Wind Speed Distribution Research Articles

Reconstruction of Unsteady Wind Field Based on CFD and Reduced-Order Model

Short-term wind power forecasting is crucial for updating the wind power trading strategy, equipment protection and control regulation. To solve the difficulty surrounding the instability of the statistical model and the time-consuming nature of the physical model in short-term wind power forecasting, two innovative wind field reconstruction methods combining CFD and a reduced-order model were developed. In this study, POD and Tucker decomposition were employed to obtain the spatial–temporal information correlation of 2D and 3D wind fields, and their inverse processes were combined with sparse sensing to reconstruct multi-dimensional unsteady wind fields. Simulation and detailed discussion were performed to verify the practicability of the proposed algorithms. The simulation results indicate that the wind speed distributions could be reconstructed with reasonably high accuracy (where the absolute velocity relative error was less than 0.8%) using 20 sensors (which only accounted for 0.04% of the total data in the 3D wind field) based on the proposed algorithms. The factors influencing the results of reconstruction were systematically analyzed, including all-time steps, the number of basis vectors and 4-mode dimensions, the diversity of CFD databases, and the reconstruction time. The results indicated that the reconstruction time could be shortened to the time interval of data acquisition to synchronize data acquisition with wind field reconstruction, which is of great significance in the reconstruction of unsteady wind fields. Although there are still many studies to be carried out to achieve short-term predictions, both unsteady reconstruction methods proposed in this paper enable a new direction for short-term wind field prediction.

Effect of wind speed distribution and site assessment on pitch bearing loads and life

In this paper, the wind speed distribution at thirteen onshore and offshore wind sites has been studied. Different probability distributions are used to estimate the wind speed distribution. Several goodness-of-fit indicators were used to assess the suitability of the fitting. The highest results were achieved by Kernel distribution in both onshore and offshore wind sites. Onshore wind sites did not fit well compared to offshore wind sites. Rayleigh distribution results at onshore wind sites were worse than at offshore wind sites. Onshore and offshore wind distributions result in various load duration distributions in pitch bearing. The concept of life ratio was introduced to compare the long-term fatigue life of the pitch bearing in different wind speed conditions. It is observed that the fatigue life of the pitch bearings in some wind sites is less than that of related IEC classes, and the risk of failure of the pitch bearing before the end of its expected designed fatigue life exists.

A baseline for ensemble-based, time-resolved inflow reconstruction for a single turbine using large-eddy simulations and latent diffusion models

We are interested in reconstructing winds flowing through a turbine on a second-by-second basis over a 10 min window. Previously, we developed a machine learning algorithm that takes in a snapshot of wind speed measurements and generates ensembles of three-dimensional wind field estimates. Here, we use these estimates as initial conditions in large-eddy simulations and reconstruct atmospheric and turbine response dynamic quantities in a synthetic field campaign. In doing so, we establish a baseline for model validation that future time-aware data assimilation techniques will be compared to. In turbine-free case studies, ground truth wind speeds consistently fall within our estimated wind speed distribution for the first 100 s after the simulation start. In simulations with turbines, the wind estimates show a small bias of 0.10 m s−1 and good correlation of 0.80 during the first 100 s. During this window, our estimates of the Blade 1 bending moment and generator power typically span the ground truth, with the estimate of the former performing better overall. In summary, this approach shows promise as a stand-alone technique for reconstructing real-world inflow and turbine dynamics in 1−2 min windows and as a foundation for future time-aware data assimilation techniques.

Comparative Analysis of Wind Energy Potential with Nakagami and Weibull Distribution Methods for Wind Turbine Planning

Wind energy is renewable energy used as an energy source for wind power plants (PLTB). The most common distribution method used to model wind speed distribution data is the Weibull distribution. The Nakagami distribution has begun to be widely used in several studies to model wind speed distribution data. The Nakagami distribution is considered an alternative to the Weibull distribution in modeling wind speed distribution data. This study aims to compare the distribution of Nakagami and Weibull in analyzing wind power potential and calculating the resulting Wind Energy Production (WEP), using wind speed distribution data from both distributions in Kuta Raja, Banda Aceh and Lhoknga, Aceh Besar. The wind speed data used is satellite data (secondary data) downloaded via windguru.cz, with the most stable wind speed being a wind speed of 3-5 m/s. The value of wind power potential at the Kuta Raja location, Banda Aceh was obtained at 64.16% with the Nakagami distribution and 62.73% with the Weibull distribution, and 73.60% with the Nakagami distribution and 73.28% at the Lhoknga location, Aceh Besar. The comparison of these two distributions produces a Weibull distribution that is superior to the Nakagami distribution for both locations, where the Weibull distribution has a smaller error value and produces a WEP value that is in accordance with the actual/observable data compared to the Nakagami distribution. In this study, the Nakagami distribution has results that make this distribution an alternative or comparison to the Weibull distribution in distributing wind speed data with further research.

