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Estimating parameters for multiple datasets can be time consuming, especially when the number of datasets is large. One solution is to sample from multiple datasets simultaneously using Bayesian methods such as adaptive multiple importance sampling (AMIS). Here, we use the AMIS approach to fit a von Mises distribution to multiple datasets for wind trajectories derived from a Lagrangian Particle Dispersion Model driven from 3D meteorological data. A posterior distribution of parameters can help to characterise the uncertainties in wind trajectories in a form that can be used as inputs for predictive models of wind-dispersed insect pests and the pathogens of agricultural crops for use in evaluating risk and in planning mitigation actions. The novelty of our study is in testing the performance of the method on a very large number of datasets (>11,000). Our results show that AMIS can significantly improve the efficiency of parameter inference for multiple datasets.

Over several decades, high-frequency (HF) radars have been employed for remotely measuring various ocean surface parameters, encompassing surface currents, waves, and winds. Wind direction and speed are usually estimated from both first-order and second-order Bragg-resonant scatter from two or more HF radars monitoring the same area of the ocean surface. This limits the observational domain to the common area where second-order scatter is available from at least two radars. Here, we propose to estimate wind direction and speed from the first-order scatter of a single HF radar, yielding the same spatial coverage as for surface radial currents. Wind direction is estimated using the ratio of the positive and negative first-order Bragg peaks intensity, with a new simple algorithm to remove the left/right directional ambiguity from a single HF radar. Wind speed is estimated from wind direction and de-tided surface radial currents using an artificial neural network which has been trained with in situ wind speed observations. Radar-derived wind estimations are compared with in situ observations in the Lower Saint-Lawrence Estuary (Quebec, Canada). The correlation coefficients between radar-estimated and in situ wind directions range from 0.84 to 0.95 for Wellen Radars (WERAs) and from 0.79 to 0.97 for Coastal Ocean Dynamics Applications Radars (CODARs), while the root mean square differences range from 8° to 12° for WERAs and from 10° to 19° for CODARs. Correlation coefficients between the radar-estimated and the in situ wind speeds range from 0.89 to 0.93 for WERAs and from 0.81 to 0.93 for CODARs, while the root mean square differences range from 1.3 m.s−1 to 2.3 m.s−1 for WERAs and from 1.6 m.s−1 to 3.9 m.s−1 for CODARs.

The joint probability density function of wind speed and wind direction serves as the mathematical basis for directional wind energy assessment. In this study, a nonparametric joint probability estimation system for wind velocity and direction based on copulas is proposed and empirically investigated in Inner Mongolia, China. Optimal bandwidth algorithms and transformation techniques are used to determine the nonparametric copula method. Various parameter copula models and models without considering dependency relationships are introduced and compared with this approach. The results indicate a significant advantage of employing the nonparametric copula model for fitting joint probability distributions of both wind speed and wind direction, as well as conducting correlation analyses. By utilizing the proposed KDE-COP-CV model, it becomes possible to accurately and reliably analyze how wind power density fluctuates in relation to wind direction. This study reveals the researched region possesses abundant wind resources, with the highest wind power density being highly dependent on wind direction at maximum speeds. Wind resources in selected regions of Inner Mongolia are predominantly concentrated in the northwest and west directions. These findings can contribute to improving the accuracy of micro-siting for wind farms, as well as optimizing the design and capacity of wind turbine generators.

South Africa is currently grappling with a national energy crisis and the high infrastructure costs associated with expanding the national grid to remote areas. Simultaneously, the government has made substantial efforts to harness renewable energy technologies, particularly wind energy. The average wind speed in a specific region significantly influences the energy yield from wind turbines. The vast open inland terrains, mountainous regions, and coastal areas in the Northern Cape, Eastern Cape, and Western Cape provinces of South Africa possess the most substantial wind potential. It is imperative to initiate wind energy projects in these provinces to cater to a significant portion of the local electricity demand, especially in remote areas disconnected from the national grid. Wind energy generation is inherently stochastic, subject to variations in both time and space. Consequently, it is essential to gain a comprehensive understanding of the local wind patterns to assess the feasibility of utilizing wind resources. In the Eastern Cape Province, the Mthatha area still lags in household electrification, presenting an opportunity to electrify some households using wind energy. This study aimed to evaluate the wind resource potential for Mthatha area, utilizing data spanning from 2018 to 2023, provided by the South African Weather Services. Two distribution models, the two-parameter Weibull and three-parameter Weibull, were employed to characterize the provided wind data. To determine the parameters associated with each distribution model, two estimation methods, the Maximum Likelihood Method (MLM) and the Method of Moments (MOM), were utilized. The performance of these distribution models was assessed using the Root Mean Square Error (RMSE) statistical indicator. The results showed that Mthatha area predominantly experiences low wind speeds, with an annual average wind speed of 3.30 m/s and an overall wind power density of approximately 48.48 W/m2. The prevailing winds predominantly originate from the south and east–southeast directions. Consequently, Mthatha is recommended for stand-alone applications, with the added suggestion of augmented wind turbines for the area.

In this work, the ERA5 reanalysis dataset, with its fine spatial and temporal resolution, is used to study the wind speed and direction characteristics in the Mediterranean basin from 1979 to 2020. Their variability, trend and mean values, as well as their association, are examined in the monthly, annual and interannual/decadal time scales. The long-term variability of the wind direction is assessed using the angular variance, while the mean annual and interannual variability are used for the wind speed. The most characteristic and constant flow systems appear in the Gulf of Lion (Mistral) and the Aegean Sea during the summer (Etesian winds). The mean annual variability maximizes in the northern part of the basin, while the western part appears to have the most variable wind directions, while the fastest increasing wind speeds appear in the southwestern Levantine sub-basin. The long-term linear trend for the mean, the 95th and 99th quantiles of annual wind speed and the number of occurrences of extreme wind events have been also assessed. For particular areas, there is an increasing tendency in both intensity and frequency of occurrence of extreme wind events. Τhe linear-circular correlation coefficient has been implemented in order to quantify the linear association between the wind direction and the wind speed. This parameter demonstrates higher annual values for the Mistral wind in the Gulf of Lion and the Etesian winds in the Aegean Sea during the summer. Finally, the comparison of the results of ERA5 with the results of ERA-Interim highlighted significant differences in the Mediterranean wind speed and direction characteristics.

