Wind Speed Distribution Research Articles

Distributions and Correlation Properties of Offshore Wind Speeds and Wind Speed Increments

We determine distributions and correlation properties of offshore wind speeds and wind speed increments by analyzing wind data sampled with a resolution of one second for 20 months at different heights above sea level in the North Sea. Distributions of horizontal wind speeds can be fitted to Weibull distributions with shape and scale parameters varying weakly with the vertical height separation. Kullback–Leibler divergences between distributions at different heights change with the squared logarithm of the height ratio. Cross-correlations between time derivatives of wind speeds are long-term anticorrelated, and the even parts of their correlation functions satisfy sum rules. Distributions of horizontal wind speed increments change from a tent-like shape to a Gaussian with rising increment lag. A surprising peak occurs in the left tail of the increment distributions for lags in a range 10-200km\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$10-200\\,{km}$$\\end{document} after applying the Taylor’s hypothesis locally to transform time lags into distances. The peak is decisive in order to obtain an expected and observed linear scaling of third-order structure functions with distance. This suggests that it is an intrinsic feature of atmospheric turbulence.

Assessment of offshore wind conditions in coastal areas of Japan using single scanning Doppler LiDAR

Abstract In this study, offshore wind conditions in coastal areas of Japan measured by single scanning Doppler lidar is investigated. The effects of measurement range as well as rainfall and snowfall on data availability of the Doppler scanning lidar are examined. Data filtering criteria are then proposed and verified by data thinning to meet both accuracy and post-processed data availability requirements for offshore wind measurements at multiple altitudes. Finally, one-year offshore wind measurement with three different elevation angles is conducted using a single Doppler scanning lidar to investigate wind conditions in the coastal region of Northern Japan. It is found that the accuracy of 15-second average wind speed measurements by PPI (Plan Position Indicator) scan depends on the sector size. An accurate 10-minute mean wind is measured when the sector size is larger than 39 degrees and proportion of valid data acquisition is more than 10% of the time duration. The vertical and horizontal distributions of offshore wind speed in different wind directions are also analyzed and the effects of onshore topography on offshore wind conditions are clarified.

Collocating wind data: A case study on the verification of the CERRA dataset

Abstract Wind resource assessment often relies on reanalysis data or meso-scale models due to the challenges and costs associated with on-site measurement campaigns, especially in offshore locations. While these datasets offer extensive spatial coverage, they often provide the desired parameters on a coarse grid only. The spatial averaging over the grid and the low frequency of the outputs hinders the model’s ability to capture the short-term wind speed variability and events with a time scale of a few minutes to a few dozen of minutes. In this paper we investigate the temporal scales of the wind events resolved by the Copernicus European Regional Re-Analysis (CERRA) dataset, ERA5, and an in-house regional down-scaling of ERA5 using the WRF model by comparing them against measurements. We propose a method to collocate the two sources of wind data (measured and modeled), focusing on the verification of CERRA with respect to first and second-order statistics in the North Sea. The results highlight the superiority of CERRA compared to ERA5 in estimating the wind speed distribution, improving the 95th percentile error from -0.85 to -0.19 ms−1 and the average bias from -0.286 to -0.004 m s−1 across all locations. The wind speed change over one hour is underestimated by both CERRA and ERA5 when collocated with 10-minute measurements at the full hour. However, hourly averaged measurements align well with CERRA’s wind speed variation predictions, while an averaging window of 3.5 hours is needed to align with ERA5’s hourly wind speed changes. The results showed that the synthetic wind speeds exhibit optimal correlation with the measurements when the measurements are averaged over 4.5 to 7 hours, depending on the modeled dataset. This time scale corresponds to a length scale of 160-250 km, assuming a mean wind speed of 10 m s−1, which falls within the meso-scale range. The study contributes to the understanding of model validation in offshore wind energy studies, with the potential to enhance wind resource assessment calculations and improve the results of long-term extrapolations.

