Articles | Volume 11, issue 3
https://doi.org/10.5194/wes-11-961-2026
© Author(s) 2026. 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-11-961-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 Mar 2026
Research article |
| 26 Mar 2026
| 26 Mar 2026
How well can the Mann model describe typhoon turbulence?
Department of Wind and Energy Systems, Danish Technical University, Risø Lab/Campus Frederiksborgvej 399, Roskilde 4000, 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, Roskilde 4000, Denmark
Fei Hu
Institute of Atmospheric Physics, Chinese Academy of Sciences, 1000029 Beijing, China
Related authors
Sara Müller, Xiaoli Guo Larsén, and David Robert Verelst
Wind Energ. Sci., 9, 1153–1171, https://doi.org/10.5194/wes-9-1153-2024, https://doi.org/10.5194/wes-9-1153-2024, 2024
Short summary
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.
Branko Kosović, Sukanta Basu, Jacob Berg, Larry K. Berg, Sue E. Haupt, Xiaoli G. Larsén, Joachim Peinke, Richard J. A. M. Stevens, Paul Veers, and Simon Watson
Wind Energ. Sci., 11, 509–555, https://doi.org/10.5194/wes-11-509-2026, https://doi.org/10.5194/wes-11-509-2026, 2026
Short summary
Short summary
Most human activity happens in the layer of the atmosphere which extends a few hundred meters to a couple of kilometers above the surface of the Earth. The flow in this layer is turbulent. Turbulence impacts wind power production and turbine lifespan. Optimizing wind turbine performance requires understanding how turbulence affects both wind turbine efficiency and reliability. This paper points to gaps in our knowledge that need to be addressed to effectively utilize wind resources.
Xiaoran Guo, Jianping Guo, Ning Li, Zhen Zhang, Tianmeng Chen, Yu Shi, Pengzhan Yao, Shuairu Jiang, Lei Zhao, and Fei Hu
Atmos. Chem. Phys., 26, 2391–2409, https://doi.org/10.5194/acp-26-2391-2026, https://doi.org/10.5194/acp-26-2391-2026, 2026
Short summary
Short summary
Wind gusts threaten safety and infrastructure but are hard to predict. To address this gap, we studied an extreme wind gust event in Beijing on 30 May 2024. We used seven radar wind profilers to track how this gust developed. It formed when cold northeasterly air clashed with warm southerly winds as the storm moved downhill. Evaporation of rain cooled the air, boosting downward air movement and wind strength. The turbulence transferring energy from small to large eddies intensify winds.
Georgios Deskos, Jiali Wang, Sanjay Arwade, Murray Fisher, Brian Hirth, Xiaoli Guo Larsén, Julie K. Lundquist, Andrew Myers, Weichiang Pang, William J. Pringle, Robert Rogers, Miguel Sanchez-Gomez, Chao Sun, Atsushi Yamaguchi, and Paul Veers
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-32, https://doi.org/10.5194/wes-2026-32, 2026
Preprint under review for WES
Short summary
Short summary
Wind energy is increasingly built in coastal and offshore areas exposed to powerful tropical storms. This paper explains why current wind turbine design approaches are often insufficient for these conditions and identifies what must change to improve resilience. By combining insights from weather modeling, engineering, and risk analysis, we highlight key gaps in data and standards, and show how addressing them can enable safer more reliable wind energy in storm-prone regions.
Sima Hamzeloo, Xiaoli Guo Larsén, Alfredo Peña, Jana Fischereit, and Oscar García-Santiago
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-267, https://doi.org/10.5194/wes-2025-267, 2026
Preprint under review for WES
Short summary
Short summary
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.
Keeta Chapman-Smith, Xiaoli Guo Larsén, and Mark Laier Brodersen
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-269, https://doi.org/10.5194/wes-2025-269, 2026
Preprint under review for WES
Short summary
Short summary
This study presents a method to estimate wind speeds which could occur in a 50-year period. The 50-year wind speed is calculated for three regions: Taiwan, Japan, and the east coast of the United States of America. The method performs well in Taiwan and Japan which can be attributed to the large dataset size located within a limited spatial area. The east coast of the United States performs less well due to the smaller dataset size and wider spatial region of which they cover.