Wind forecasting, which is essential for numerous services and safety, has significantly improved in accuracy due to machine learning advancements. This study reviews 23 articles from 1983 to 2023 on machine learning for wind speed and direction nowcasting. The wind prediction ranged from 1 min to 1 week, with more articles at lower temporal resolutions. Most works employed neural networks, focusing recently on deep learning models. Among the reported performance metrics, the most prevalent were mean absolute error, mean squared error, and mean absolute percentage error. Considering these metrics, the mean performance of the examined works was 0.56 m/s, 1.10 m/s, and 6.72%, respectively. The results underscore the novel effectiveness of machine learning in predicting wind conditions using high-resolution time data and demonstrated that deep learning models surpassed traditional methods, improving the accuracy of wind speed and direction forecasts. Moreover, it was found that the inclusion of non-wind weather variables does not benefit the model’s overall performance. Further studies are recommended to predict both wind speed and direction using diverse spatial data points, and high-resolution data are recommended along with the usage of deep learning models.

The double vertical-axis wind turbine (VAWT) system serves as a high-performance design solution. The Nautilus wind turbine investigated in this research imitated the structure of a Nautilus shell and is a vertical-axis drag-type wind turbine that exhibits relatively low efficiency. Therefore, the improvement of its wind energy efficiency is of paramount importance. This paper utilizes Computational Fluid Dynamics (CFD) software, based on the Reynolds-averaged Navier–Stokes equations and dynamic meshing techniques, to conduct numerical investigations on the aerodynamic performance of the Nautilus wind turbine array layout. The effects of wind direction, spacing ratio, and rotation direction are individually studied, and the interpretations and explanations are provided based on flow field characteristics. The results show that when the wind direction is 90°, i.e., a transverse layout, the closer the spacing between the transverse turbines, the higher the average power coefficient of the entire wind turbine system, with little effect from the three rotation directions. The maximum average power coefficient reached 28.9% and the power gain factor (TPGF) reached 11.1%. The enhancement effect primarily originates from the wake interaction among neighboring turbines. The experimental results showed a deviation of 8.1% compared to the CFD simulation results, thus validating the accuracy of the numerical CFD modeling. Ultimately, several array layouts are proposed, based on the prevalent wind direction and spacing ratio research. The enhancement of the wind turbine array’s situation could significantly increase the average efficiency of the entire wind turbine cluster. Consequently, this study provides a reference for the practical application of biomimetic vertical-axis drag-type wind turbine systems in actual engineering.

This paper presents a novel algorithm based on the attenuation horizontal component for wind direction retrieval from X-band marine radar images. The range dependence of radar return on the ocean surface can be presented in radar images, and the radar return decreases with the increase in range. The traditional curve-fitting method averages the radar return of the whole range to retrieve the wind direction, but it is vulnerable to the interference of fixed objects and long-range low-intensity pixel points. For the pixels with the same range in the polar coordinates of the radar image, the ideal range attenuation model is derived by selecting the pixels with the highest intensity value. The ideal attenuation model is used to fit the attenuation data and calculate the attenuation horizontal component at each azimuth direction. To eliminate the effect of outliers, the iterative optimization method is used in the estimation of the attenuation horizontal component and the weights of the data are continuously updated. Finally, the wind direction is determined based on the azimuthal dependence of the attenuation horizontal component. This algorithm was tested using shipboard radar images and anemometer data collected in the East China Sea. The results show that, compared with the single curve-fitting method, the proposed algorithm can improve the wind direction retrieval accuracy in the case of more fixed targets. Under the condition of more fixed targets, the deviation and root mean square error are reduced by 16.3° and 16.2°, respectively.

The objective of this work is to assess the wind resources of the east coast of Maranhão, Brazil. Wind profilers were combined with micrometeorological towers and atmospheric reanalysis to investigate micro- and mesoscale aspects of wind variability. Field campaigns recorded winds in the dry and wet seasons, under the influence of the Intertropical Convergence Zone. The dry season was characterized by strong winds (8 to 12 m s−1) from the northeast. Surface heat fluxes were generally positive (250 to 320 W m−2) at midday and negative (−10 to −20 W m−2) during the night. Convective profiles predominated near the beach, with strongly stable conditions rarely occurring before sunrise. Further inland, convective to strongly convective profiles occurred during the day, and neutral to strongly stable profiles at night. Wind speeds decreased during the rainy season (4 to 8 m s−1), with increasingly easterly and southeasterly components. Cloud cover and precipitation reduced midday heat fluxes (77 W m−2). Profiles were convective during midday and stable to strongly stable at night. Terrain roughness increased with distance from the ocean ranging from smooth surfaces (zo = 0.95 mm) and rough pastures (zo = 15.33 mm) to crops and bushes (zo = 52.68 mm), and trees and small buildings (zo = 246.46 mm) farther inland. Seasonal variations of the mean flow and sea and land breezes produced distinct diurnal patterns of wind speeds. The strongest (weakest) breeze amplitudes were observed in the dry (rainy) period. Daily changes in heat fluxes and fetch over land controlled the characteristics of wind profiles. During sea breezes, winds approached the coast at right angles, resulting in shorter fetches over land that maintained or enhanced oceanic convective conditions. During land breezes, winds blew from the mainland or with acute angles against the coastline, resulting in large fetches with nighttime surface cooling, generating strongly stable profiles. Coastal observations demonstrated that with increasing monopiles from 100 to 130 m it is possible to obtain similar capacity factors of beachfront turbines.

