Articles | Volume 9, issue 5
https://doi.org/10.5194/wes-9-1153-2024
© Author(s) 2024. 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-9-1153-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 May 2024
Research article |
| 13 May 2024
| 13 May 2024
Tropical cyclone low-level wind speed, shear, and veer: sensitivity to the boundary layer parametrization in the Weather Research and Forecasting model
Department of Wind and Energy Systems, Danish Technical University, Risø Lab/Campus, Frederiksborgvej 399, 4000 Roskilde, Denmark
Sino-Danish Center for Education and Research (SDC), 100093, Beijing, China
Xiaoli Guo Larsén
Department of Wind and Energy Systems, Danish Technical University, Risø Lab/Campus, Frederiksborgvej 399, 4000 Roskilde, Denmark
David Robert Verelst
Department of Wind and Energy Systems, Danish Technical University, Risø Lab/Campus, Frederiksborgvej 399, 4000 Roskilde, Denmark
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Cited articles
Charnock, H.: Wind stress on a water surface, Q. J. Roy. Meteorol. Soc., 81, 639–640, https://doi.org/10.1002/qj.49708135027, 1955. a
Chen, X.: How Do Planetary Boundary Layer Schemes Perform in Hurricane Conditions: A Comparison With Large-Eddy Simulations, J. Adv. Model. Earth Syste., 14, e2022MS003088, https://doi.org/10.1029/2022MS003088, 2022. a, b
Chen, X. and Xu, J. Z.: Structural failure analysis of wind turbines impacted by super typhoon Usagi, Eng. Fail. Anal., 60, 391–404, https://doi.org/10.1016/j.engfailanal.2015.11.028, 2016. a
Chen, X., Li, C., and Xu, J.: Failure investigation on a coastal wind farm damaged by super typhoon: A forensic engineering study, J. Wind Eng. Indust. Aerodynam., 147, 132–142, https://doi.org/10.1016/j.jweia.2015.10.007, 2015. a
Chu, J.-H., Sampson, R. C., Levine, S. A., and Fukada, E.: The Joint Typhoon Warning Center Tropical Cyclone Best-Tracks, 1945–2000, Tech. Rep. NRL/MR/7540-02-16, Naval Research Laboratory and Joint Typhoon Warning Center, https://www.metoc.navy.mil/jtwc/products/best-tracks/tc-bt-report.html (last access: 4 May 2024), 2002. a, b, c, d
Dimitrov, N. K., Natarajan, A., and Kelly, M. C.: 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
Donelan, M. A., Haus, B. K., Reul, N., Plant, W. J., Stiassnie, M., Graber, H. C., Brown, O. B., and Saltzman, E. S.: On the limiting aerodynamic roughness of the ocean in very strong winds, Geophys. Res. Lett., 31, L183061-5, https://doi.org/10.1029/2004GL019460, 2004. 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
Draxl, C., Hahmann, A. N., Pena Diaz, 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, https://doi.org/10.1002/we.1555, 2014. a
Dvorak, V.: Tropical cyclone intensity analysis using satellite data, Tech. Rep. NOAA technical report NESDIS, 11, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data and Information Service, USA, https://repository.library.noaa.gov/view/noaa/19322 (last access: 5 May 2024), 1984. a
Emanuel, K.: An air sea interaction theory for tropical cyclones. Part 1: Steady-state maintenance, J. Atmos. Sci., 43, 585–604, https://doi.org/10.1175/1520-0469(1986)043<0585:AASITF>2.0.CO;2, 1986. a
Fitch, A. C., Olson, J. B., Lundquist, J. K., Dudhia, J., Gupta, A. K., Michalakes, J., and Barstad, I.: Local and mesoscale impacts of wind farms as parameterized in a mesoscale NWP model, Mon. Weather Rev., 140, 3017–3038, https://doi.org/10.1175/MWR-D-11-00352.1, 2012. a
4C Offshore: Global Offshore Wind Farms, http://www.4coffshore.com (last access: 25 May 2023), 2023. a