Optimal configuration of dynamic VAR compensators considering uncertainty and correlation

AbstractThe transient voltage stability of modern power systems is challenged by the increasing uncertainty on source‐load dual sides. To cope with it, a robust optimal configuration method for dynamic VAR compensators (DVCs) is proposed. First, a series of power‐flow scenarios are generated with uncertainty and correlation considered, where nonparametric kernel density estimation is used to predict the marginal distribution of wind speeds and combined Copula function is employed to characterize correlations between nearby wind farms. Then, the variation‐of‐information indicator is introduced to assess the similarity among power‐flow (PF) scenarios, and representative PF (RPF) scenarios are screened with the help of Spectral clustering algorithm. Finally, locating and sizing of DVCs for RPF scenarios are achieved. By synthesizing the compensation schemes for RPF scenarios, the robust configuration scheme is suggested. Simulation studies in the modified New England 10‐machine 39‐bus system show the proposed method can ensure transient voltage security of the studied power system under source‐load uncertainty.

Bayesian mixture model for accurate assessment of monthly maximum wind speed: A case study in Gwadar

Assessment of monthly maximum wind speed (MMWS) is essential in various environmental disciplines, including architectural risk assessment, climatology, renewable energy sources, building design, and agricultural activities. The distribution of wind speed is an important module of its development. Presently, several mixing distributions have been suggested for fitting the theoretical maximum wind speed distributions. However, the drawback of various distribution elements is the outcome of numerous fitting presentations, and it is challenging to choose the best form of single distribution to employ when building a hybrid model. In order to solve those difficulties, utilizing the mixtures of Sine-skewed-von Mises distributions (MSS-VM) with various prior parameter distributions is extended in the present study. In this research, the assessment of MMWS employing the maximum-likelihood estimation (MLE) method is taken into consideration for the wind speed data from the Pakistan province of Baluchistan Gwadar station. The results of the Anderson Darling (AD), Chi-square, BIC, and AIC tests, as well as the Kolmogorov Smirnov (KS) test, indicated that MSS-VM was the best distribution for the Gwadar station. The suggested models automatically find the optimum number of elements, in addition to having more appropriate performance. The outcomes demonstrate that MSS-VM performance is more suitable than other heterogeneous and single-distribution models. The design estimations derived using the Bayesian mixture distribution provide a sense of the highest wind speed that will occur across a particular area. It is therefore crucial for designers and policymakers in the planning, designing, and building of various structures.

New method to model the wind speed distribution: method of decile

Energy is a primary need for individuals and societies; the new era has revolutionized everything, from cell phones to daily used equipment in houses; all need electrical energy. Advanced countries have already managed energy demand according to their need. Renewable energy is better than conventional energy sources such as gas, coal, and nuclear fuel. Researchers have continuously worked on renewable energy to make it as helpful as possible. Mathematical modeling and new methods of harnessing energy are the leading fields researchers are working in. Wind energy is ubiquitous; however, it depends on a cut-off speed. In numerous places worldwide, the wind speed surpasses the cut-off speed. Such locations are good candidates for generating electricity from wind energy. Proper modeling of wind energy enables one to know available wind potential. An effort has been made to model wind speed; a new method known as the method of decile has been developed. The method works using the first and last decile of the wind distribution. The Weibull parameters, average wind speed, and errors have been calculated and compared to five other methods. Akaike information criterion (AIC) was used to see the suitability of the new method. The AIC values for the new method were calculated for 12 months of wind speed data in 2016; the minimum values occurred for the novel method. It shows that the new method is the best among the other five methods. The annual output energy is estimated using E-5 (by Ryse Energy company) horizontal axis wind turbine and evaluated as 6900 kWh at Hyderabad, the highest among the three sites.

The potential of mixing model of wind speed distribution in Algerian High Plateaus

The evaluation of wind energy relies primarily on the probability density function PDF, which corresponds well with the wind speed data. Single PDFs are widely used in the assessment of wind. In contrast, homogeneous or heterogeneous mixed models are rarely used, especially in Algeria, where the bimodal wind speed distribution is expected. This research aims to investigate the potential of heterogeneous PDFs Generalized Extreme Value-Weibul and Normal-Extreme Value PDFs in assessing wind energy at three meteorological stations in the high plateau against the single widespread PDFs Weibull and GEV by analyzing five years of archived wind speed data. The estimation of mixed model parameters is obtained by applying the Expectation-Maximization algorithm, and the identification of the appropriate PDF is obtained by four goodness-of-fit (GOF) criteria and compared with the widespread single distributions. The results show that the mixed model surpasses widely the single model for all the GOF criteria used at the three selected sites. The proposed mixed model fits all the wind speed distributions related to unimodal and bimodal regimes.