The new sea surface wind direction from the X-band marine radar image is proposed in this study using a fast convergent gray-level co-occurrence matrix (FC-GLCM) algorithm. First, the radar image is sampled directly without the need for interpolation due to the algorithm’s application of the GLCM to the polar co-ordinate system, which reduces the inaccuracy caused by image transformation. An additional process is then to merge the fast convergence method with the optimized GLCM so that the circular transition between rough and fine estimates is acquired, resulting in the fast convergence and accuracy improvement of the GLCM. Furthermore, the algorithm will affect the GLCM spatial distribution while calculating it, and it can automatically resolve the 180° ambiguity problem of sea surface wind direction retrieved from radar images. Finally, the proposed method is applied to 1436 X-band marine radar sequences collected from the coast of the East China Sea. Compared with in situ anemometer data, the correlation coefficient is as high as 0.9268, and the RMSE is 4.9867°. The new method was also tested under diverse sea conditions. The FC-GLCM wind direction results against the adaptive reduced method (ARM), energy spectrum method (ESM), and the traditional GLCM (T-GLCM) method produced the best stability and accuracy, in which the RMSE decreased by 91.6%, 67.7%, and 18.1%, respectively.

In this article, we present the results of astronomical seeing measurements conducted at the Maidanak observatory and the influence of meteorological parameters on astronomical seeing. A differential image motion monitor (DIMM) was used to measure astronomical seeing. Astronomical seeing observations were made between 2018–2022, mainly in the summer and autumn months. A total of 266 night observations were conducted between 2018 and 2022. For the whole time frame, the median seeing value was 0.69 arc seconds. With a median value of 0.65″, 2019 is the year with the best seeing, and 2021 has the worst seeing (0.71″). Analysis of long-term variations of the amount of clear time revealed a small trend of decrease in the amount. The correlation between the meteorological parameters and seeing was analyzed. It was determined that the meteorological parameters with the greatest impact on the seeing are wind speed and nighttime temperature variation. The correlation coefficient was equal to 13% and 21% for both quantities. It was found that wind direction and temperature have a relatively weak correlation with astronomical seeing.

The aim of the study was to determine the content of heavy metals (Cr, Cu, Ni, Pb, and Zn) in the soil near Road No. 7, near Chyżne. Soil samples were collected in 12 transects (locations) on the east (downwind) and west (upwind) sides of the road. The samples were taken at a distance of: 5, 50, 100, 300, 500, and 600 m from the edge of the road. Six transects were located in the open area, and another six in the forest. The zinc content in the soil was the highest while that of copper was the lowest. Cr, Cu, Ni, Pb, and Zn concentrations decreased with increasing distance from the road. Differences in the concentration of metals in the soil on the upwind and downwind sides were found. The contents of all tested metals in the soil were higher on the downwind side of the road compared to the concentrations of metals on the upwind side.

Mean wind profiles within a unit-aspect-ratio street canyon have been estimated by solving the three-dimensional Poisson equation for a set of discrete vortex sheets. The validity of this approach, which assumes inviscid vortex dynamics away from boundaries and a small nonlinear contribution to the growth of turbulent fluctuations, is tested for a series of idealised and realistic flows. In this paper, the effects of urban geometry on accuracy are examined with neutral flow over shallow, deep, asymmetric and realistic canyons, while thermal effects are investigated for a single street canyon and both bottom cooling and heating. The estimated mean profiles of the streamwise and spanwise velocity components show good agreement with reference profiles obtained from the large-eddy simulation: the canyon-averaged errors (e.g., normalised absolute errors around 1%) are of the same order of magnitude as those for the unit-aspect-ratio street canyon. It is argued that the approach generalises to more realistic flows because strong spatial localisation of the vorticity field is preserved. This work may be applied to high-resolution modelling of winds and pollutants, for which mean wind profiles are required, and fast statistical modelling, for which physically-based estimates can serve as initial guesses or substitutes for analytical models.

The extreme significant wave height predictions often neglect within-year wave climate variability and wave directionality. Depending on a geographical region, local wind patterns and year climate variability could have an influence on the long-term prediction of waves. The Adriatic Sea having two dominant wind patterns of different characteristics, Bura and Jugo, is a great example for the case study. The 23-year hindcast wave data used in the presented study is extracted from the WorldWaves database. Based on wind and wave data, annual extreme significant wave heights generated by different wind patterns and for different months are fitted by Gumbel distribution using maximum likelihood estimation. Combined long-term extremes are then predicted by calculating system probability. It was found that considering the wave directionality, and especially the seasonality of wave climate, leads to a larger prediction of extreme significant wave heights. The extreme value prediction considering wave directionality on average yields 4% larger significant wave heights, while considering within-year climate variability leads to, on average, 8% larger extremes compared to the predictions when both effects are neglected.