Gage, K. S. and Nastrom, G. D.: Theoretical interpretation of atmospheric wavenumber spectra of wind and temperature observed by commercial aircraft during GASP, J. Atmos. Sci., 43, 729–40, https://doi.org/10.1175/1520-0469(1986)043<0729:TIOAWS>2.0.CO;2, 1986. a
Gopalakrishnan, S. G., Marks, F., Zhang, J. A., Zhang, X., Bao, J. W., and Tallapragada, V.: A study of the impacts of vertical diffusion on the structure and intensity of the tropical cyclones using the high-resolution HWRF system, J. Atmos. Sci., 70, 524–541, https://doi.org/10.1175/JAS-D-11-0340.1, 2013. a, b, c, d, e, f
Green, B. W. and Zhang, F.: Impacts of air-sea flux parameterizations on the intensity and structure of tropical cyclones, Mon. Weather Rev., 141, 2308–2324, https://doi.org/10.1175/MWR-D-12-00274.1, 2013. a, b
He, J. Y., Chan, P. W., Li, Q. S., Li, L., Zhang, L., and Yang, H. L.: Observations of wind and turbulence structures of Super Typhoons Hato and Mangkhut over land from a 356 m high meteorological tower, Atmos. Res., 265, 105910, https://doi.org/10.1016/j.atmosres.2021.105910, 2022. a
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on pressure levels from 1979 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.bd0915c6, 2018. a, b
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
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.-Atmos., 113, D13103, https://doi.org/10.1029/2008JD009944, 2008. a
Jackson, C. R., Ruff, T. W., Knaff, J. A., Mouche, A., and Sampson, C. R.: Chasing cyclones from space, EOS, https://doi.org/10.1029/2021EO159148, 2021. a
Japan Meteorological Agency, RSMC Tokyo-Typhoon Center: RSMC Best Track Data, https://www.jma.go.jp/jma/jma-eng/jma-center/rsmc-hp-pub-eg/trackarchives.html (last access: May 2024), 2024. a
Jiménez, P. A., Dudhia, J., González-Rouco, J. F., Navarro, J., Montávez, J. P., and García-Bustamante, E.: A revised scheme for the WRF surface layer formulation, Mon. Weather Rev., 140, 898–918, 2012. a
Kain, J. S.: The Kain–Fritsch Convective Parameterization: An Update, J. Appl. Meteorol., 43, 170–181, https://doi.org/10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2, 2004. a
Kapoor, A., Ouakka, S., Arwade, S. R., Lundquist, J. K., Lackner, M. A., Myers, A. T., Worsnop, R. P., and Bryan, G. H.: Hurricane eyewall winds and structural response of wind turbines, Wind Energ. Sci., 5, 89–104, https://doi.org/10.5194/wes-5-89-2020, 2020. a, b, c, d
Karagali, I., Larsén, X. G., Badger, M., Peña, A., and Hasager, C. B.: Spectral Properties of ENVISAT ASAR and QuikSCAT Surface Winds in the North Sea, Remote Sens., 5, 6096–6115, https://doi.org/10.3390/rs5116096, 2013. a
Kepert, J. D.: Choosing a boundary layer parameterization for tropical cyclone modeling, Mon. Weather Rev., 140, 1427–1445, https://doi.org/10.1175/MWR-D-11-00217.1, 2012. a
Krogsæter, O. and Reuder, J.: Validation of boundary layer parameterization schemes in the weather research and forecasting model under the aspect of offshore wind energy applications – part I: Average wind speed and wind shear, Wind Energy, 18, 769–782, https://doi.org/10.1002/we.1727, 2014. a, b, c
Larsén, X. G., Ott, S., Badger, J., Hahmann, A. N., and Mann, J.: Recipes for correcting the impact of effective mesoscale resolution on the estimation of extreme winds, J. Appl. Meteorol. Clim., 51, 521–533, https://doi.org/10.1175/JAMC-D-11-090.1, 2012. a, b
Larsén, X. G., Larsen, S. E., and Lundtang Petersen, E.: Full-Scale Spectrum of Boundary-Layer Winds, Bound.-Lay. Meteorol., 159, 349–371, https://doi.org/10.1007/s10546-016-0129-x, 2016. a
