variability. The other two commercial lidars, which have two and four beams, respectively, measure only the fluctuation in the along-wind direction.
Adam S. Wise, James M. T. Neher, Robert S. Arthur, Jeffrey D. Mirocha, Julie K. Lundquist, and Fotini K. Chow
Wind Energ. Sci., 7, 367–386, https://doi.org/10.5194/wes-7-367-2022, https://doi.org/10.5194/wes-7-367-2022, 2022
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Wind turbine wake behavior in hilly terrain depends on various atmospheric conditions. We modeled a wind turbine located on top of a ridge in Portugal during typical nighttime and daytime atmospheric conditions and validated these model results with observational data. During nighttime conditions, the wake deflected downwards following the terrain. During daytime conditions, the wake deflected upwards. These results can provide insight into wind turbine siting and operation in hilly regions.
Davide Conti, Nikolay Dimitrov, Alfredo Peña, and Thomas Herges
Wind Energ. Sci., 6, 1117–1142, https://doi.org/10.5194/wes-6-1117-2021, https://doi.org/10.5194/wes-6-1117-2021, 2021
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We carry out a probabilistic calibration of the Dynamic Wake Meandering (DWM) model using high-spatial- and high-temporal-resolution nacelle-based lidar measurements of the wake flow field. The experimental data were collected from the Scaled Wind Farm Technology (SWiFT) facility in Texas. The analysis includes the velocity deficit, wake-added turbulence, and wake meandering features under various inflow wind and atmospheric-stability conditions.
Miguel Sanchez Gomez, Julie K. Lundquist, Jeffrey D. Mirocha, Robert S. Arthur, and Domingo Muñoz-Esparza
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2021-57, https://doi.org/10.5194/wes-2021-57, 2021
Revised manuscript not accepted
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Winds decelerate upstream of a wind plant as turbines obstruct and extract energy from the flow. This effect is known as wind plant blockage. We assess how atmospheric stability modifies the upstream wind plant blockage. We find stronger stability amplifies this effect. We also explore different approaches to quantifying blockage from field-like observations. We find different methodologies may induce errors of the same order of magnitude as the blockage-induced velocity deficits.
Davide Conti, Vasilis Pettas, Nikolay Dimitrov, and Alfredo Peña
Wind Energ. Sci., 6, 841–866, https://doi.org/10.5194/wes-6-841-2021, https://doi.org/10.5194/wes-6-841-2021, 2021
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We define two lidar-based procedures for improving the accuracy of wind turbine load assessment under wake conditions. The first approach incorporates lidar observations directly into turbulence fields serving as inputs for aeroelastic simulations; the second approach imposes lidar-fitted wake deficit time series on the turbulence fields. The uncertainty in the lidar-based power and load predictions is quantified for a variety of scanning configurations and atmosphere turbulence conditions.
Cited articles
Arthur, R. S., Lundquist, K. A., Mirocha, J. D., and Chow, F. K.: Topographic
effects on radiation in the WRF model with the immersed boundary method:
implementation, validation and application to complex terrain, Mon. Weather Rev., 146, 3277–3292, 2018. a
Arthur, R. S., Mirocha, J. D., Marjanovic, N., Hirth, B. D., Schroeder, J. L., Wharthon, S., and Chow, F. K.: Multi-scale simulation of wind farm
performance during a frontal passage, Atmosphere, 11, 245, https://doi.org/10.3390/atmos11030245, 2020. a
Arthur, R. S., Lundquist, K. A., and Olson, J. B.: Improved prediction of
cold-air pools in the Weather Research and Forecasting model using a