Deep learning methods have excellent prospects for application in wave forecasting research. This study employed the convolutional LSTM (ConvLSTM) algorithm to predict the South China Sea (SCS) significant wave height (SWH). Three prediction models were established to investigate the influences of setting different parameters and using multiple training data on the forecasting effects. Compared with the SWH data from the China–France Ocean Satellite (CFOSAT), the SWH of WAVEWATCH III (WWIII) from the pacific islands ocean observing system are accurate enough to be used as training data for the ConvLSTM-based SWH prediction model. Model A was preliminarily established by only using the SWH from WWIII as the training data, and 20 sensitivity experiments were carried out to investigate the influences of different parameter settings on the forecasting effect of Model A. The experimental results showed that Model A has the best forecasting effect when using three years of training data and three hourly input data. With the same parameter settings as the best prediction performance Model A, Model B and C were also established by using more different training data. Model B used the wind shear velocity and SWH as training and input data. When making a 24-h SWH forecast, compared with Model A, the root mean square error (RMSE) of Model B is decreased by 17.6%, the correlation coefficient (CC) is increased by 2.90%, and the mean absolute percentage error (MAPE) is reduced by 12.2%. Model C used the SWH, wind shear velocity, wind and wave direction as training and input data. When making a 24-h SWH forecast, compared with Model A, the RMSE of Model C decreased by 19.0%, the CC increased by 2.65%, and the MAPE decreased by 14.8%. As the performance of the ConvLSTM-based prediction model mainly rely on the SWH training data. All the ConvLSTM-based prediction models show a greater RMSE in the nearshore area than that in the deep area of SCS and also show a greater RMSE during the period of typhoon transit than that without typhoon. Considering the wind shear velocity, wind, and wave direction also used as training data will improve the performance of SWH prediction.

This paper evaluates the feasibility of using wind power for power supply to coastal communities isolated from the main supply grid. The case study is Stewart Island, where the cost of electricity provided by a central diesel power station is higher than the grid network in New Zealand. The Princeton Ocean Model (POM) conducted by MetOcean Solutions Limited (MSL) is used to find Foveaux as an optimized site for generating wind power. Global Wind Atlas is used to plot the wind rose of current wind patterns in New Zealand. In the next step, wind speed data from each site are imported from the NASA database to WRPLOT view software and Homer Pro to find wind frequency distribution and output power in the area. The maximum annual power can be seen in WSW (32,299 kW hours/year), SW (20,111 kW hours/year) and W (15,622 kW hour/year) directions, respectively.

Canada goldenrod (Solidago canadensis L.) is a serious invasive alien plant species that exerts negative effects on natural and agricultural ecosystems in China. Few studies have addressed the dispersal of S. canadensis to explain how it rapidly spreads to large areas over long distances. Here, we quantify the dispersal of S. canadensis via wind by capturing in situ−stained diaspores. The diaspores were trapped and counted along 11 radiating transects from the center of a dispersal source. Solidago canadensis diaspores could be dispersed in all directions from the source, traveling longer distances and in greater amounts in the downwind direction than the upwind one. With a source including about 58 million diaspores and a wind speed at Beaufort scale 4, the dispersal distance in the prevailing wind direction (PWD) was at least 2000 m. Diaspores shattered at a rate of approximately 3% daily with the common wind speed of scale 4, indicating that dispersal could last for more than a month. A mechanistic model was used to fit the dispersal curve along the PWD. Although the model slightly underestimated long−distance dispersal, it still demonstrated the potential of long−distance dispersal with great source strength. Wind−dispersed diaspores to new areas persisted over winter and were able to form new plants at a density of about 2 plants per m2 at 500 m away from the source. Further experiments showed that the dispersed amount of S. canadensis diaspores was significantly positively correlated with the temperature and wind speed, but significantly negatively correlated with relative humidity, which indicated that, during a day, the maximum dispersal usually occurred in the afternoon when the temperature was the highest and the relative humidity the lowest. In addition, for an already existent population patch, the patch range can expand 2−4 m per year, mainly depending on the seedlings recruited from the rhizomes. These results provide insights into the long−distance dispersal of S. canadensis by wind and its effects on the range expansion process.

The numerical modeling of the effect of wind direction and velocity over the air cooling of PV panels with heat sinks is realized. During the study, a random PV panel with typical characteristics was analyzed for three different wind directions—towards its back, towards its front and from the side. The analysis was realized on a fixed PV panel, oriented to the south, with an inclination of 45 degrees from the horizontal position. The accuracy of the numerical simulation was achieved by comparison with the experimental studies presented in the literature and by comparing the NOCT conditions. The numerical study is focused on different types of heat sinks attached to a typical PV panel. The fins were distributed both horizontally and vertically. A challenging task consisted in simulation of the real wind conditions around the PV panel by taking into account the entire air domain. The simulations were realized for air velocity vair from 1 m/s to 5 m/s, solar radiation of G = 1000 W/m2 and ambient temperature tair = 35 °C. The output parameters analyzed were the average temperature of PV panels and their power production. Although the lowest temperatures were achieved for the back wind, the cooling effect was more intense for the side wind. The other direction studied also determined the cooling of PV panels. The passive cooling solutions analyzed introduced a rise of maximum power production between 1.85% and 7.71% above the base case, depending on the wind direction and velocity.

Previous studies show that the largest wind-induced response of a square section fixed-base high-rise building occurs when the strong wind is blowing perpendicular onto a building face, and the greatest translational response is likely to occur in the crosswind direction. When it comes to a square section base-isolated high-rise building that allows the isolation system to yield under strong wind excitation, the inelastic response shows distinctive non-Gaussian characteristics under fluctuating wind excitation and mean drift phenomenon under non-zero mean wind load. These characteristics may lead to a quite different result when determining the most unfavorable wind direction. Thus, the influence of wind direction on the inelastic response of a square base-isolated high-rise building is discussed in this study based on synchronous pressure measurement. The multi-story superstructure is modeled as a linear elastic shear building, while the isolation system is represented in a bilinear hysteresis restoring force model. The peak value of the inelastic response is estimated through a moment-based Hermit model from an underlying standard Gaussian process. The results show that when the strong wind blows perpendicular onto a building face, the greatest inelastic displacement, both at the top and isolation level, occurs in the along-wind direction, which is different from the elastic response. With the change of wind direction, the largest combined inelastic displacement still occurs when the wind inclination angle is 0°, while the combined displacement in other directions is also very large, which is worthy of concern.