Jana Fischereit, Bjarke T. E. Olsen, Marc Imberger, Henrik Vedel, Kristian H. Møller, Andrea N. Hahmann, and Xiaoli Guo Larsén
EGUsphere, https://doi.org/10.5194/egusphere-2025-5407, https://doi.org/10.5194/egusphere-2025-5407, 2025
Short summary
Short summary
We evaluated how operating wind farms influence the atmosphere in numerical weather prediction using two wind farm parameterizations in the HARMONIE-AROME model, applied by over 10 European weather services. Accurate yield forecasts require including both onshore and offshore turbines. Wind turbines slightly alter near-surface temperature (<1 K on average). We also present an open-access European wind turbine dataset combining multiple data sources.
Xiaoli Guo Larsén, Marc Imberger, and Rogier Floors
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-245, https://doi.org/10.5194/wes-2025-245, 2025
Revised manuscript under review for WES
Short summary
Short summary
This study delivers a method and datasets for a global offshore atlas for turbulence intensity from height 10 m to 200 m. The method innovatively includes both two-dimensional and three-dimensional turbulence, stability, wave age and height. Results show satisfactory agreement with measurements and data from the literature.
Nathalia Correa-Sánchez, Xiaoli Guo Larsén, Giorgia Fosser, Eleonora Dallan, Marco Borga, and Francesco Marra
Wind Energ. Sci., 10, 2551–2561, https://doi.org/10.5194/wes-10-2551-2025, https://doi.org/10.5194/wes-10-2551-2025, 2025
Short summary
Short summary
We examined the power spectra of wind speed in three convection-permitting models in central Europe and found that these models have a better representation of wind variability characteristics than standard wind datasets like the New European Wind Atlas, due to different simulation approaches, providing more reliable extreme wind predictions.
Nathalia Correa-Sánchez, Xiaoli Guo Larsén, Eleonora Dallan, Marco Borga, and Fracesco Marra
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-172, https://doi.org/10.5194/wes-2025-172, 2025
Revised manuscript under review for WES
Short summary
Short summary
This research presents the first use of SMEV for wind extremes, extending it to wind energy applications. We use a categories framework combining climate, roughness, and topography for CPM evaluation. We find that model formulation drives inter-model uncertainties, rather than surface conditions. Also, there is a higher model agreement in winter (synoptic) and lower in summer (convective). CPM uncertainty analysis improves the reliability of extreme winds for design parameters.
Sara Müller, Xiaoli Guo Larsén, and David Robert Verelst
Wind Energ. Sci., 9, 1153–1171, https://doi.org/10.5194/wes-9-1153-2024, https://doi.org/10.5194/wes-9-1153-2024, 2024
Short summary
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.
Jana Fischereit, Henrik Vedel, Xiaoli Guo Larsén, Natalie E. Theeuwes, Gregor Giebel, and Eigil Kaas
Geosci. Model Dev., 17, 2855–2875, https://doi.org/10.5194/gmd-17-2855-2024, https://doi.org/10.5194/gmd-17-2855-2024, 2024
Short summary
Short summary
Wind farms impact local wind and turbulence. To incorporate these effects in weather forecasting, the explicit wake parameterization (EWP) is added to the forecasting model HARMONIE–AROME. We evaluate EWP using flight data above and downstream of wind farms, comparing it with an alternative wind farm parameterization and another weather model. Results affirm the correct implementation of EWP, emphasizing the necessity of accounting for wind farm effects in accurate weather forecasting.
