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
Related authors
No articles found.
David Robert Verelst, Rasmus Sode Lund, and Jean-Philippe Roques
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-24, https://doi.org/10.5194/wes-2024-24, 2024
Preprint under review for WES
Short summary
Short summary
This study discusses key issues when performing simulations of a dynamic power cable that is connected to a floating wind turbine. Such simulations are an important tool to asses if the floater and cable motions cause the power cable to survive or fail specific conditions, and generally assure they can fulfil their intended design life. This work describes how to model such power cables and combine that with a fully coupled model of an operating floating wind turbine.
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.
Ásta Hannesdóttir, David R. Verelst, and Albert M. Urbán
Wind Energ. Sci., 8, 231–245, https://doi.org/10.5194/wes-8-231-2023, https://doi.org/10.5194/wes-8-231-2023, 2023
Short summary
Short summary
In this work we use observations of large coherent fluctuations to define a probabilistic gust model. The gust model provides the joint description of the gust rise time, amplitude, and direction change. We perform load simulations with a coherent gust according to the wind turbine safety standard and with the probabilistic gust model. A comparison of the simulated loads shows that the loads from the probabilistic gust model can be significantly higher due to variability in the gust parameters.
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.
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.
Gesine Wanke, Leonardo Bergami, Frederik Zahle, and David Robert Verelst
Wind Energ. Sci., 6, 203–220, https://doi.org/10.5194/wes-6-203-2021, https://doi.org/10.5194/wes-6-203-2021, 2021
Short summary
Short summary
This article regards a rotor redesign for a wind turbine in upwind and in downwind rotor configurations. A simple optimization tool is used to estimate the aerodynamic planform, as well as the structural mass distribution of the rotor blade. The designs are evaluated in full load base calculations according to the IEC standard with the aeroelastic tool HAWC2. A scaling model is used to scale turbine and energy costs from the design loads and compare the costs for the turbine configurations.
Laura Schröder, Nikolay Krasimirov Dimitrov, and David Robert Verelst
Wind Energ. Sci., 5, 1007–1022, https://doi.org/10.5194/wes-5-1007-2020, https://doi.org/10.5194/wes-5-1007-2020, 2020
Short summary
Short summary
We suggest a methodology for correlating loads with component reliability of turbines in wind farms by combining physical modeling with machine learning. The suggested approach is demonstrated on an offshore wind farm for comparing performance, loads and lifetime estimations against recorded main bearing failures from maintenance reports. It is found that turbines positioned at the border of the wind farm with a higher expected AEP are estimated to experience earlier main bearing failures.
Gesine Wanke, Leonardo Bergami, and David Robert Verelst
Wind Energ. Sci., 5, 929–944, https://doi.org/10.5194/wes-5-929-2020, https://doi.org/10.5194/wes-5-929-2020, 2020
Short summary
Short summary
Converting an upwind wind turbine into a downwind configuration is shown to come with higher edgewise loads due to lower edgewise damping. The study shows from modal displacements of a reduced-order turbine model that the interaction between the forces on the rotor, the rotor motion, and the tower torsion is the main reason for the observed damping decrease.
Ozan Gözcü and David R. Verelst
Wind Energ. Sci., 5, 503–517, https://doi.org/10.5194/wes-5-503-2020, https://doi.org/10.5194/wes-5-503-2020, 2020
Short summary
Short summary
Geometrically nonlinear blade modeling effects on the turbine loads and computation time are investigated in an aero-elastic code based on multibody formulation. A large number of fatigue load cases are used in the study. The results show that the nonlinearities become prominent for large and flexible blades. It is possible to run nonlinear models without significant increase in computational time compared to the linear model by changing the matrix solver type from dense to sparse.
Jianting Du, Rodolfo Bolaños, Xiaoli Guo Larsén, and Mark Kelly
Ocean Sci., 15, 361–377, https://doi.org/10.5194/os-15-361-2019, https://doi.org/10.5194/os-15-361-2019, 2019
Short summary
Short summary
Ocean surface waves generated by wind and dissipated by white capping are two important physics processes for numerical wave simulations. In this study, a new pair of wind–wave generation and dissipation source functions is implemented in the wave model SWAN, and it shows better performance in real wave simulations during two North Sea storms. The new source functions can be further used in other wave models for both academic and engineering purposes.
Alexander R. Stäblein, Morten H. Hansen, and David R. Verelst
Wind Energ. Sci., 2, 343–360, https://doi.org/10.5194/wes-2-343-2017, https://doi.org/10.5194/wes-2-343-2017, 2017
Short summary
Short summary
Coupling between bending and twist has a significant influence on the aeroelastic response of wind turbine blades. The coupling can arise from the blade geometry or from the anisotropic properties of the blade material. Bend–twist coupling can be utilised to reduce the fatigue loads of wind turbine blades. In this study the effects of material-based coupling on the aeroelastic modal properties and stability limits of the DTU 10 MW Reference Wind Turbine are investigated.