Multi-scale variability of southeastern Australian wind resources

Abstract. There is growing need to understand wind variability in various regions throughout the world, including in relation to wind energy resources. Here we examine wind variability in southeastern Australia in relation to the El Niño–Southern Oscillation (ENSO) as a dominant mode of atmospheric and oceanic variability for this region. The analysis covers variability from seasonal to diurnal timescales for both land and maritime regions of relevance to wind energy generation. Wind speeds were obtained from the 12 km grid length Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) reanalysis, with a focus on wind at a typical hub-height of 100 m above the surface. Results show spatiotemporal variations in how ENSO influences wind speeds, including consistency in these variations over the wind speed distribution. For example, ENSO-related variations in mean winds were mostly similar in sign to ENSO-related variations in weak winds, noting uncertainties for strong winds given available data. Diurnal variability in wind speed was larger for summer than winter and for land than ocean regions, with the diurnal cycle maxima typically occurring in the afternoon and evening rather than morning, plausibly associated with sensible heating of air above land following solar radiation. Localised variations in the diurnal cycle were identified around mountains and coastal regions. The results show some indication of ENSO influences on the diurnal variability. These findings are intended to help enhance scientific understanding on wind variability, including in relation to ENSO, and to contribute information towards practical guidance in planning such as for use in energy sector applications.

Study on Downscaling Correction of Near-Surface Wind Speed Grid Forecasts in Complex Terrain

Accurate forecasting of wind speeds is a crucial aspect of providing fine-scale professional meteorological services (such as wind energy generation and transportation operations etc.). This article utilizes CMA-MESO model forecast data and CARAS-SUR_1 km ground truth grid data from January, April, July, and October 2022, employing the random forest algorithm to establish and evaluate a downscaling correction model for near-surface 1 km resolution wind-speed grid forecast in the complex terrain area of northwestern Hebei Province. The results indicate that after downscaling correction, the spatial distribution of grid forecast wind speeds in the entire complex terrain study area becomes more refined, with spatial resolution improving from 3 km to 1 km, reflecting fine-scale terrain effects. The accuracy of the corrected wind speed forecast significantly improves compared to the original model, with forecast errors showing stability in both time and space. The mean bias decreases from 2.25 m/s to 0.02 m/s, and the root mean square error (RMSE) decreases from 3.26 m/s to 0.52 m/s. Forecast errors caused by complex terrain, forecast lead time, and seasonal factors are significantly reduced. In terms of wind speed categories, the correction significantly improves forecasts for wind speeds below 8 m/s, with RMSE decreasing from 2.02 m/s to 0.59 m/s. For wind speeds above 8 m/s, there is also a good correction effect, with RMSE decreasing from 2.20 m/s to 1.65 m/s. Selecting the analysis of the Zhangjiakou strong wind process on 26 April 2022, it was found that the downscaled corrected forecast wind speed is very close to the observed wind speed at the station and the ground truth grid points. The correction effect is particularly significant in areas affected by strong winds, such as the Bashang Plateau and valleys, which has significant reference value.

Study on Wind Farm Flow Field Characteristics Based on Boundary Condition Optimization of Complex Mountain Numerical Simulation

The accurate prediction of the flow field characteristics of complex mountains is of great practical significance for the development and construction of wind farms, but it is not yet fully understood. The main purpose of this study is to propose a method for the study of flow field characteristics under complex mountain conditions, which can optimize the boundary conditions required for numerical simulation through the wind acceleration ratio and, at the same time, couple the numerical simulation and wind measurement data to reflect the real mountain flow field distribution. The results show that the proposed method has good applicability in complex mountain wind farms, can reproduce the real flow field distribution, and has a certain practical value. Wind speed distribution and turbulence intensity are greatly affected by boundary conditions such as wind speed and wind direction and are also affected by the shielding effect brought by terrain changes. The contrast between 120° and 150° wind direction is more obvious. When the incoming wind moves to the top of a mountain or the ridgeline, it will form a low-speed wake area behind it, resulting in reduced wind speed, increased turbulence intensity, and an unstable flow field.