Wind Retrieval from Constellations of Small SAR Satellites: Potential for Offshore Wind Resource Assessment

The planning of offshore wind energy projects requires wind observations over long periods for the establishment of wind speed distributions. In the marine environment, high-quality in situ observations are sparse and restricted to point locations. Numerical modeling is typically used to determine the spatial variability of the wind resource. Synthetic Aperture Radar (SAR) observations from satellites can be used for retrieval of wind fields over the ocean at a high spatial resolution. The recent launch of constellations of small SAR satellites by private companies will improve the sampling of SAR scenes significantly over the coming years compared with the current sampling rates offered by multi-purpose SAR missions operated by public space agencies. For the first time, wind fields are retrieved from a series of StriX SAR scenes delivered by Synspective (Japan) and also from Sentinel-1 scenes delivered by the European Space Agency. The satellite winds are compared with wind speed observations from the FINO3 mast in the North Sea. This leads to root-mean-square errors of 1.4–1.8 m s−1 and negative biases of −0.4 m s−1 and −1.0 m s−1, respectively. Although the Geophysical Model Functions (GMF) applied for wind retrievals have not yet been tuned for StriX SAR observations, the wind speed accuracy is satisfactory. Through conditional sampling, we estimate the wind resource from current and future SAR sampling scenarios where the number of SAR satellites in orbit is increasing over time. We find that hourly samples are needed to fully capture the diurnal wind speed variability at the site investigated. A combination of SAR samples from current missions with samples from clusters of small SAR satellites can yield the necessary number of wind speed samples for accurate wind resource estimation. This is particularly important for sites with pronounced diurnal wind speed variability. An additional benefit of small SAR satellites is that wind speed variability can be mapped at the sub-km scale. The very high spatial resolution is valuable for characterizing the wind conditions in the vicinity of existing offshore wind farms.

Impact of components number selection in truncated Gaussian mixture model and interval partition on wind speed probability distribution estimation

Owing to global environmental concerns in recent years, wind power has garnered significant attention as a clean and renewable energy source. The probability distribution of the wind speed plays a pivotal role in assessing the wind energy potential and power system operation. As a single Gaussian may be powerless in capturing features of real wind speed distributions owing to the randomness of wind speed, this study aimed to use a truncated Gaussian mixture (TGM) distribution with determined optimal Gaussian components number and suitable interval partitions as a possible alternative model to estimate the wind speed probability distribution (WSPD). In particular, two expectation-maximization algorithms were employed to determine the parameters of the TGM models. Subsequently, the continuous ranked probability score loss theory related to TGM models with two arbitrary truncated points was developed and employed to detect the optimal number of Gaussian components in the TGM models, in addition to casually or empirically setting the number of Gaussian components. Subsequently, the WSPD is estimated using the detected optimal Gaussian component number and the corresponding parameter determination results. Considering that different interval partitions may result in diverse accuracy results, to enhance the flexibility of the above TGM models, another technique is designed using the False-Discovery-Rate-inspired algorithm. This algorithm performs well in the case of large-scale hypothesis tests and can determine the suitable interval partition number. The feasibility and effectiveness of the proposed model were validated using real wind speed data collected from two stations: Haikou and Wulumuqi stations. Comparison of the different goodness-of-fit evaluations demonstrates that the estimation accuracy of WSPD of the proposed approach is comparable to those of the truncated Gaussian mixture distribution with arbitrary Gaussian component numbers and some other comparison models.