Exterior louver blinds are one of the most common shading devices attached to the building façade, which will not only screen solar radiation but also affect the flow around the building and change the convective heat exchange at the façade. To better understand how different wind directions affect the convective heat properties of louvered building façades, this paper conducted a CFD simulation with the validation of previous wind-tunnel experiments. A total of 63 shading cases and 7 unshaded cases were both investigated by simulations. It was found that with the increase in wind attack angle θ, the surface-averaged convective heat transfer coefficient (CHTC) at the louvered surface will first increase by about 40~50% (θ from 0° to 60°) and then be cut down by 60% (θ from 60° to 90°). In addition, the convective heat exchange rate at the louver slats will gradually decrease with the growth of θ. Finally, modifications for previous correlations were proposed to quantify the effect of wind directions on the CHTCavg at the blinds and façade, of which the maximum deviation for convective heat exchange rate is less than 18%.

In this study, a method based on the discrete wavelet transform (DWT) and azimuth-scale expansion is presented to retrieve the sea-surface wind direction from a single X-band marine radar image. The algorithm first distinguishes rain-free and rain-contaminated radar images based on the occlusion zero-pixel percentage and then discards the rain-contaminated images. The radar image whose occlusion areas have been removed is decomposed into different low-frequency sub-images by the 2D DWT, and the appropriate low-frequency sub-image is selected. Images collected with a standard marine HH-polarized X-band radar operating at grazing incidence display a single intensity peak in the upwind direction. To overcome the influence of the occlusion area, before determining the wind direction, the data near the ship bow are shifted to expand the azimuth scale of the data. Finally, a harmonic function is least-square-fitted to the range-averaged radar return of the low-frequency sub-image as a function of the antenna look azimuth to determine the wind direction. Different from the wind-direction retrieval algorithms previously presented, this method is more suitable for sailing ships, as it functions well even if the radar data are heavily blocked. The results show that compared with the single-curve fitting algorithm, the algorithm based on DWT and azimuth-scale expansion can improve the wind-direction results in sailing ships, showing a reduction of 7.84° in the root-mean-square error with respect to the reference.

HF radars are now an important part of operational coastal observing systems where they are used primarily for measuring surface currents. Their use for wave and wind direction measurement has also been demonstrated. These measurements are based on physical models of radar backscatter from the ocean surface described in terms of its ocean wave directional spectrum and the influence thereon of the surface current. Although this spectrum contains information about the local wind that is generating the wind sea part of the spectrum, it also includes spectral components propagating into the local area having been generated by winds away from the area i.e., swell. In addition, the relationship between the local wind sea and wind speed depends on fetch and duration. Thus, finding a physical model to extract wind speed from the radar signal is not straightforward. In this paper, methods that have been proposed to date will be briefly reviewed and an alternative approach is developed using machine learning methods. These have been applied to three different data sets using different radar systems in different locations. The results presented here are encouraging and proposals for further development are outlined.

Traditionally, circular cross-section towers have been used as supporting systems of wind turbines, but weaknesses have become apparent with recent upsizing of wind turbines. Thus, polygonal cross-section towers have been proposed and used in Europe. In this study, the effects of polygonal cross-sections on the aeroelastic and aerodynamic characteristics of wind turbines were examined through a series of wind tunnel tests. Aeroelastic tests showed that a square cross-section tower showed instability vibrations, and polygonal cross-section towers showed limited vibrations for tower-only cases. However, for wind turbines with various polygonal cross-section towers, no instability vibrations were observed, and displacements increased proportionally to the square of mean wind speed. Furthermore, pressure measurements showed that local force coefficients changed largely depending on wind direction and azimuth angle. Local drag force coefficients decreased with increasing number of tower sides, approaching those of the tower-only case, and local lift force coefficients showed larger absolute values than those of the tower-only case. The maximum mean and fluctuating drag force and the maximum fluctuating lift coefficients at each height decreased with increasing number of tower sides.

In view of the difficulty of wind direction retrieval in the case of the large space and time span of the global sea surface, a method of sea surface wind direction retrieval using a support vector machine (SVM) is proposed. This paper uses the space-borne global navigation satellite systems reflected signal (GNSS-R) as the remote sensing signal source. Using the Cyclone Global Navigation Satellite System (CYGNSS) satellite data, this paper selects a variety of feature parameters according to the correlation between the features of the sea surface reflection signal and the wind direction, including the Delay Doppler Map (DDM), corresponding to the CYGNSS satellite parameters and geometric feature parameters. The Radial Basis Function (RBF) is selected, and parameter optimization is performed through cross-validation based on the grid search method. Finally, the SVM model of sea surface wind direction retrieval is established. The result shows that this method has a high retrieval classification accuracy using the dataset with wind speed greater than 10 m/s, and the root mean square error (RMSE) of the retrieval result is 26.70°.

The structure and evolution of the atmospheric planetary boundary layer (PBL) plays an important role in the physical and chemical processes of cloud–radiation interaction, vertical mixing and pollutant transport in the atmosphere. The PBL parameterization scheme describes the vertical transport of atmospheric momentum, heat, water vapor and other physical quantities in the boundary layer. The accuracy of wind field simulation and prediction is one of the most significant parameters in the field of atmospheric science and wind energy. Limited by the observation data, there are few studies on wind energy development. A 3D Doppler wind LiDAR (DWL) providing the high-vertical-resolution wind data over the urban complex underlying surface in February 2018 was employed to systematically evaluate the accuracy of vertical wind field simulation for the first time. 11 PBL schemes of the Weather Research and Forecasting Model (WRF) were employed in simulation. The model results were evaluated in groups separated by weather (sunny days, hazy days and windy days), observation height layers of wind field, and various observation wind speeds. Among these factors, the simulation accuracy is most closely related to the observation height layers of wind field. The simulation is fairly accurate at a height of 1000–2000 m, as most of the relative mean biases for wind speed and wind direction are less than 20% and 6% respectively. Below 1000 m, the wind speed and direction biases are about 30–150% m·s−1 and 6–30%, respectively. Moreover, when the observed wind speed was lower than 5 m·s−1, the biases were usually large, and the wind speed relative mean bias reaches up to 50–300%. In addition, the accuracy of the simulated wind profile is better in the range of 10–15 m·s−1 than other speed ranges, and is better above 1000 m than below 1000 m in the boundary layer. We see that the WRF boundary layer schemes have different applicabilities to different weather conditions. The WRF boundary layer schemes have significant differences in wind field simulations, with larger error under the complex topographies. A PBL scheme is not likely to maintain its advantages in the long term under different conditions including altitude and weather conditions.