Murmansk Marine Biological Institute of Kola Scientific Center of the Russian Academy of Sciences

The authors assess the wind potential indicators and the scale of its use for power supply of hard-to-reach off-grid settlements in the eastern Russian Arctic. The primary challenge facing autonomous power plants is fuel delivery due to long distances and underdeveloped transport infrastructure. The use of wind power plants will reduce fuel consumption and cut the cost of energy production. The researchers use ground-based measurement data and the characteristics of existing autonomous power plants as initial data when assessing the rational capacity of wind power plants. They employ authoring research methods. Via Wind-MCA program they estimate wind potential indicators: average wind speed, capacity factor, and coefficient of wind speed variation. The research results are presented in cartographic form. The rational capacities of wind power plants as part of hybrid energy systems are determined using the generalized dependencies method based on the maximum load of a populated area and calculated wind potential indicators, taking into account the characteristic intra-annual distribution of wind speeds. The best conditions for the development of wind energy are observed on the coast of the Arctic seas in the Krasnoyarsk Territory and the Chukot Autonomous Area. The rational capacity of wind power plants in zones of high wind potential in hard-to-reach areas of eastern Russian Arctic is estimated at 12,5 MW.

Practical method for evaluating wind influence on autonomous ship operations (2nd report)

AbstractRecently, a considerable number of research and development projects have focused on automatic vessels. A highly realistic simulator is needed to validate control algorithms for autonomous vessels. For instance, when considering the automatic berthing/unberthing of a vessel, the effect of wind in such low-speed operations cannot be ignored because of the low rudder performance during slow harbor maneuvers. Therefore, a simulator used to validate an automatic berthing/unberthing control algorithm should be able to reproduce the time histories of wind speed and wind direction realistically. Therefore, in our first report on this topic, to obtain the wind speed distribution, we proposed a simple algorithm to generate the time series and distribution of wind speed only from the mean wind speed. However, for wind direction, the spectral distribution could not be determined based on our literature surveys, and hence, a simple method for estimating the coefficients of the stochastic differential equation (SDE) could not be proposed. In this study, we propose a new methodology for generating the time history of wind direction based on the results of Kuwajima et al.’s work. They proposed a regression equation of the standard deviation of wind direction variation for the mean wind speed. In this study, we assumed that the wind direction distribution can be represented by a linear filter as in our previous paper, and its coefficients are derived from Kuwajima’s proposed equation. Then, as in the previous report, the time series of wind speed and wind direction can be calculated easily by analytically solving the one-dimensional SDE. The joint probability density functions of wind speed and wind direction obtained by computing them independently agree well with the measurement results.

Analysis of the Eddy current of water heating device to convert wind energy directly into heat: Case study maldo island, South Korea

The Eddy Current of Water Heating (ECWH) system introduces a pioneering approach for converting wind energy into heat, marking a significant step in renewable energy technology. The current study focuses on refining the ECWH system by evaluating eight distinct heat generator models. Each model is selected based on its magnet configurations and pole numbers to maximize power efficiency at rotational speeds ranging from 100 to 500 rpm. This selection strategy is designed to provide a deep understanding of not only system efficiency across a spectrum of technical specifications but also the practical limitations of experimental research considerations. Energy efficiency is thoroughly analyzed for four models over a power input range of 120 to 2193 W. Wind speed data collected over a year from Maldo Island, west coast of South Korea, simulates the Weibull distribution of wind speeds and estimates total power production by employing the QBlade software. This study aims to explore the practicality of converting wind's kinetic energy directly into thermal energy through the ECWH system. Our findings indicate that the ECWH system outperforms conventional permanent magnet alternators, particularly in its ability to generate more power at lower angular velocities and wind speeds—a crucial feature for locales like Maldo Island, where typical wind speeds range from 3 to 6 m/s, considered low to moderate. With an energy efficiency exceeding 90%, generating an annual power output of 2266.3 kWh, and achieving a facility utilization rate of 10.34%, the results underscore the potential of the ECWH system as an efficient and sustainable solution for heating in areas with similar wind conditions.

Developing a methodology for user‐oriented verification of polar low forecasts

AbstractPolar lows exhibit features with very sharp weather contrasts. In weather forecasting, a small misplacement of areas with hazardously high wind speeds can have fatal impacts for people living in polar regions. Therefore, a novel application of spatial verification methods for objective metrics of size, shape, and location of areas with hazardous weather is tested. To separate the effect of errors in polar low location and direction of motion from errors relative to the polar low centre, surface wind fields from the limited‐area weather forecasting model Applications of Research to Operations at Mesoscale‐Arctic and Copernicus Climate Change Service Arctic Regional Reanalysis are centred at the polar low centre and rotated according to the direction of background flow surrounding the polar low. Then the possibilities of the features‐based verification methods SAL (structure, amplitude, location) and MODE (Method for Object‐based Diagnostic Evaluation) are explored using a test case from October 2019. The study demonstrates that the methodology can provide valuable information about forecast performance. MODE is a flexible method with metrics that focus on characteristics of individual objects and can be adapted to questions at hand. For example, a measure of storm eye size was added. SAL, on the other hand, provides effective summary metrics for the full domain and proved particularly useful for evaluation of the overall distribution of wind speed. To evaluate the number of correctly or incorrectly identified areas with harsh weather rather than their details about their shape, contingency scores are more suitable. Applied to a larger dataset, this methodology can assess performance as a function of forecast length, as well as geographical area, and the type of polar low. The methodology can also be applied to other types of low‐pressure systems, such as extratropical cyclones.