truly horizontal diffusion scheme for potential temperature, Mon. Weather Rev., 149, 155–171, 2021. a
Basu, S., Holtslag, A. A. M., Van De Wiel, B. J. H., Moene, A. F., and
Steeneveld, G.-J.: An inconvenient “truth” about using sensible heat flux
as a surface boundary condition in models under stably stratified regimes,
Acta Geophys., 56, 88–99, 2008. a
Deardorff, J. W.: Stratocumulus-capped mixed layers derived from a
three-dimensional model, Bound.-Lay. Meteorol., 18, 495–527, 1980. a
Gryning, S.-E., Batchvarova, E., Brümmer, B., Jørgensen, H., and Larsen,
S.: On the extension of the wind profile over homogeneous terrain beyond the
surface layer, Bound.-Lay. Meteorol., 124, 251–268, 2007. a
Hahmann, A. N., Vincent, C., Peña, A., Lange, J., and Hasager, C. B.: Wind climate estimation using WRF model output: method and model sensitivities over the sea, Int. J. Climatol., 35, 3422–3439, 2015. a
Honnert, R., Masson, V., and Couvreux, F.: A diagnostic for evaluating the
representation of turbulence in atmospheric models at the kilometric scale,
J. Atmos. Sci., 68, 3112–3131, 2011. a
Kolmogorov, A. N.: The local structure of turbulence in incompressible viscous fluid for very large Reynolds number, Dokl. Akad. Nauk SSSR+, 30, 301–304, 1941. a
Kosović, B., Haupt, S., Adriaansen, D., Alessandrini, S., Wiener, G.,
Delle Monache, L., Liu, Y., Linden, S., Jensen, T., Cheng, W., Politovich,
M., and Prestopnik, P. A.: Comprehensive wind power forecasting system
integrating artificial intelligence and numerical weather prediction,
Energies, 13, 1372, https://doi.org/10.3390/en13061372, 2020a. a
Kosović, B., Munoz, P. J., Juliano, T. W., Martilli, A., Eghdami, M.,
Barros, A. P., and Haupt, S. E.: Three-dimensional planetary boundary layer
parameterization for high-resolution mesoscale simulations,
J. Phys. Conf. Ser., 1452, 012080, https://doi.org/10.1088/1742-6596/1452/1/012080, 2020b. a
Lundquist, K. A., Chow, F. K., and Lundquist, J. K.: An immersed boundary
method enabling large-eddy simulations of flow over complex terrain in the
WRF model, Mon. Weather Rev., 140, 3936–3955, 2012. a
Mann, J.: The spatial structure of neutral atmospheric surface-layer
turbulence, J. Fluid Mech., 273, 141–168, 1994. a
Mirocha, J. D., Churchfield, M. J., Muñoz-Esparza, D., Rai, R. K., Feng, Y., Kosović, B., Haupt, S. E., Brown, B., Ennis, B. L., Draxl, C., Sanz Rodrigo, J., Shaw, W. J., Berg, L. K., Moriarty, P. J., Linn, R. R., Kotamarthi, V. R., Balakrishnan, R., Cline, J. W., Robinson, M. C., and Ananthan, S.: Large-eddy simulation sensitivities to variations of configuration and forcing parameters in canonical boundary-layer flows for wind energy applications, Wind Energ. Sci., 3, 589–613, https://doi.org/10.5194/wes-3-589-2018, 2018. a, b, c, d, e, f, g
Moeng, C.-H., Dudhia, J., Klemp, J., and Sullivan, P.: Examining two-way grid
nesting for large eddy simulation of the PBL using the WRF model, Mon. Weather Rev., 135, 2295–2311, 2007. a
Monin, A. S. and Obukhov, A. M.: Osnovnye zakonomernosti turbulentnogo
peremeshivanija v prizemnom sloe atmosfery (Basic laws of turbulent mixing
in the atmosphere near the ground), Trudy Geofiz. Inst. AN SSSR, 24, 163–187, 1954. a
Muñoz-Esparza, D., Kosović, B., van Beeck, J., and Mirocha, J.: A
stochastic perturbation method to generate inflow turbulence in large-eddy
simulation models: Application to neutrally stratified atmospheric boundary
layers, Phys. Fluids, 27, 035102, https://doi.org/10.1063/1.4913572, 2015. a
Muñoz-Esparza, D., Lundquist, J. K., Sauer, J. A., Kosović, B., and
Linn, R. R.: Coupled mesoscale-LES modeling of a diurnal cycle during the
CWEX-13 field campaign: From weather to boundary-layer eddies,
J. Adv. Model. Earth Sy., 9, 1572–1594, 2017. a