Sea temperature structures are important for water stratification and marine ecosystems. In the coastal water of Muping, China, stationary measurements of sea temperature captured temporal temperature changes during two summer storm events. The north component of the wind during the two storms was opposite. The temperature responded differently to wind directions in the two storm events. A well-validated numerical ocean model was used to investigate the mechanism of sea temperature variation of the coast of Muping. The model revealed that the southerly and easterly wind was upwelling-favorable in the study area. They generated the shoreward transport of bottom cold water, which induced bottom water cooling, enhanced stratification, and weakened vertical mixing. On the other hand, the northerly and westerly wind was downwelling-favorable and enhanced turbulent mixing. The alongshore upwelling-favorable wind caused more cross-shore transport than cross-shore upwelling-favorable wind, which resulted in stronger bottom cooling. Similarly, alongshore downwelling-favorable wind generated lower temperature than cross-shore wind. A surface cold-water band was formed in the second storm. Although it was formed during upwelling-favorable wind, the temperature balance analysis indicated that vertical mixing and westward horizontal advection were the two dominant processes compared to upwelling.

Recent developments in wind speed sensors have mainly focused on reducing the size and moving parts to increase reliability and stability. In this study, the development of a low-cost wind speed and direction measurement system is presented. A heat sink mounted on a self-regulating heater is used as means to interact with the wind changes and a thermopile array mounted atop of the heat sink is used to collect temperature data. The temperature data collected from the thermopile array are used to estimate corresponding wind speed and direction data using an artificial neural network. The multilayer artificial neural network is trained using 96 h data and tested on 72 h data collected in an outdoor setting. The performance of the proposed model is compared with linear regression and support vector machine. The test results verify that the proposed system can estimate wind speed and direction measurements with a high accuracy at different sampling intervals, and the artificial neural network can provide significantly a higher coefficient of determination than two other methods.

Results on the accuracy of SeaSonde High Frequency (HF) radar wind direction measurements in the Gulf of Naples (Southern Tyrrhenian Sea, Central Mediterranean Sea) are here presented. The investigation was carried out for a winter period (2 February–6 March) and for one summer month (August) of the reference year 2009. HF radar measurements were compared with in situ recordings from a weather station and with model data, with the aim of resolving both small scale and large scale dynamics. The analysis of the overall performance of the HF radar system in the Gulf of Naples shows that the data are reliable when the wind speed exceeds a 5 m/s threshold. Despite such a limitation, this study confirms the potentialities of these systems as monitoring platforms in coastal areas and suggests further efforts towards their improvement.

Wind-speed decline is an important impact of climate change on the eastern Asian atmospheric circulation. Although wind does not determine algae biomass in eutrophic lakes, it is a decisive factor in the formation and severity of algae blooms. Based on 2000–2018 MODIS images, this study compared the effects of wind speed on algal blooms in three typical eutrophic lakes in China: Lake Taihu, Lake Chaohu and Lake Dianchi. The results indicate that climate change has different effects on the wind speed of the three lakes, but a common effect on the vertical distribution of algae. A wind speed of 3.0 m/s was identified as the critical threshold in the vertical distribution of chlorophyll-a concentrations in the three study lakes. The basic characteristics of the periodic variation of wind speed were different, but there was a significant negative correlation between wind speed and floating algal bloom area in all three lakes. In addition, considering lake bathymetry, wind direction could be used to identify locations that were particularly susceptible to algae blooms. We estimated that algal bloom conditions will worsen in the coming decades due to the continuous decline of wind, especially in Lake Taihu, even though the provincial and national governments have made major efforts to reduce eutrophication drivers and restore lake conditions. These results suggest that early warning systems should include a wind-speed threshold of 3.0 m/s to improve control and mitigation of algal blooms on these intensively utilized lakes.

For a two-dimensional solid silicon thermal wind sensor with symmetrical structure, the wind speed and direction information can be derived from the output voltages in two orthogonal directions, i.e., the north-south and east-west. However, the output voltages in these two directions will vary linearly with the ambient temperature. Therefore, in this paper, a temperature model to study the temperature effect on the wind direction measurement has been developed. A theoretical analysis has been presented first, and then Finite Element Method (FEM) simulations have been performed. It is found that due to symmetrical structure of the thermal wind sensor, the temperature effects on the output signals in the north-south and east-west directions are highly similar. As a result, the wind direction measurement of the thermal wind sensor is approximately independent of the ambient temperature. The experimental results fit the theoretical analysis and simulation results very well.

The increased velocity (Invelox) wind turbine system is a novel wind energy collection device. This system can collect and accelerate the air flow through a funnel and a Venturi tube. However, the efficiency of this system is relatively low under some wind directions. To improve the aerodynamic performance of Invelox, a straight-through layout with a windshield was proposed. The flow field of the improved design was studied by applying Computational Fluid Dynamics (CFD) and was compared with that in the original configuration. Numerical results show that when the Invelox exit is facing the incoming wind, the ratio of the average velocity inside the Venturi tube to the incoming wind speed, i.e., the speed ratio, will drop sharply, and even the airflow will push back. The improved layout can eliminate the sensitivity of incoming wind direction to aerodynamic characteristics. The windshield can effectively reduce the interference of incoming air to the outlet air, making the speed ratio increase by about 42%. Different wind profiles in the atmospheric boundary layer are used in the boundary of the flow domain as the incoming flow wind. With the increase in the wind profile index, the speed ratio of the Invelox system will gradually decrease.