Li, J., Li, Z., Jiang, Y., and Tang, Y.: Typhoon Resistance Analysis of Offshore Wind Turbines: A Review, Atmosphere, 13, 451, https://doi.org/10.3390/atmos13030451, 2022. a, b
Li, X., Pu, Z., and Gao, Z.: Effects of roll vortices on the evolution of hurricane harvey during landfall, J. Atmos. Sci., 76, 1847–1867, https://doi.org/10.1175/JAS-D-20-0270.1, 2021. a, b
Müller, S.: Tropical cyclone low-level wind speed, shear, and veer: sensitivity to the boundary layer parameterization in WRF [Data set], Zenodo [data set], https://doi.org/10.5281/zenodo.10202995, 2023. a
Müller, S., Larsén, X. G., and Verelst, D.: Enhanced shear and veer in the Taiwan Strait during Typhoon passage, in: Torque Conference, 29–31 May 2024, Florence, Italy, ID 358, https://www.torque2024.eu/ (last access: 7 May 2024), 2024. a
Nakanishi, M. and Niino, H.: Development of an improved turbulence closure model for the atmospheric boundary layer, J. Meteorol. Soc. Jpn. Ser. II, 87, 895–912, https://doi.org/10.2151/jmsj.87.895, 2009. a, b
Nolan, D. S., Stern, D. P., and Zhang, J. A.: Evaluation of planetary boundary layer parameterizations in tropical cyclones by comparison of in situ observations and high-resolution simulations of Hurricane Isabel (2003). Part II: Inner-core boundary layer and eyewall structure, Mon. Weather Rev., 137, 3675–3698, https://doi.org/10.1175/2009MWR2786.1, 2009a. a
Nolan, D. S., Zhang, J. A., and Stern, D. P.: Evaluation of planetary boundary layer parameterizations in tropical cyclones by comparison of in situ observations and high-resolution simulations of Hurricane Isabel (2003). Part I: Initialization, maximum winds, and the outer-core boundary layer, Mon. Weather Rev., 137, 3651–3674, https://doi.org/10.1175/2009MWR2785.1, 2009b. a, b, c, d
Ott, S.: Extreme winds in the Western North Pacific, Tech. Rep. No. 1544, Technical University of Denmark, ISBN 8755035000, ISBN 9788755035003, 2006. a
Powell, M. D., Vickery, P. J., and Reinhold, T. A.: Reduced drag coefficient for high wind speeds in tropical cyclones, Nature, 422, 279–283, https://doi.org/10.1038/nature01481, 2003. a, b
Rai, D. and Pattnaik, S.: Sensitivity of Tropical Cyclone Intensity and Structure to Planetary Boundary Layer Parameterization, Asia-Pacif. J. Atmos. Sci., 54, 473–488, https://doi.org/10.1007/s13143-018-0053-8, 2018. a, b
Ren, H., Dudhia, J., Ke, S., and Li, H.: The basic wind characteristics of idealized hurricanes of different intensity levels, J. Wind Eng. Indust. Aerodynam., 225, 104980, https://doi.org/10.1016/j.jweia.2022.104980, 2022. a
Shenoy, M., Raju, P. V., and Prasad, J.: Optimization of physical schemes in WRF model on cyclone simulations over Bay of Bengal using one-way ANOVA and Tukey's test, Sci. Rep., 11, 24412, https://doi.org/10.1038/s41598-021-02723-z, 2021. a, b, c
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Liu, Z., Berner, J., Wang, W., Powers, J. P., Duda, M. G., Barker, D., and Huang, X.-Y.: A Description of the Advanced Research WRF Model Version 4.3, National Center for Atmospheric Research [code], https://doi.org/10.5065/1dfh-6p97, 2021. a
Sparks, N., Hon, K. K., Chan, P. W., Wang, S., Chan, J. C., Lee, T. C., and Toumi, R.: Aircraft observations of tropical cyclone boundary layer turbulence over the South China Sea, J. Atmos. Sci., 76, 3773–3783, https://doi.org/10.1175/JAS-D-19-0128.1, 2019. a
Technical University of Denmark: Sophia HPC Cluster, Research Computing at DTU, https://doi.org/10.57940/FAFC-6M81, 2019. a