Using Observed and Modelled Heat Fluxes for Improved Extrapolation of Wind Distributions

Modelling the horizontal and vertical variation of wind speed is crucial for wind energy applications. A model frequently used for this purpose is part of the Wind Atlas Analysis and Application program (WAsP). Here, we modify the model in WAsP to account for local atmospheric stability parameters. Atmospheric stability effects are treated by using the impact of a temperature scale on the geostrophic drag law and the diabatic logarithmic wind profile. Using this approach, wind-direction dependent mean and standard deviation of a surface-layer temperature scale and a mean boundary-layer height scale can be obtained from either numerical weather prediction model output or observations to improve vertical extrapolations of Weibull wind speed distribution parameters. The modified atmospheric stability model is validated at six flat sites in northwestern Europe. The surface-layer temperature scale is available from sonic anemometer measurements at three of the sites. At all sites the temperature scale is also estimated from reanalysis data and from mesoscale model output. The modified model improves the aggregated estimations of power density distributions when extrapolating to nearby locations from 5.2 to 3%, when using the temperature scale derived from either observations or mesoscale/reanalysis output compared to the current model.

Flutter characteristics of bridges under non-uniform wind speed

Increasing the bridge span reduces the aerodynamic stability of a bridge’s structure, and the complex wind environment in mountainous areas makes wind-resistant designs of bridges more difficult. The flutter response of bridges under a uniform flow field has been extensively studied. However, there are still various blind areas regarding the impact of non-uniform inflow on the aerodynamic stability of structures. Because of the increasing complexity of wind fields at bridge sites in mountainous areas, further investigation of the wind resistance characteristics of bridges under inhomogeneous wind fields is required. The non-uniform wind speed distribution model is first summarized in this paper, and the research conditions are set based on it. Considering a simply supported beam bridge and suspension bridge as the research objects, the flutter responses of the two bridges under the set conditions were explored, and their likenesses were compared. The analysis results demonstrate that the flutter stability of the bridge is reduced under non-uniform wind speed; therefore, the non-uniformity of wind speed cannot be completely ignored in the wind resistance design. The degree of influence of wind velocity non-uniformity on structural flutter stability hardly changes with the change in angle of attack (AOA), which can be used to simplify the bridges wind resistance design under a nonuniform inflow.

Improving the energy efficiency of wind turbines for charging batteries

The article describes the method for determining the optimal angular velocity and the number of turns of the generator winding on permanent magnets powered by a wind turbine operating in specific operating conditions according to the wind speed regime. The optimization criterion is the maximum potential of energy that can be used to charge the battery. The permissible power of the generator and wind turbine, current and battery charging voltage are accepted as limiting factors. The restriction is provided by connecting a ballast resistor to the generator output. The power developed by the turbine is determined taking into account the wind energy utilization factor, which depends on the angular velocity of its shaft and wind speed. Two variants of power limitation are compared: by limiting the angular velocity by aerodynamic means and by stopping the wind turbine. The return of energy to charging in both cases is determined taking into account the distribution of wind speeds, obeying the Weibull probability distribution law. As an example, the calculation of the possible annual power generation for charging a battery with a capacity of 200 A∙h with a voltage of 24 volts from a synchron generator with a number of poles of 48 driven by a wind turbine with a radius of 2 meters, operating in an area with an average wind speed of 5 m/s. The calculation shows that for the parameters and operating conditions of the electrical installation used in the example, the maximum annual energy output (3.3 × 103 kWh) is observed at optimal 11 turns of the winding at each of the poles of the generator. The deviation of the number of turns from the optimal one in both directions by 2 times leads, with the same dimensions of the wind turbine, to a decrease in annual energy output by 3...5 times, which is a clear proof of the need to carry out such a calculation for each specific wind turbine.

Comprehensive validation of 68 wind speed models highlights the benefits of ensemble approaches

Many different wind speed models support assessing the wind climate. Depending on their development, spatiotemporal resolution, and intended use, typical wind speed model types are wind atlases, reanalysis products, regional climate, and global climate models. They realistically represent different properties of the wind climate. For the first time, 68 wind speed models of different types are assessed for their predictive power for (1) mean wind speed, (2) wind speed distribution, and (3) temporal variability. In addition, eight ensembles of individual models from each model type are created and validated. The hypothesis is tested that ensembles of wind speed models generally improve model accuracy. For the validation, 148 wind speed time series with a data availability of at least ten years in 1979 to 2014 were used. The measurement sites were separated into flat and open, and mountainous locations. The results reveal the improved model performance of ensembles compared to individual models. Only wind atlases are capable of estimating wind speed properties at mountainous locations. Their median coefficient of determination related to mean wind speed at mountainous sites is 0.81 but less than 0.15 for any other model type. One important conclusion is that individual models may not be appropriate for wind resource assessment without prior validation. Furthermore, ensembles are recommended to improve predictive power for any wind climate properties. The results indicate how well the different model types can reproduce the analyzed wind speed characteristics.