Jianping Guo, Jian Zhang, Jia Shao, Tianmeng Chen, Kaixu Bai, Yuping Sun, Ning Li, Jingyan Wu, Rui Li, Jian Li, Qiyun Guo, Jason B. Cohen, Panmao Zhai, Xiaofeng Xu, and Fei Hu
Earth Syst. Sci. Data, 16, 1–14, https://doi.org/10.5194/essd-16-1-2024, https://doi.org/10.5194/essd-16-1-2024, 2024
Short summary
Short summary
A global continental merged high-resolution (PBLH) dataset with good accuracy compared to radiosonde is generated via machine learning algorithms, covering the period from 2011 to 2021 with 3-hour and 0.25º resolution in space and time. The machine learning model takes parameters derived from the ERA5 reanalysis and GLDAS product as input, with PBLH biases between radiosonde and ERA5 as the learning targets. The merged PBLH is the sum of the predicted PBLH bias and the PBLH from ERA5.
Xingxia Kou, Zhen Peng, Meigen Zhang, Fei Hu, Xiao Han, Ziming Li, and Lili Lei
Atmos. Chem. Phys., 23, 6719–6741, https://doi.org/10.5194/acp-23-6719-2023, https://doi.org/10.5194/acp-23-6719-2023, 2023
Short summary
Short summary
A CMAQ EnSRF-based regional inversion system was extended to resolve satellite retrievals into biogenic source–sink changes. The size of the assimilated biosphere sink in China inferred from GOSAT was −0.47 Pg C yr−1. The biosphere flux at the provincial scale was re-estimated following the refined description in the regional inversion.
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
Short summary
Short summary
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.
Xiaoli Guo Larsén and Søren Ott
Wind Energ. Sci., 7, 2457–2468, https://doi.org/10.5194/wes-7-2457-2022, https://doi.org/10.5194/wes-7-2457-2022, 2022
Short summary
Short summary
A method is developed for calculating the extreme wind in tropical-cyclone-affected water areas. The method is based on the spectral correction method that fills in the missing wind variability to the modeled time series, guided by best track data. The paper provides a detailed recipe for applying the method and the 50-year winds of equivalent 10 min temporal resolution from 10 to 150 m in several tropical-cyclone-affected regions.
Jana Fischereit, Kurt Schaldemose Hansen, Xiaoli Guo Larsén, Maarten Paul van der Laan, Pierre-Elouan Réthoré, and Juan Pablo Murcia Leon
Wind Energ. Sci., 7, 1069–1091, https://doi.org/10.5194/wes-7-1069-2022, https://doi.org/10.5194/wes-7-1069-2022, 2022
Short summary
Short summary
Wind turbines extract kinetic energy from the flow to create electricity. This induces a wake of reduced wind speed downstream of a turbine and consequently downstream of a wind farm. Different types of numerical models have been developed to calculate this effect. In this study, we compared models of different complexity, together with measurements over two wind farms. We found that higher-fidelity models perform better and the considered rapid models cannot fully capture the wake effect.
Lei Liu, Yu Shi, and Fei Hu
Nonlin. Processes Geophys., 29, 123–131, https://doi.org/10.5194/npg-29-123-2022, https://doi.org/10.5194/npg-29-123-2022, 2022
Short summary
Short summary
We find a new kind of non-stationarity. This new kind of non-stationarity is caused by the intrinsic randomness. Results show that the new kind of non-stationarity is widespread in small-scale variations of CO2 turbulent fluxes. This finding reminds us that we need to handle the short-term averaged turbulent fluxes carefully, and we also need to re-screen the existing non-stationarity diagnosis methods because they could make a wrong diagnosis due to this new kind of non-stationarity.
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
Short summary
Short summary
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.