Near‐surface wind profiles from numerical model predictions. Part II: Verifications against Australia‐wide surface wind observations

AbstractThe new scheme for deriving the near‐surface wind profiles discussed in Ma (in review) is applied to an Australian Bureau of Meteorology operational convective scale model over various domains. Both the new and conventional schemes' diagnostic 10‐m winds are then verified against Australia‐wide automatic weather station observations. Analyses of bulk statistics reveal that the new scheme's 10‐m wind forecasts have generally better accuracy than the current conventional scheme with a consistent reduction of biases over all domains. A widely recognised diurnal bias pattern of surface wind speed over the land is substantially reduced, and the inclusion of Ekman spiral effect on the 10‐m wind marginally improves statistics of the wind direction during the nighttime. The new scheme introduces no systemic bias, given the histogram of a bulk mean bias is analogiased to a Gaussian distribution, and moves the distribution of diagnostic wind speed closer to that observed.

Joint probabilistic modeling of extreme wind-wave conditions under typhoon impact and applications to extreme response analysis of floating offshore wind turbines

The joint probabilistic modeling of extreme wind and wave conditions under typhoon impact is a critical task for extreme response analysis of floating offshore wind turbines (FOWTs) deployed in typhoon-prone areas. However, wind and wave observations under typhoon impact are quite limited, which poses great challenges to the probabilistic modeling task. Although some sophisticated programs can be employed to simulate typhoon-induced wave fields, they suffer from unbearable computational burdens. To improve the accuracy and efficiency of joint probabilistic modeling of extreme wind and wave conditions under typhoon impact, an approach that synthesizes the typhoon hazard analysis method and copula-based conditional modeling method is proposed in the present study. The proposed joint distribution model is expressed as the product of the distribution of extreme wind speed and the conditional distribution of significant wave height. The distribution of extreme wind speed is estimated through the typhoon hazard analysis method, and the conditional distribution of significant wave height is modeled by the copula-based method. The effectiveness of the proposed method is verified by comparing with observation data. Applications of the proposed method to extreme response analysis of FOWTs are illustrated as well.

Statistical Analysis of Wind Resource Assessment for Different Locations in South-Western Thailand

Weibull parameters have been widely used to evaluate wind energy potential. In this work presents wind resource assessment by statistical analysis with a Weibull distribution model for Krabi, Phuket, and Ranong weather stations in south-western Thailand. Ten-minute intervals include wind speed and wind direction of 10m from four-year records obtained by the Thai meteorological department. Four numerical methods, namely empirical method, graphical method, energy pattern factor method and maximum likelihood method are examined to estimate the Weibull parameters. The Weibull distribution obtained from each method is compared with the observed wind speed distribution by the performance tests using root mean square error, mean percentage error, and chi-square error to select a suitable method for the station area. The results revealed that the maximum likelihood method was the most accurate for Krabi and Phuket stations, and the energy pattern factor method was the most accurate for Ranong station. At a hub height of 80m, the highest mean wind speed and mean wind power density found in Krabi station were 3.25 m/s and 44.84 W/m2. The most probable wind speed value in three stations had a range from 1.80 to 2.50 m/s. The maximum wind speed carrying maximum energy found in Krabi station was 5.53 m/s. The operating probability of a wind turbine in Krabi station was 49.61%, followed by Phuket station was 46.80%, and Ranong station was 37.84%, respectively. In conclusion, all three stations had wind power potential classified as wind class 1 and can be sorted as follows: Krabi, Phuket, and Ranong stations.