Thompson, G., Field, P. R., Rasmussen, R. M., and Hall, W. D.: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: Implementation of a new snow parameterization, Mon. Weather Rev., 136, 5095–5115, https://doi.org/10.1175/2008MWR2387.1, 2008. a, b
Tse, K. T., Li, S. W., Chan, P. W., Mok, H. Y., and Weerasuriya, A. U.: Wind profile observations in tropical cyclone events using wind-profilers and doppler SODARs, J. Wind Eng. Indust. Aerodynam., 115, 93–103, https://doi.org/10.1016/j.jweia.2013.01.003, 2013. a, b, c, d
Volker, P. J. H., Badger, J., Hahmann, A. N., and Ott, S.: The Explicit Wake Parametrisation V1.0: a wind farm parametrisation in the mesoscale model WRF, Geosci. Model Dev., 8, 3715–3731, https://doi.org/10.5194/gmd-8-3715-2015, 2015. a
Wang, H., Ke, S. T., Wang, T. G., Kareem, A., Hu, L., and Ge, Y. J.: Multi-stage typhoon-induced wind effects on offshore wind turbines using a data-driven wind speed field model, Renew. Energy, 188, 765–777, https://doi.org/10.1016/j.renene.2022.02.072, 2022. a
Worsnop, R. P., Lundquist, J. K., Bryan, G. H., Damiani, R., and Musial, W.: Gusts and shear within hurricane eyewalls can exceed offshore wind turbine design standards, Geophys. Res. Let., 44, 6413–6420, https://doi.org/10.1002/2017GL073537, 2017. a, b, c
Xu, H., Wang, H., and Duan, Y.: An Investigation of the Impact of Different Turbulence Schemes on the Tropical Cyclone Boundary Layer at Turbulent Gray‐Zone Resolution, J. Geophys. Res.-Atmos., 126, e2021JD035327, https://doi.org/10.1029/2021JD035327, 2021. a
Yang, H., Wu, L., and Xie, T.: Comparisons of four methods for tropical cyclone center detection in a high-resolution simulation, J. Meteorol. Soc. Jpn., 98, 379–393, https://doi.org/10.2151/jmsj.2020-020, 2020. a
Ye, G., Zhang, X., and Yu, H.: Modifications to Three-Dimensional Turbulence Parameterization for Tropical Cyclone Simulation at Convection-Permitting Resolution, J. Adv. Model. Syst., 15, e2022MS003530, https://doi.org/10.1029/2022MS003530, 2023. a
Ye, L., Li, Y., Zhu, P., and Gao, Z.: The effects of boundary layer vertical turbulent diffusivity on the tropical cyclone intensity, Atmos. Res., 295, 106994, https://doi.org/10.1016/j.atmosres.2023.106994, 2023. a, b
Zhang, J. A., Kalina, E. A., Biswas, M. K., Rogers, R. F., Zhu, P., and Marks, F. D.: A review and evaluation of planetary boundary layer parameterizations in hurricane weather research and forecasting model using idealized simulations and observations, Atmosphere, 11, 1091, https://doi.org/10.3390/atmos11101091, 2020. a, b, c, d
Zhu, P., Menelaou, K., and Zhu, Z.: Impact of subgrid-scale vertical turbulent mixing on eyewall asymmetric structures and mesovortices of hurricanes, Q. J. Roy. Meteoro. Soc., 140, 416–438, https://doi.org/10.1002/qj.2147, 2014. a, b, c
Short summary
Tropical cyclone winds are challenging for wind turbines. We analyze a tropical cyclone before landfall in a mesoscale model. The simulated wind speeds and storm structure are sensitive to the boundary parametrization. However, independent of the boundary layer parametrization, the median change in wind speed and wind direction with height is small relative to wind turbine design standards. Strong spatial organization of wind shear and veer along the rainbands may increase wind turbine loads.
Tropical cyclone winds are challenging for wind turbines. We analyze a tropical cyclone before...
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