Related subject area
Thematic area: Wind and the atmosphere | Topic: Atmospheric physics
The multi-scale coupled model: a new framework capturing wind farm–atmosphere interaction and global blockage effects
Seasonal variability of wake impacts on US mid-Atlantic offshore wind plant power production
Simulating low-frequency wind fluctuations
Bayesian method for estimating Weibull parameters for wind resource assessment in a tropical region: a comparison between two-parameter and three-parameter Weibull distributions
Lessons learned in coupling atmospheric models across scales for onshore and offshore wind energy
Investigating the physical mechanisms that modify wind plant blockage in stable boundary layers
Offshore wind energy forecasting sensitivity to sea surface temperature input in the Mid-Atlantic
Lifetime prediction of turbine blades using global precipitation products from satellites
Evaluation of low-level jets in the southern Baltic Sea: a comparison between ship-based lidar observational data and numerical models
Predicting power ramps from joint distributions of future wind speeds
Scientific challenges to characterizing the wind resource in the marine atmospheric boundary layer
Research challenges and needs for the deployment of wind energy in hilly and mountainous regions
Observer-based power forecast of individual and aggregated offshore wind turbines
Sensitivity analysis of mesoscale simulations to physics parameterizations over the Belgian North Sea using Weather Research and Forecasting – Advanced Research WRF (WRF-ARW)
Sebastiano Stipa, Arjun Ajay, Dries Allaerts, and Joshua Brinkerhoff
Wind Energ. Sci., 9, 1123–1152, https://doi.org/10.5194/wes-9-1123-2024, https://doi.org/10.5194/wes-9-1123-2024, 2024
Short summary
Short summary
This paper introduces the multi-scale coupled (MSC) model, an engineering framework aimed at modeling turbine–wake and wind farm–gravity wave interactions, as well as local and global blockage effects. Comparisons against large eddy simulations show that the MSC model offers a valid contribution towards advancing our understanding of the coupled wind farm–atmosphere interaction, helping refining power estimation methodologies for existing and future wind farm sites.
David Rosencrans, Julie K. Lundquist, Mike Optis, Alex Rybchuk, Nicola Bodini, and Michael Rossol
Wind Energ. Sci., 9, 555–583, https://doi.org/10.5194/wes-9-555-2024, https://doi.org/10.5194/wes-9-555-2024, 2024
Short summary
Short summary
The US offshore wind industry is developing rapidly. Using yearlong simulations of wind plants in the US mid-Atlantic, we assess the impacts of wind turbine wakes. While wakes are the strongest and longest during summertime stably stratified conditions, when New England grid demand peaks, they are predictable and thus manageable. Over a year, wakes reduce power output by over 35 %. Wakes in a wind plant contribute the most to that reduction, while wakes between wind plants play a secondary role.
Abdul Haseeb Syed and Jakob Mann
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-142, https://doi.org/10.5194/wes-2023-142, 2023
Revised manuscript accepted for WES
Short summary
Short summary
Wind flow consists of swirling patterns of air called eddies, some as big as many kilometers across, while others are as small as just a few meters. This paper introduces a method to simulate these large swirling patterns on a flat grid. Using these simulations we can better figure out how these large eddies affect big wind turbines in terms of loads and forces.
Mohammad Golam Mostafa Khan and Mohammed Rafiuddin Ahmed
Wind Energ. Sci., 8, 1277–1298, https://doi.org/10.5194/wes-8-1277-2023, https://doi.org/10.5194/wes-8-1277-2023, 2023
Short summary
Short summary
A robust technique for wind resource assessment with a Bayesian approach for estimating Weibull parameters is proposed. Research conducted using seven sites' data in the tropical region from 1° N to 21° S revealed that the three-parameter (3-p) Weibull distribution with a non-zero shift parameter is a better fit for wind data that have a higher percentage of low wind speeds. Wind data with higher wind speeds are a special case of the 3-p distribution. This approach gives accurate results.
Sue Ellen Haupt, Branko Kosović, Larry K. Berg, Colleen M. Kaul, Matthew Churchfield, Jeffrey Mirocha, Dries Allaerts, Thomas Brummet, Shannon Davis, Amy DeCastro, Susan Dettling, Caroline Draxl, David John Gagne, Patrick Hawbecker, Pankaj Jha, Timothy Juliano, William Lassman, Eliot Quon, Raj K. Rai, Michael Robinson, William Shaw, and Regis Thedin
Wind Energ. Sci., 8, 1251–1275, https://doi.org/10.5194/wes-8-1251-2023, https://doi.org/10.5194/wes-8-1251-2023, 2023
Short summary
Short summary
The Mesoscale to Microscale Coupling team, part of the U.S. Department of Energy Atmosphere to Electrons (A2e) initiative, has studied various important challenges related to coupling mesoscale models to microscale models. Lessons learned and discerned best practices are described in the context of the cases studied for the purpose of enabling further deployment of wind energy. It also points to code, assessment tools, and data for testing the methods.