Marcus Reckermann, Anders Omstedt, Tarmo Soomere, Juris Aigars, Naveed Akhtar, Magdalena Bełdowska, Jacek Bełdowski, Tom Cronin, Michał Czub, Margit Eero, Kari Petri Hyytiäinen, Jukka-Pekka Jalkanen, Anders Kiessling, Erik Kjellström, Karol Kuliński, Xiaoli Guo Larsén, Michelle McCrackin, H. E. Markus Meier, Sonja Oberbeckmann, Kevin Parnell, Cristian Pons-Seres de Brauwer, Anneli Poska, Jarkko Saarinen, Beata Szymczycha, Emma Undeman, Anders Wörman, and Eduardo Zorita
Earth Syst. Dynam., 13, 1–80, https://doi.org/10.5194/esd-13-1-2022, https://doi.org/10.5194/esd-13-1-2022, 2022
Short summary
Short summary
As part of the Baltic Earth Assessment Reports (BEAR), we present an inventory and discussion of different human-induced factors and processes affecting the environment of the Baltic Sea region and their interrelations. Some are naturally occurring and modified by human activities, others are completely human-induced, and they are all interrelated to different degrees. The findings from this study can largely be transferred to other comparable marginal and coastal seas in the world.
Marc Imberger, Xiaoli Guo Larsén, and Neil Davis
Adv. Geosci., 56, 77–87, https://doi.org/10.5194/adgeo-56-77-2021, https://doi.org/10.5194/adgeo-56-77-2021, 2021
Short summary
Short summary
Events like mid-latitude storms with their high winds have an impact on wind energy production and forecasting of such events is crucial. This study investigates the capabilities of a global weather prediction model MPAS and looks at how key parameters like storm intensity, arrival time and duration are represented compared to measurements and traditional methods. It is found that storm intensity is represented well while model drifts negatively influence estimation of arrival time and duration.
Xiaoli G. Larsén and Jana Fischereit
Geosci. Model Dev., 14, 3141–3158, https://doi.org/10.5194/gmd-14-3141-2021, https://doi.org/10.5194/gmd-14-3141-2021, 2021
Short summary
Short summary
For the first time, turbulent kinetic energy (TKE) calculated from the explicit wake parameterization (EWP) in WRF is examined using high-frequency measurements over a wind farm and compared with that calculated using the Fitch et al. (2012) scheme. We examined the effect of farm-induced TKE advection in connection with the Fitch scheme. Through a case study with a low-level jet (LLJ), we analyzed the key features of LLJs and raised the issue of interaction between wind farms and LLJs.
Cited articles
Barthlott, C. and Fiedler, F.: Turbulence structure in the wake region of a meteorological tower, Bound.-Layer Meteorol., 108, 175–190, https://doi.org/10.1023/A:1023012820710, 2003. a, b
Billesbach, D., Chan, S., Cook, D., Papale, D., Bracho-Garrillo, R., Verfallie, J., Vargas, R., and Biraud, S.: Effects of the Gill-Solent WindMaster-Pro “w-boost” firmware bug on eddy covariance fluxes and some simple recovery strategies, Agric. For. Meteorol., 265, 145–151, https://doi.org/10.1016/j.agrformet.2018.11.010, 2018. a
Cao, S., Tamura, Y., Kikuchi, N., Saito, M., Nakayama, I., and Matsuzaki, Y.: Wind characteristics of a strong typhoon, J. Wind Eng. Ind. Aerodyn., 97, 11–21, https://doi.org/10.1016/j.jweia.2008.10.002, 2009. 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
Cheynet, E.: Influence of the measurement height on the vertical coherence of natural wind, in: Proceedings of the XV Conference of the Italian Association for Wind Engineering (IN-VENTO), Naples, Italy, 2018, 207–221, https://doi.org/10.1007/978-3-030-12815-9_17, 2019. a