The basic solar wind speed distribution and its sunspot cycle variations

In this paper, it is suggested that the latitudinal solar wind speed observed by the Ulysses spacecraft during the lowest solar activity (when both the ecliptic and magnetic equators coincide) may be identified as the basic speed distribution throughout the solar cycle. We demonstrate this suggestion by rotating this particular Ulysses distribution counterclockwise up to 70° in accordance with the rotation of the equivalent dipole axis during active periods of the cycle. The corresponding magnetic equator in the Carrington map latitude-longitude (27 days) becomes quasi-sinusoidal with respect to the ecliptic equator. The quasi-sinusoidal magnetic equator on the Carrington map and its modification associated with the degree of sunspot activities can explain the two high speed peaks (750–800 km/s) and the two lowest speed (350 km/s) during 27-day solar rotation periods, most clearly recognizable after the sunspot peak period. Thus, it may be not necessary to consider coronal holes or open regions as the source of high speed streams. In fact, this particular (lowest solar activity) Ulysses distribution may represent the speed distribution pattern by the basic generation process of the solar wind itself.

Assessing Predictions of Australian Offshore Wind Energy Resources from Reanalysis Datasets

Offshore wind farms are a current area of interest in Australia due to their ability to support its transition to renewable energy. Climate reanalysis datasets that provide simulated wind speed data are frequently used to evaluate the potential of proposed offshore wind farm locations. However, there has been a lack of comparative studies of the accuracy of wind speed predictions from different reanalysis datasets for offshore wind farms in Australian waters. This paper assesses wind speed distribution accuracy and compares predictions of offshore wind turbine power output in Australia from three international reanalysis datasets: BARRA, ERA5, and MERRA-2. Pressure level data were used to determine wind speeds and capacity factors were calculated using a turbine bounding curve. Predictions across the datasets show consistent spatial and temporal variations in the predicted plant capacity factors, but the magnitudes differ substantially. Compared to weather station data, wind speed predictions from the BARRA dataset were found to be the most accurate, with a higher correlation and lower average error than ERA5 and MERRA-2. Significant variation was seen in predictions and there was a lack of similarity with weather station measurements, which highlights the need for additional site-based measurements.

Friction Velocity and Aerodynamic Drag Coefficient Retrieval from Sentinel-1 IW Cross-Polarization C-SAR Images under Hurricane Conditions

This study presents an approach for friction velocity and aerodynamic drag coefficient retrieval utilizing C-band VH SAR observations from Sentinel-1. The dataset contained 14 SAR images collected under six hurricane scenes co-analyzed with stepped frequency microwave radiometer (SFMR) measurements. The basis for creating this approach utilizes the results proposed earlier linking the parameters of the atmospheric boundary layer from GPS-dropsondes data to the ocean surface emissivity from SFMR measurements. The obtained dependencies of the ocean surface emissivity on surface friction velocity, aerodynamic drag coefficient, and surface wind speed are analyzed together with the collocated SAR data leading to the new GMF valid for the retrieval of friction velocities ranging from 0.55–1.56 m/s and drag coefficient values ranging from 0.00076–0.00232 for all sub swaths. Within the framework of the proposed approach, dependences of the normalized radar cross-section on the surface wind speed were also obtained and used for comparison with existing GMFs to show that the proposed approach is valid. A good consistency was obtained when comparing our results with H14E and MS1A. As an example the distributions of friction velocity, drag coefficient, and surface wind speed retrieved from the Hurricane Maria SAR image (23 September 2017) were considered.

Astronomical seeing and wind speed distributions with ERA5 data at Lenghu site on the Tibetan Plateau