Wind resource assessment for turbine class identification in Bayanzhaganxiang, China

Abstract The wind resource assessment has been used effectively to identify the classification of wind turbines at a particular wind farm site. The current study used WAsP software and various statistical methods such as graphical, energy pattern factor, standard deviation, and Rayleigh distribution methods to find the Weibull parameters by evaluating the raw data collected from August 2005 until July 2006 at four (4) different heights of the meteorological mast station in Bayanzhaganxiang, China. The Weibull parameters were utilized to find the annual mean wind speed, probability density, and cumulative distribution functions of wind conditions at the reference heights of 70 m, 50 m, 30 m, and 10 m. The wind shear coefficient was 0.130 with an overall roughness factor of 0.0385 m, suggesting the site vicinity is an open country with no significant structures and vegetation. The results also showed that the post-processed output from WAsP and standard deviation method at the sensor’s height of 70 m have a correlation coefficient and confidence level of 0.99977 and above 95%, respectively. Based on the turbine classification from GL Wind 2003 and IEC 61400-1 Ed.2, it was found that the turbine class ideal for the site is class III wind turbines with an annual mean wind speed of 7.439 m/s at a hub height of 99 m. The measured wind power density at hub height was calculated according to IEC 61400-12-1, which yields 464.36 W/m2. The characteristic wind turbulence at 70 m high is IEC subclass B. Among the selected wind turbines, the net annual energy production with efficiency is 8,059.57 MWh/year using Avantis AV1010, with the highest capacity factor of 40.05%. It has been found that the lowest energy generation cost is US$ 0.0292/kWh for a period of 20 years.

Study on the Influence of Some Ventilation Parameters on Dust Dispersion in Heading Face Coal Mine Using CFD Numerical Model

Coal dust is one of the environmental factors that seriously affect the health of workers as well as the mining equipment in underground coal mines. At present, coal dust is commonly generated during drilling, blasting, excavation, and transportation processes in mining operations. During mining blasting processes, coal dust is generated with varying particle sizes and high concentration levels. High concentrations of dust will affect mining operations and increase the ventilation time required for mining faces. In addition, coal dust exists in suspended form in the roadway and is harmful to human health, especially fine dust particles that have a negative impact on work efficiency. To improve ventilation efficiency and eliminate coal dust, this article presents a CFD-DPM numerical modeling method that integrates a DEM collision model based on the finite element method to analyze the motion characteristics of airflow and dust particles in the mine tunnel, while considering collisions between particles and between particles and walls. The article analyzes the distribution of wind speed, the dispersion of dust in the space around the roadway, and dust concentrations at distances of 1 m, 3 m, and 6 m from the working personnel and at a position 1.5 m above the roadway floor, corresponding to the breathing zone of the workers, with varying parameters such as velocity and duct position. The results indicate that with a wind velocity of V = 18 m/s and an air duct height h = 3.0 m, the best dust reduction results are achieved, and they provide theoretical guidance for selecting and optimizing ventilation parameters in dust control.

Characterization of the offshore wind dynamics for wind energy production in the Gulf of Lion, Western Mediterranean Sea

A ground-based wind lidar system has been deployed on a coastal landmass off Marseille in the Gulf of Lion, western Mediterranean Sea. This location holds significant relevance as it anticipates the imminent deployment of floating wind farms. Over the course of one year, a comprehensive wind dataset was collected by a lidar profiler at altitudes ranging from 60 to 220 m above sea level, aiming to assess the wind resource potential. This study presents an analysis of monthly wind metrics, including key parameters such as wind speed, wind shear exponent, and turbulence intensity. Weibull distributions are provided, along with their scaling and shaping parameters, to elucidate the probability distribution of wind speeds. The focus extends to the diurnal variation of wind metrics and monthly wind speed vertical profiles. Our results indicate that, at an altitude of 140 m above sea level, the one-year average wind speed is 8.3 m/s, with a turbulence intensity (TI) of 13.0 %. The mean wind shear exponent is quantified at 0.077. Notably, the highest wind speeds are consistently observed between 12:00 and 15:00, coinciding with lower values of TI and wind shear exponent, while night-time reveals high levels of both TI and wind shear exponent. Furthermore, an examination of wind direction unveils a bi-modal pattern with a slight variation, specifically wind veer, at each measurement height relative to the 140 m altitude. Additionally, our study explores low-level jets (LLJs), revealing that 80 % of these phenomena occur at altitudes below 140 m, predominantly during the summer period. The mean LLJ core speed, averaged across all measurement heights, is found to be 8.4 m/s.