Cheynet, E., Jakobsen, Jasna, B., and Reuder, J.: Velocity spectra and coherence estimates in the marine atmospheric boundary layer, Bound.-Layer Meteorol., 169, 429–460, https://doi.org/10.1007/s10546-018-0382-2, 2018. a, b, c
Davenport, A. G.: The spectrum of horizontal gustiness near the ground in high winds, Q. J. R. Meteorol. Soc., 87, 194–211, https://doi.org/10.1002/qj.49708737208, 1961. a
Dimitrov, N. K., Natarajan, A., and Mann, J.: Effects of normal and extreme turbulence spectral parameters on wind turbine loads, Renew. Energ., 101, 1180–1193, https://doi.org/10.1016/j.renene.2016.10.001, 2017. a, b
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
Fang, Q., Chu, K., Zhou, B., Chen, X., Peng, Z., Zhang, C., Luo, M., and Zhao, C.: Observed turbulent dissipation rate in a landfalling tropical cyclone boundary layer, J. Atmos. Sci., 80, 1739–1754, https://doi.org/10.1175/JAS-D-22-0265.1, 2023. a
Foster, R. C.: Why rolls are prevalent in the hurricane boundary layer, J. Atmos. Sci., 62, 2647–2661, https://doi.org/10.1175/JAS3475.1, 2005. a
Gahtan, J., Knapp, K. R., Schreck, C. J. I., Diamond, H. J., Kossin, J. P., and Kruk, M. C.: International Best Track Archive for Climate Stewardship (IBTrACS) project, Version 4.01, National Centers for Environmental Information, NESDIS, NOAA, U.S. Department of Commerce [data set], https://doi.org/10.25921/82ty-9e16, 2024. a, b, c
Gao, K. and Ginis, I.: On the generation of roll vortices due to the inflection point instability of the hurricane boundary layer flow, J. Atmos. Sci., 71, 4292–4307, https://doi.org/10.1175/JAS-D-13-0362.1, 2014. a
Gao, Z., Liu, H., Li, D., Yang, B., Walden, V., Li, L., and Bogoev, I.: Uncertainties in temperature statistics and fluxes determined by sonic anemometers due to wind-induced vibrations of mounting arms, Atmos. Meas. Tech., 17, 4109–4120, https://doi.org/10.5194/amt-17-4109-2024, 2024. a, b
Geernaert, G.: Measurements of the angle between the wind vector and wind stress vector in the surface-layer over the north-sea, J. Geophys. Res.-Oceans, 93, 8215–8220, https://doi.org/10.1029/JC093iC07p08215, 1988. a, b
Han, T., McCann, G., Mücke, T. A., and Freudenreich, K.: How can a wind turbine survive in tropical cyclone?, Renew. Energ., 70, 3–10, https://doi.org/10.1016/j.renene.2014.02.014, 2014. a
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, b
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
Huang, L., Li, X., Liu, B., Zhang, J. A., Shen, D., Zhang, Z., and Yu, W.: Tropical cyclone boundary layer rolls in synthetic aperture radar imagery, J. Geophys. Res.-Oceans, 123, 2981–2996, https://doi.org/10.1029/2018JC013755, 2018. a, b, c
Huang, P., Xie, W., and Gu, M.: A comparative study of the wind characteristics of three typhoons based on stationary and nonstationary models, Nat. Hazards, 101, 785–815, https://doi.org/10.1007/s11069-020-03894-0, 2020. a
Huffman, G., Stocker, E., Bolvin, D., Nelkin, E., and Jackson, T.: GPM IMERG Final precipitation L3 Half Hourly 0.1 degree x 0.1 degree V07, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/GPM/IMERG/3B-HH/07, 2023. 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.-Atmos., 113, https://doi.org/10.1029/2008JD009944, 2008. a
IEC: IEC 61400-1 Ed4: Wind turbines – Part 1: Design requirements, standard, International Electrotechnical Commission, Geneva, Switzerland, https://webstore.iec.ch/publication/29360#additionalinfo (last access: 17 December 2024), 2019. a