ABSTRACT In this paper, we present comprehensive night-time seeing statistics at the Lenghu site using data collected between 2018 and 2021 using a differential image motion monitor. The results show that the median seeing is 0.80 arcsec, which slightly larger than that previously reported due to climate change. Additionally, we investigated the annual, seasonal, and monthly seeing statistics, as well as possible hourly seeing trends in each season. The results demonstrate that the seeing at the Lenghu site is very stable, with monthly median seeing varying from 0.6 to 0.95 arcsec. The seasonal dependence of seeing shows obvious variations over different years. The best year of seeing was 2019 (0.74 arcsec), and the best seeing occurred in spring (0.68 arcsec) to early summer (June, 0.63 arcsec), while the worst seeing occurred in winter (0.80 arcsec). The minimum seeing value throughout the night is at approximately three hours before dawn. Furthermore, wind speed profiles, especially V200 (particularly the wind speed at 200 mbar), are important parameters for evaluating astronomical sites in terms of their suitability for adaptive optics. We first show the complete monthly average behaviours of the wind speed profile and V200 using the European Center for Medium-Range Weather Forecast reanalysis data sets. The results demonstrate the annual periodicity and variability of V200, and the yearly V200 modulation function is also provided. V200 maintains large values throughout the autumn (average: 35.01 m s−1) and winter (average: 34.33 m s−1), while the minimum V200 occurs in spring (average: 27.93 m s−1).

Field Measurements of Wind Microclimate at the Vehicle Level on a Bridge Deck under Typical Canyon Terrain

The wind field characteristics of a bridge site are the key issues for driving the safety assessment condition of mountain canyon terrain. Field measurements on a bridge deck were conducted to investigate the microclimate wind environment at the vehicle level. The measurement points were arranged in the midspan and bridge tower regions along the bridge at vehicle height above the bridge deck. The height-dependent characteristics of the microclimate wind environment at the midspan and bridge tower regions were extensively analyzed. The results show that the mean wind profiles at midspan approximately confirm the power exponential distribution. However, the mean wind profiles show non-power-exponential distribution at the tower regions. The vertical mean wind speed profiles at the midspan and bridge tower regions were then statistically fitted and suggested. The wind speed distribution at the bridge tower regions showed obvious shielding or acceleration effects. Therefore, a typical wind envelope curve across the tower region was proposed along the driving direction. For the turbulence characteristics, turbulence intensities at the tower regions are larger than those at the midspan, and integral scales at the tower regions are smaller than those at the midspan. In this study, a double logarithm third polynomial was recommended to depict the energy distribution of the turbulence microclimate wind in the frequency domain instead of the commonly used empirical spectrum functions. Gust wind speeds were also discussed, and showed that gust wind speeds are more reasonable for evaluating vehicle driving safety and comfort in long-span bridges, especially at the tower regions.

Comparing Weibull distribution method and Gram–Charlier series method within the context of estimating low-occurrence strong wind speed of idealized building cases

Understanding low-occurrence strong wind speed (LOSWS) distributions at the pedestrian level is important. However, the robustness of the statistical methods for estimating LOSWSs under different conditions remains uncertain. In this study, the performances of the Weibull distribution method and Gram–Charlier series (GCS) method were compared. Their accuracies for isolated building and building array cases were analyzed. It was found that the constant peak factor (PF) of the Gaussian distribution showed acceptable accuracy only when the exceedance probability q = 10%. For q = 0.1%, the PF of the three-parameter Weibull distribution (3W) was more accurate than that of GCS-3rd. For fitting probability density functions, GCS-6th exhibited better flexibility than the two-parameter Weibull distribution (2W) and 3W. However, large leading terms leaded to oscillations at several points in GCS-6th. Regarding the estimation accuracy of LOSWSs, the 2W and 3W methods are superior to the GCS methods when the available orders of statistics are equal. Although the GCS methods showed higher accuracy than the 3W method in some regions, the oscillations at specific points in the GCS methods may lead to lower accuracy on average. The present findings can serve as an illuminating reference for further applications of these statistical methods.

The monthly average distributions of sea-surface wind speeds and directions in the East Vietnam Sea

This paper analyzes a monthly global gridded sea-surface wind dataset from Jan 1991 to Dec 2020, sourced from the International Comprehensive Ocean-Atmosphere Data Set (ICOADS). The goal is to construct schematic maps depicting the monthly average distributions of sea-surface wind speeds and directions in the East Vietnam Sea. These maps provide a visual representation of the monsoon patterns at different points and areas in the region. The statistical and analytical findings reveal that the East Vietnam Sea is primarily influenced by two types of monsoons: Northeast and Southwest. When one is strong, the other is weak, and vice versa. The Northeast monsoon dominates from September to April of the following year, while the Southwest monsoon is more dominant from May to August. The intensity of the Northeast monsoon is usually greater than that of the Southwest monsoon. These results provide an overview of the monsoon distributions in the East Vietnam Sea, which can assist scientists and managers in proposing policies and measures suitable to the climate environment and wind seasons. These findings also help in the effective exploitation of abundant wind energy resources in coastal Vietnam and the East Vietnam Sea. Furthermore, the results are consistent with previous studies of monsoons in the East Vietnam Sea, indicating that the data source used in this study is reliable and can be utilized as input data for tidal, currents, or oil spread models.