Storm surge is dependent on wind direction, with maximum surge heights occurring when strong winds blow onshore. It is less obvious what happens when a port city is situated at the end of a long narrow gulf, like Venice at the northwestern end of the Adriatic Sea. Does the narrow marine approach to the port city limit the dangerous wind direction to a span of only a few degrees? This modeling study shows that the response in surge height to wind direction is a sinusoidal curve for port cities at the end of a long inlet, as well as for cities exposed along a straight coastline. Surge height depends on the cosine of the angle between the wind direction and the major axis of the narrow gulf. There is no special protection from storm surge afforded by a narrow ocean-going approach to a port city.

Reliable wind modelling is of crucial importance for wind farm development. The common practice of using sector-wise Weibull distributions has been found inappropriate for wind farm layout optimization. In this study, we propose a simple and easily implementable method to construct joint distributions of wind speed and wind direction, which is based on the parameters of sector-wise Weibull distributions and interpolations between direction sectors. It is applied to the wind measurement data at Horns Rev and three different joint distributions are obtained, which all fit the measurement data quite well in terms of the coefficient of determination . Then, the best of these joint distributions is used in the layout optimization of the Horns Rev 1 wind farm and the choice of bin sizes for wind speed and wind direction is also investigated. It is found that the choice of bin size for wind direction is especially critical for layout optimization and the recommended choice of bin sizes for wind speed and wind direction is finally presented.

Despite recent efforts to record wind at finer spatial and temporal scales, stochastic realizations of wind are still important for many purposes and particularly for wind energy grid integration and reliability studies. Most instances of wind generation in the literature focus on simulating only wind speed, or power, or only the wind vector at a particular location and sampling frequency. In this work, we introduce a Markov-switching vector autoregressive (MSVAR) model, and we demonstrate its flexibility in simulating wind vectors for 10-min, hourly and daily time series and for individual, locally-averaged and regionally-averaged time series. In addition, we demonstrate how the model can be used to simulate wind vectors at multiple locations simultaneously for an hourly time step. The parameter estimation and simulation algorithm are presented along with a validation of the important statistical properties of each simulation scenario. We find the MSVAR to be very flexible in characterizing a wide range of properties in the wind vector, and we conclude with a discussion of extensions of this model and modeling choices that may be investigated for further improvements.

This study evaluates projected changes to surface wind characteristics for the 2071−2100 period over North America (NA), using four Global Environmental Multiscale regional climate model simulations, driven by two global climate models (GCMs) for two Representative Concentration Pathway scenarios. For the current climate, the model simulates well the climatology of mean sea level pressure (MSLP) and associated wind direction over NA. Future simulations suggest increases in mean wind speed for northern and eastern parts of Canada, associated with decreases in future MSLP, which results in more intense low-pressure systems situated in those regions such as the Aleutian and Icelandic Lows. Projected changes to annual maximum 3-hourly wind speed show more spatial variability compared to seasonal and annual mean wind speed, indicating that extreme wind speeds are influenced by regional level features associated with instantaneous surface temperature and air pressure gradients. The simulations also suggest some increases in the future 50-year return levels of 3-hourly wind speed and hourly wind gusts, mainly due to increases in the inter-annual variability of annual maximum values. The variability of projected changes to both extreme wind speed and gusts indicate the need for a larger set of projections, including those from other regional models driven by many GCMs to better quantify uncertainties in future wind extremes and their characteristics.

The joint modeling of angular and linear observations is crucial as data of this nature are prevalent in multiple disciplines, for example the joint modeling of wind direction and another climatological variable such as wind speed or air temperature, the direction an animal moves and the distance moved, or wave direction and wave height. Hence, there is a need for developing flexible distributions on the hyper-disc, which has support of the interior of the hyper-sphere, as it allows for modeling the combination of angular and linear observations. This paper addresses this need by developing flexible distributions for the disc that have the ability to capture any inherent bimodality present in the data. A new class of bivariate distributions is proposed which has support on the unit disc in two dimensions that includes, as a special case, the existing Möbius distribution on the disc. This class is obtained by expressing the density function in a general form using a measurable function termed as generator. Special cases of this generator are considered to demonstrate the flexibility. By applying a conformal mapping to the generator function a new Möbius distribution class emanates. This class of bivariate distributions on the disc is the first to account for bimodality and skewness present in the data. The flexible behavior of the proposed models in terms of bimodality and skewness is graphically demonstrated. Preliminary evidential analysis of the wind data observed at Marion Island reveals the absence of unimodality in the data. The fit of the proposed models, which account for bimodality, to the Marion Island wind data were evaluated analytically and visually.

In this study, large-eddy simulations (LESs) were performed to investigate the effects of changing wind direction on the turbine wakes and associated power losses in the Horns Rev offshore wind farm. In the LES framework, the turbulent subgrid-scale stresses are parameterized using a tuning-free Lagrangian scale-dependent dynamic model, and the turbine-induced forces are computed using a dynamic actuator-disk model with rotation (ADM-R). This dynamic ADM-R couples blade-element theory with a turbine-specific relation between the blade angular velocity and the shaft torque to compute simultaneously turbine angular velocity and power output. A total of 67 simulations were performed for a wide range of wind direction angles. Results from the simulations show a strong impact of wind direction on the spatial distribution of turbine-wake characteristics, such as velocity deficit and turbulence intensity. This can be explained by the fact that changing the wind angle can be viewed as changing the wind farm layout relative to the incoming wind, while keeping the same wind turbine density. Of particular importance is the effect of wind direction on the distance available for the wakes to recover and expand before encountering other downwind turbines (in full-wake or partial-wake interactions), which affects the power losses from those turbines. As a result, even small changes in wind direction angle can have strong impacts on the total wind farm power output. For example, a change in wind direction of just 10° from the worst-case full-wake condition is found to increase the total power output by as much as 43%. This has important implications for the design of wind farms and the management of the temporal variability of their power output.