Imberger, M., Larsén, X. G., and Davis, N.: Global atlas for siting parameters ocean and coasts – ensemble calculation of 50-year return winds, DTU Wind and Energy Systems, https://doi.org/10.11581/DTU.00000321, 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, https://doi.org/10.1175/MWR-D-11-00056.1, 2012. a
Kaimal, J. C., Wyngaard, J. C., Izumi, Y., and Cote, O. R.: Spectral characteristics of surface-layer turbulence, Q. J. R. Meteorol. Soc., 98, 563–589, https://doi.org/10.1002/qj.49709841707, 1972. a, b, c
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
Knapp, K. R., Kruk, M. C., Levinson, D. H., Diamond, H. J., and Neumann, C. J.: The International Best Track Archive for Climate Stewardship (IBTrACS), Bull. Am. Meteorol. Soc., 91, 363–376, https://doi.org/10.1175/2009BAMS2755.1, 2010. a, b
Kosović, B., Basu, S., Berg, J., Berg, L. K., Haupt, S. E., Larsén, X. G., Peinke, J., Stevens, R. J. A. M., Veers, P., and Watson, S.: Impact of atmospheric turbulence on performance and loads of wind turbines: knowledge gaps and research challenges, Wind Energ. Sci., 11, 509–555, https://doi.org/10.5194/wes-11-509-2026, 2026. a, b
Larsen, S. E., Ejsing Jørgensen, H., Kelly, M. C., Larsén, X. G., Ott, S., and Jørgensen, E.: Elements of extreme wind modeling for hurricanes, Tech. Rep. E No. 0109, Technical University of Denmark, ISBN 87-87-93278-66, 2016. a
Larsén, X. G. and Ott, S.: Adjusted spectral correction method for calculating extreme winds in tropical-cyclone-affected water areas, Wind Energ. Sci., 7, 2457–2468, https://doi.org/10.5194/wes-7-2457-2022, 2022. a
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
Larsén, X. G., Larsen, S. E., Petersen, E. L., and Mikkelsen, T. K.: Turbulence characteristics of wind-speed fluctuations in the presence of open cells: A case study, Bound.-Layer Meteorol., 171, 191–212, https://doi.org/10.1007/s10546-019-00425-8, 2019. a, b, c
Larsén, X. G., Larsen, S. E., Petersen, E. L., and Mikkelsen, T. K.: A Model for the spectrum of the lateral velocity component from mesoscale to microscale and its application to wind-direction variation, Bound.-Layer Meteorol., 178, 415–434, https://doi.org/10.1007/s10546-020-00575-0, 2021. a
Leys, C., Ley, C., Klein, O., Bernard, P., and Licata, L.: Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median, J. Exp. Soc. Psychol., 49, 764–766, https://doi.org/10.1016/j.jesp.2013.03.013, 2013. 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
Li, L., Xiao, Y., Kareem, A., Song, L., and Qin, P.: Modeling typhoon wind power spectra near sea surface based on measurements in the South China Sea, J. Wind Eng. Ind. Aerodyn., 104-106, 565–576, https://doi.org/10.1016/j.jweia.2012.04.005, 2012. a, b, c
Liu, D. H., Song, L. L., Li, G. P., Qin, P., Chen, W. C., and Huang, H. H.: Characteristics of the offshore extreme wind load parameters for wind turbines during strong typhoon Hagupit, J. Trop. Meteorol., 17, 399–408, https://doi.org/10.3969/j.issn.1006-8775.2011.04.010, 2011. a
McKinnon, K. I.: Convergence of the Nelder-Mead simplex method to a nonstationary point, SIAM J. Optim., 9, 148–158, https://doi.org/10.1137/S1052623496303482, 1998. a
Miyake, M., Stewart, R. W., and Burling, R. W.: Spectra and cospectra of turbulence over water, Q. J. R. Meteorol. Soc., 96, 138–143, https://doi.org/10.1002/qj.49709640714, 1970. a