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
Short summary
Short summary
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.
Stephanie Redfern, Mike Optis, Geng Xia, and Caroline Draxl
Wind Energ. Sci., 8, 1–23, https://doi.org/10.5194/wes-8-1-2023, https://doi.org/10.5194/wes-8-1-2023, 2023
Short summary
Short summary
As wind farm developments expand offshore, accurate forecasting of winds above coastal waters is rising in importance. Weather models rely on various inputs to generate their forecasts, one of which is sea surface temperature (SST). In this study, we evaluate how the SST data set used in the Weather Research and Forecasting model may influence wind characterization and find meaningful differences between model output when different SST products are used.
Merete Badger, Haichen Zuo, Ásta Hannesdóttir, Abdalmenem Owda, and Charlotte Hasager
Wind Energ. Sci., 7, 2497–2512, https://doi.org/10.5194/wes-7-2497-2022, https://doi.org/10.5194/wes-7-2497-2022, 2022
Short summary
Short summary
When wind turbine blades are exposed to strong winds and heavy rainfall, they may be damaged and their efficiency reduced. The problem is most pronounced offshore where turbines are tall and the climate is harsh. Satellites provide global half-hourly rain observations. We use these rain data as input to a model for blade lifetime prediction and find that the satellite-based predictions agree well with predictions based on observations from weather stations on the ground.
Hugo Rubio, Martin Kühn, and Julia Gottschall
Wind Energ. Sci., 7, 2433–2455, https://doi.org/10.5194/wes-7-2433-2022, https://doi.org/10.5194/wes-7-2433-2022, 2022
Short summary
Short summary
A proper development of offshore wind farms requires the accurate description of atmospheric phenomena like low-level jets. In this study, we evaluate the capabilities and limitations of numerical models to characterize the main jets' properties in the southern Baltic Sea. For this, a comparison against ship-mounted lidar measurements from the NEWA Ferry Lidar Experiment has been implemented, allowing the investigation of the model's capabilities under different temporal and spatial constraints.
Thomas Muschinski, Moritz N. Lang, Georg J. Mayr, Jakob W. Messner, Achim Zeileis, and Thorsten Simon
Wind Energ. Sci., 7, 2393–2405, https://doi.org/10.5194/wes-7-2393-2022, https://doi.org/10.5194/wes-7-2393-2022, 2022
Short summary
Short summary
The power generated by offshore wind farms can vary greatly within a couple of hours, and failing to anticipate these ramp events can lead to costly imbalances in the electrical grid. A novel multivariate Gaussian regression model helps us to forecast not just the means and variances of the next day's hourly wind speeds, but also their corresponding correlations. This information is used to generate more realistic scenarios of power production and accurate estimates for ramp probabilities.
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
Short summary
Short summary
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.
Andrew Clifton, Sarah Barber, Alexander Stökl, Helmut Frank, and Timo Karlsson
Wind Energ. Sci., 7, 2231–2254, https://doi.org/10.5194/wes-7-2231-2022, https://doi.org/10.5194/wes-7-2231-2022, 2022
Short summary
Short summary
The transition to low-carbon sources of energy means that wind turbines will need to be built in hilly or mountainous regions or in places affected by icing. These locations are called
complexand are hard to develop. This paper sets out the research and development (R&D) needed to make it easier and cheaper to harness wind energy there. This includes collaborative R&D facilities, improved wind and weather models, frameworks for sharing data, and a clear definition of site complexity.
Frauke Theuer, Andreas Rott, Jörge Schneemann, Lueder von Bremen, and Martin Kühn
Wind Energ. Sci., 7, 2099–2116, https://doi.org/10.5194/wes-7-2099-2022, https://doi.org/10.5194/wes-7-2099-2022, 2022
Short summary
Short summary
Remote-sensing-based approaches have shown potential for minute-scale forecasting and need to be further developed towards an operational use. In this work we extend a lidar-based forecast to an observer-based probabilistic power forecast by combining it with a SCADA-based method. We further aggregate individual turbine power using a copula approach. We found that the observer-based forecast benefits from combining lidar and SCADA data and can outperform persistence for unstable stratification.
Adithya Vemuri, Sophia Buckingham, Wim Munters, Jan Helsen, and Jeroen van Beeck
Wind Energ. Sci., 7, 1869–1888, https://doi.org/10.5194/wes-7-1869-2022, https://doi.org/10.5194/wes-7-1869-2022, 2022
Short summary
Short summary
The sensitivity of the WRF mesoscale modeling framework in accurately representing and predicting wind-farm-level environmental variables for three extreme weather events over the Belgian North Sea is investigated in this study. The overall results indicate highly sensitive simulation results to the type and combination of physics parameterizations and the type of the weather phenomena, with indications that scale-aware physics parameterizations better reproduce wind-related variables.
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...
Altmetrics
Final-revised paper
Preprint