Hourly surface observations from the Canadian Weather Energy and Engineering Dataset were analyzed with respect to long-term wind direction drift or rotation. Most of the Canadian landmass, including the High Arctic, exhibits a spatially consistent and remarkably steady anticyclonic rotation of wind direction. The period of anticyclonic rotation recorded at 144 out of 149 Canadian meteostations directly correlated with latitude and ranged from 7 days at Medicine Hat (50°N, 110°W) to 25 days at Resolute (75°N, 95°W). Only five locations in the vicinity of the Rocky Mountains and Pacific Coast were found to obey a “negative” (i.e., cyclonic) rotation. The observed anticyclonic rotation appears to be a deterministic, virtually ubiquitous, and highly persistent feature of continental surface wind. These findings are directly applicable to probabilistic assessments of airborne pollutants.

Land-based, high-frequency (HF) surface wave radar has the unique capability of monitoring coastal surface parameters, such as current, waves, and wind, up to 200 km off the coast. The Doppler spectrum of the backscattered radar signal is characterized by two strong peaks that are caused by the Bragg-resonant scattering from the ocean surface. The wavelength of Bragg resonant waves is exactly half the radio wavelength (grazing incidence), and these waves are located at the higher frequency part of the wave spectral distribution. When HF radar operates at higher frequencies, the resonant waves are relatively shorter waves, which are more sensitive to a change in wind direction, and they rapidly respond to local wind excitation and a change in wind direction. When the radar operates at lower frequencies, the corresponding resonant waves are relatively longer and take longer time to respond to a change in wind direction due to the progress of wave growth from short waves to long waves. For the wind inversion from HF radar backscatter signals, the accuracy of wind measurement is also relevant to radar frequency. In this paper, a pattern-fitting method for extracting wind direction by estimating the wave spreading parameter is presented, and a comparison of the pattern-fitting method and a conventional method is given as well, which concludes that the pattern-fitting method presents better results than the conventional method. In order to analyze the wind direction inversion from radar backscatter signals under different wind conditions and at different radar frequencies, two radar experiments accomplished in Norway and Italy are introduced, and the results of wind direction inversion are presented. In the two experiments, the radar worked at 27.68 MHz and 12 MHz, respectively, and the wind conditions at the sea surface were quite different. In the experiment in Norway, 67.4% of the wind records were higher than 5 m/s, while, in the experiment in Italy, only 18.9% of the wind records were higher than 5 m/s. All these factors affect the accuracy of wind direction inversion. The paper analyzes the radar data and draws a conclusion on the influencing factor of wind direction inversion.

The accurate measurement of wind profiles in the planetary boundary layer (PBL) is important not only for numerical weather prediction, but also for air quality modeling. Two wind retrieval methods using scanning Doppler light detection and ranging (lidar) measurements were compared and validated with simultaneous radiosonde soundings. A comparison with 17 radiosonde sounding profiles showed that the sine-fitting method was able to retrieve a larger number of data points, but the singular value decomposition method showed significantly smaller bias (0.57 m s−1) and root-mean-square error (1.75 m s−1) with radiosonde soundings. Increasing the averaging time interval of radial velocity for obtaining velocity azimuth display scans to 15 min resulted in better agreement with radiosonde soundings due to the signal averaging effect on noise. Simultaneous measurements from collocated wind Doppler lidar and aerosol Mie-scattering lidar revealed the temporal evolution of PBL winds and the vertical distribution of aerosols within the PBL.

Wind field analysis from synthetic aperture radar images allows the estimation of wind direction and speed based on image descriptors. In this paper, we propose a framework to automate wind direction retrieval based on wavelet decomposition associated with spectral processing. We extend existing undecimated wavelet transform approaches, by including à trous with B3 spline scaling function, in addition to other wavelet bases as Gabor and Mexican-hat. The purpose is to extract more reliable directional information, when wind speed values range from 5 to 10 ms−1. Using C-band empirical models, associated with the estimated directional information, we calculate local wind speed values and compare our results with QuikSCAT scatterometer data. The proposed approach has potential application in the evaluation of oil spills and wind farms.

There is a strong need for long term observations of land surface fluxes such as actual evapotranspiration (ETa). Eddy covariance (EC) method is widely used to provide ETa measurements, and several gap-filling methods have been proposed to complete inherent missing data. However, implementing gap-filling methods is questionable for EC time series collected within hilly agricultural areas at the watershed extent. Indeed, changes in wind direction induce changes in airflow inclination and footprint, and therefore possibly induce changes in the relationships on which rely gap-filling methods. This study aimed to obtain continuous ETa time series by adapting gap-filling methods to the particular conditions abovementioned. The experiment took place within an agricultural watershed in north-eastern Tunisia. A 9.6-m-high EC flux tower has been operating close to the watershed center since 2010. The sensible and latent heat fluxes data collected from 2010 to 2013 were quality controlled, and the REddyProc software was used to fill gaps at the hourly timescale. Adapting REddyProc method consisted of splitting the dataset according to wind direction, which improved the flux data at the hourly timescale, but not at the daily and monthly timescales. Finally, complete time series permitted to analyze seasonal and inter-annual variability of ETa.