Morrison, I., Businger, S., Marks, F., Dodge, P., and Businger, J. A.: An observational case for the prevalence of roll Vortices in the hurricane boundary layer, J. Atmos. Sci., 62, 2662–2673, https://doi.org/10.1175/JAS3508.1, 2005. a
Müller, S.: Can the Mann model describe the typhoon turbulence: WRF-namelists, Zenodo [data set], https://doi.org/10.5281/zenodo.14610013, 2025. a
Müller, S., Larsén, X. G., and Verelst, D. R.: Tropical cyclone low-level wind speed, shear, and veer: sensitivity to the boundary layer parametrization in the Weather Research and Forecasting model, Wind Energ. Sci., 9, 1153–1171, https://doi.org/10.5194/wes-9-1153-2024, 2024. a, b
Ott, S.: Extreme winds in the Western North Pacific, Tech. Rep. No. 1544, Technical University of Denmark, ISBN 87-550-3500-0, 2006. a
Peña, A., Gryning, S.-E., and Mann, J.: On the length-scale of the wind profile, Q. J. R. Meteorol. Soc., 136, 2119–2131, https://doi.org/10.1002/qj.714, 2010a. a, b
Peña, A., Gryning, S.-E., Mann, J., and Hasager, C. B.: Length scales of the neutral wind profile over homogeneous terrain, J. Appl. Meteorol. Climatol., 49, 792–806, https://doi.org/10.1175/2009JAMC2148.1, 2010b. a, b
Priestley, M.: Evolutionary spectra and non-stationary processes, J. R. Stat. Soc., B: Stat. Methodol., 27, 204–237, https://doi.org/10.1111/j.2517-6161.1965.tb01488.x, 1965. a
Rios Gaona, M. F., Villarini, G., Zhang, W., and Vecchi, G. A.: The added value of IMERG in characterizing rainfall in tropical cyclones, Atmos. Res., 209, 95–102, https://doi.org/10.1016/j.atmosres.2018.03.008, 2018. a
Sanchez Gomez, M., Lundquist, J. K., Deskons, G., Arwade, S. R., Myers, A. T., and Hajjar, J. F.: Wind conditions in category 1-3 tropical cyclones can exceed wind turbine design standards, J. Geophys. Res., 128, e2023JD039233, https://doi.org/10.1029/2023JD039233, 2023. a
Sathe, A., Mann, J., Barlas, T. K., Bierbooms, W., and van Bussel, G.: Influence of atmospheric stability on wind turbine loads, Wind Energy, 16, 1013–1032, https://doi.org/10.1002/we.1528, 2013. a, b, c
Skamarock, W. C.: Evaluating mesoscale NWP models using kinetic energy spectra, Mon. Weather Rev., 132, 3019–3032, https://doi.org/10.1175/MWR2830.1, 2004. a
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 Version 4, National Center for Atmospheric Research: Boulder, CO, USA, Note NCAR/TN-556+STR, 145 pp., https://doi.org/10.5065/1dfh-6p97, 2019a. a
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.: WRF-ARW, National Center for Atmospheric Research [code], https://doi.org/10.5065/D6MK6B4K, 2019b. a, b
Smedman, A.: Occurrence of roll circulations in a shallow boundary-layer, Bound.-Lay. Meteorol., 57, 343–358, https://doi.org/10.1007/BF00120053, 1991. a
Smedman, A., Högström, U., Bergström, H., Rutgersson, A., Kahma, K. K., and Pettersson, H.: A case study of air-sea interaction during swell conditions, J. Geophys. Res.-Oceans, 104, 1999JC900213, https://doi.org/10.1029/1999jc900213, 1999. a
Svensson, N., Sahlée, E., Bergström, H., Nilsson, E., Badger, M., and Rutgersson, A.: A case study of offshore advection of boundary layer rolls over a stably stratified sea surface, Adv. Meteorol., 2017, 1–15, https://doi.org/10.1155/2017/9015891, 2017. a, b
Syed, A. H. and Mann, J.: A model for low-frequency, anisotropic wind fluctuations and coherences in the marine atmosphere, Bound.-Lay. Meteorol., 190, 1, https://doi.org/10.1007/s10546-023-00850-w, 2024. a, b, c, d
Tang, J., Zhang, J. A., Chan, P., Hon, K., Lei, X., and Wang, Y.: A direct aircraft observation of helical rolls in the tropical cyclone boundary layer, Sci. Rep., 11, 18771, https://doi.org/10.1038/s41598-021-97766-7, 2021. a, b, c