Modelling the Wind Speed Using Exponentiated Weibull Distribution: Case Study of Poprad-Tatry, Slovakia

In the paper, we statistically analysed data on the average hourly wind speed obtained from the meteorological station Poprad (located at the Poprad-Tatry airport, the Prešov region, Northern Slovakia) for the period 2005–2021. High altitude and rough mountainous terrain influence the weather conditions considerably and are a source of occasional weather risks. Finding an appropriate wind speed distribution for modelling the wind speed data is therefore important to determine the wind profile at this particular location. In addition to the commonly used two- and three-parameter Weibull distribution, a more flexible exponentiated Weibull (EW) distribution was applied to model the wind speed. Based on the results of the goodness-of-fit criteria (the Kolmogorov–Smirnov test, the Anderson–Darling test, Akaike’s and Bayesian information criteria, the root mean square error, and the coefficient of determination), the EW distribution obtained a significantly better fit to seasonal and monthly wind speed data, especially around the peaks of the data. The EW distribution also proved to be a good model for data with high positive skewness. Therefore, we can recommend the EW distribution as a flexible distribution for modelling a dataset with extremely strong winds or outliers in the direction of the right tail. Alongside the wind speed analysis, we also provided the wind direction analysis, finding out that the most prevailing direction was west (W)—with an occurrence rate of 34.99%, and a mean wind speed of 3.91 m/s, whereas the northern (N) direction featured the lowest occurrence rate of only 4.45% and the mean wind speed of 1.99 m/s.

New Two-BWT Blade Aerodynamic Design and CFD Simulation

Due to reduced manufacturing, transportation, and installation costs, the two-blade wind turbines (Two-BWT) are a viable option for offshore wind farms. So far, there is no mature design model for offshore Two-BWT. This paper proposes an aerodynamic design method for offshore Two-BWT blades using the blade element momentum (BEM) theory. This method calculates the power coefficient of the Two-BWT by analogy with the three-blade wind turbines (Three-BWT), and then determines the wind rotor diameter. Then, the airfoil, chord length, and twist angle are taken as the key design factors. Furthermore, the piecewise combination method (PCM) for airfoil distribution, the three-point sine method (Three-PSM) for chord length distribution, and the two-point sine method (Two-PSM) for torsion angle distribution are adopted, respectively. Subsequently, the minimum rotational speed, under the rated wind speed and rated power, is taken as the optimization objective to establish the optimization model. The global flow field of Two-BWT is constructed based on CFD technology, and the characteristics of wind speed distribution and blade pressure distribution in the flow field are investigated. Finally, the CFD results are compared with the results of the BEM theory, and the consistency of the results also shows the feasibility of the design method.

Future Projections of EURO‐CORDEX Raw and Bias‐Corrected Daily Maximum Wind Speeds Over Scandinavia

AbstractTwenty historical and future Coordinated Regional Climate Downscaling Experiment ensemble for Europe simulations are bias corrected to investigate the future changes in the daily maximum wind speed over Scandinavia. We use quantile mapping to adjust the wind for the historical period (1985–2014) and quantile delta mapping for two future periods (2041–2070, 2071–2100, RCP8.5) with the 3‐km spatial resolution NORA3 hindcast as the reference data set. Decomposing the variance, we find that most of the inter‐model spread in the bias and the response to climate change is due to the regional climate model over land and mainly to the general climate model over sea. On average, the mean daily maximum wind speed is projected to increase everywhere except along the western coast of Norway and Denmark. In summer, we see an opposite sign over Sweden and Finland. The Norwegian Sea experiences weaker mean and high wind whereas the Baltic Sea experiences a strengthening. Bias correction influences the amplitude of the response, not the response pattern. Wind speed distributions can have different shapes in the future, with, for example, a flattening of the distribution off the coast of Norway with more frequent weak and very strong winds. Apart from a few locations in Norway, we find an increase in the number of days in the local highest historical wind category. Overall, summer exhibits opposite signals compared to the three other seasons. At country scale, the sign of the change in the mean and 98th percentile varies greatly depending on the region and among the simulations, especially for Norway.