Obukhov, A. M.: Turbulentnost v temperaturnoj – neodnorodnoj atmosfere
(Turbulence in an atmosphere with an non-uniform temperature), Trudy
Geofiz. Inst. AN SSSR, 1, 95–115, 1946. a
Pedersen, J., Gryning, S.-E., and Kelly, M.: On the structure and adjustment of inversion-capped neutral atmospheric boundary-layer flows: large-eddy
simulation study, Bound.-Lay. Meteorol., 153, 43–62, 2014. a
Pedersen, J. G., Kelly, M., Gryning, S.-E., and Brümmer, B.: The effect of
unsteady and baroclinic forcing on predicted wind profiles in Large Eddy
Simulations: Two case studies of the daytime atmospheric boundary layer,
Meteorol. Z., 22, 661–674, 2013. a
Peña, A.: Dataset and input files for “Evaluation of idealized large-eddy simulations performed with the Weather Research and Forecasting model using turbulence measurements from a 250 m meteorological mast”, Technical University of Denmark, available at: https://doi.org/10.11583/DTU.13395953, 2020 (data available at: https://figshare.com/s/c02c0954051d67f17992, last access 5 May 2021). a
Peña, A. and Hahmann, A. N.: Atmospheric stability and turbulent fluxes at Horns Rev – an intercomparison of sonic, bulk and WRF model data, Wind Energy, 15, 717–730, 2012. a
Peña, A., Gryning, S.-E., and Mann, J.: On the length-scale of the wind
profile, Q. J. Roy. Meteor. Soc., 136, 2119–2131, 2010b. a
Rai, R. K., Berg, L. K., Kosović, B., Haupt, S. E., Mirocha, J. D., Ennis, B. L., and Draxl, C.: Evaluation of the impact of horizontal grid spacing in terra incognita on coupled mesoscale-microscale simulations using the WRF framework, Mon. Weather Rev., 147, 1007–1027, 2019. a
Rossby, C.-G. and Montgomery, R. B.: The layers of frictional influence in wind and ocean currents, Papers in Physical Oceanography and Meteorology, 3, 101, https://doi.org/10.1575/1912/1157, 1935. a
Sanz Rodrigo, J., Churchfield, M., and Kosovic, B.: A methodology for the design and testing of atmospheric boundary layer models for wind energy applications, Wind Energ. Sci., 2, 35–54, https://doi.org/10.5194/wes-2-35-2017, 2017. a
Simon, J. S., Zhou, B., Mirocha, J. D., and Chow, F. K.: Explicit filtering and reconstruction to reduce grid dependence in convective boundary layer
simulations using WRF-LES, Mon. Weather Rev., 147, 1805–1821, 2019. a
Skamarock, W. C.: Evaluating mesoscale NWP models using kinetic energy
spectra, Mon. Weather Rev., 132, 3019–3032, 2004. a
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Duda,
M. G., Huang, X.-Y., Wang, W., and Powers, J. G.: A description of the
advanced research WRF, version 3, Tech. Rep., Mesoscale and
Microscale Meteorology Division, National Center for Atmospheric Research,
Boulder, Colorado, USA, NCAR/TN-475+STR, 2008. a
Talbot, C., Bou-Zeid, E., and Smith, J.: Nested mesoscale large-eddy
simulations with WRF: performance in real test cases, J. Hydrometeorol., 13, 1421–1441, 2012. a
wrf-model:
WRF, GitHub, available at: https://github.com/wrf-model/WRF, last access: 5 May 2021. a
Wyngaard, J. C.: Toward numerical modeling in the “terra incognita”, J.
Atmos. Sci., 61, 1816–1826, 2004. a
Short summary
We investigate the ability of a community-open weather model to simulate the turbulent atmosphere by comparison with measurements from a 250 m mast at a flat site in Denmark. We found that within three main atmospheric stability regimes, idealized simulations reproduce closely the characteristics of the observations with regards to the mean wind, direction, turbulent fluxes, and turbulence spectra. Our work provides foundation for the use of the weather model in multiscale real-time simulations.
We investigate the ability of a community-open weather model to simulate the turbulent...
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