Tao, T. and Wang, H.: Modelling of longitudinal evolutionary power spectral density of typhoon winds considering high-frequency subrange, J. Wind Eng. Ind. Aerodyn., 193, 103957, https://doi.org/10.1016/j.jweia.2019.103957, 2019. a, b, c, d
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
Vickers, D. and Mahrt, L.: Quality control and flux sampling problems for tower and aircraft data, J. Atmos. Ocean. Technol., 14, 512–526, https://doi.org/10.1175/1520-0426(1997)014<0512:QCAFSP>2.0.CO;2, 1997. a, b
von Kármán, T.: Progress in the statistical theory of turbulence, Proc. Nat. Akad. Sci., 34, 530–539, https://doi.org/10.1073/pnas.34.11.530, 1948. a, b
Wang, H., Xu, Z., Wu, T., and Mao, J.: Evolutionary power spectral density of recorded typhoons at Sutong Bridge using harmonic wavelets, J. Wind Eng. Ind. Aerodyn., 177, 197–212, https://doi.org/10.1016/j.jweia.2018.04.015, 2018. a, b
Welch, P.: The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms, IEEE T. Acoust. Speech, 15, 70–73, https://doi.org/10.1109/TAU.1967.1161901, 1967. a
Wenchao, C., Lili, S., Shiqun, Z., Haohui, H., and Peng, Q.: Analysis on gust factor of tropical cyclone strong wind over different underlying surfaces, Sci. China Technol. Sci., 54, 2576–2586, https://doi.org/10.1007/s11431-011-4511-0, 2011. a
Worsnop, R. P., Bryan, G. H., Lundquist, J. K., and Zhang, J. A.: Using large-eddy simulations to define spectral and coherence characteristics of the hurricane boundary layer for wind-energy applications, Bound.-Lay. Meteorol., 165, 55–86, https://doi.org/10.1007/s10546-017-0266-x, 2017. a, b, c
Xu, Y. L. and Chen, J.: Characterizing nonstationary wind speed using empirical mode decomposition, J. Struct. Eng., 130, 912–920, https://doi.org/10.1061/(ASCE)0733-9445(2004)130:6(912), 2004. a, b, c
Zhang, J. A., Katsaros, K. B., Black, P. G., Lehner, S., French, J. R., and Drennan, W. M.: Effects of roll vortices on turbulent fluxes in the hurricane boundary layer, Bound.-Lay. Meteorol., 128, 173–189, https://doi.org/10.1007/s10546-008-9281-2, 2008. a, b, c
Zhang, J. A., Rogers, R. F., Nolan, D. S., and Marks, F. D.: On the characteristic height scales of the hurricane boundary layer, Mon. Weather Rev., 139, 2523–2535, https://doi.org/10.1175/MWR-D-10-05017.1, 2011. a, b, c
Zhou, D., Pan, X., Guo, J., Sun, X., Wang, C., and Zheng, J.: Resilience quantification of offshore wind farm cluster under the joint influence of typhoon and its secondary disasters, Appl. Energy, 383, https://doi.org/10.1016/j.apenergy.2025.125323, 2025. a
Zhu, P., Zhang, J. A., and Masters, F. J.: Wavelet analyses of turbulence in the hurricane surface layer during landfalls, J. Atmos. Sci., 67, 3793–3805, https://doi.org/10.1175/2010JAS3437.1, 2010. a
Short summary
Wind farms are being developed in areas prone to tropical cyclones. However, it remains unclear whether turbulence models in current design standards, such as the Mann uniform shear model, are suitable for these conditions. For the first time, the Mann model is assessed in depth using high-frequency measurements from four typhoons. Larger-than-predicted spectral energy is found at small wavenumbers in the outer cyclone and, in some cases, in the crosswind component in the inner cyclone.
Wind farms are being developed in areas prone to tropical cyclones. However, it remains unclear...
Altmetrics
Final-revised paper
Preprint