Search

The Wind–Vehicle–Bridge (WVB) coupling method is extensively applied in engineering practices for the dynamic analysis of railway bridges. However, research on the dynamic behaviors of the vehicle on curved bridges in wind conditions remains limited due to the complexities of interactions involved. In this study, the railway alignment and an actual curved bridge were chosen and modeled. The framework of the WVB system was detailed, and four excitation cases were investigated, considering mean wind, fluctuating wind and track irregularities. Three distinct scenarios, including vehicle on a curved bridge subjected to Inward Blowing Wind (IBW), Outward Blowing Wind (OBW) and vehicle on a straight bridge subjected to wind, were explored. The main conclusions are as follows: (1) wind direction significantly affects wheel unloading rate, lateral force, and derailment coefficient; (2) under mean wind condition, critical wind speed increases with the vehicle speed increasing under IBW, while it decreases with the vehicle speed increasing under OBW; (3) Combined wind and track irregularities reveal that wind direction significantly influences dynamic responses, and lower speeds are not necessarily equal to safer conditions for vehicles on curved bridges. [ABSTRACT FROM AUTHOR]

BACKGROUND AND OBJECTIVES: Overusing renewable resources for various purposes is making it necessary to use fewer non-renewable ones to generate energy. Finding alternative renewable energy sources is essential for energy production. This study concentrated on using wind direction and speed to produce wind energy among renewable energy sources. Data on wind direction and speed were statistically analyzed to determine the current distribution pattern, which is then used to project the amount of wind energy that will be available in the future. METHODS: This study concentrated on choosing wind direction and speed to minimize the potential for current electricity generation from wind turbines, using data collected between 1981 and 2023. The wind speed and direction distribution pattern was assessed through the Weibull distribution, beta distribution, and three-parameter Weibull distribution. The Anderson-Darling test and the Kolmogorov-Smirnov test were employed in this study to determine the goodness-of-fit of a specific distribution. The forecasting analysis was expanded from 2024 to 2050 based on the three-parameter Weibull distribution and Anderson-Darling test results for future sustainable wind energy production. FINDINGS: The average wind speed was found to be 6.51 meters per second, with a standard deviation of 0.280 meters per second between 1981 and 2023. The wind direction varied between a minimum of 3.56 and a maximum of 356.44 degrees for the same duration. The study discovered that the three-parameter Weibull distribution caused less error in the wind speed data distribution pattern than both the Weibull distribution and beta distribution, based on the results of the Anderson-Darling and Kolmogorov-Smirnov tests. From both the tests on Weibull distribution, beta distribution, and three-parameter Weibull distribution, this study found that the Anderson-Darling test was the most appropriate for forecasting the wind speed corresponding to the wind direction for the periods between 2024 and 2050 to produce sustainable wind energy from the wind turbine. CONCLUSION: The outcomes of this study demonstrate that there is a good likelihood that the parameter Weibull distribution and Anderson-Darling test will be used in other nations to aid in the complementary integration of wind energy. This research has the potential to significantly reduce the amount of environmentally hazardous energy sources used to meet societal requirements. This study offers a trustworthy technique for assessing wind direction and speed, which helps design sustainable wind power plants, construct engineering curricula, and estimate clean, environmentally friendly energy sources. [ABSTRACT FROM AUTHOR]

Background: Insecticides are sprayed on external building walls for treatments against crawling and flying insects. These applications can lead to drift into non-target areas and thus to undesirable environmental pollution. This emission pathway needs to be considered during exposure assessments within product authorisations to assess potential environmental risks. However, now, there is only one default value for deposition that is used in all calculations based on the Emission Scenario Document of the Organization for Economic Co-operation and Development at a distance of 50 cm to the treatment area. This is not sufficient for a risk assessment. Results: When applying a chemical barrier of 50 cm at the bottom of the building wall, wind direction had the greatest influence on drift, while changing the nozzle type had no significant effect. Compared with the measured ground sediments, the OECD default value was deemed to be realistic at a distance of 57 cm from the treatment area. When treating the entire building wall, the wind direction as well as the nozzle used show significant influence on the measured values of drift. The default value for deposition proposed for modelling environmental exposure in OECD document ESD PT18 No. 18 was exceeded. Thus, the exposure estimation might not be protective enough. Conclusion: Drift values used for the environmental exposure assessment of biocidal products during treatments of building walls should be adapted. This is especially relevant for treatments of entire building walls, where the current default value was exceeded for all distances from the building wall. Wind direction and nozzle type can reduce environmental impact. This finding can be used as a measure to reduce unnecessary exposure in the environment in the future. [ABSTRACT FROM AUTHOR]

Background: Adopting adequate isolation distances is one of the very crucial principles and practices of quality seed production. Upon research and continuous evaluation, isolation distances for each crop, kind and cultivar have been determined in almost all cultivable crops. The development of hybrids through various mechanisms has led to redefining isolation distances in a few crops like pigeonpea based upon the principles of pollination ecology and the activity of various pollinators. Methods: Our study on the direction of male and female blocks, activity and frequency of pollinators and their effect on production potential and quality of seed was systematically conducted at various isolation distances viz., 100, 150, 200, 250, 300 and 400 m between female (A) and male (R) lines. Result: Our empirical information depicts that an isolation distance of 400 m between male and female parents reduces the chances of genetic contamination and hence the isolation distance in case of hybrid seed production of pigeonpea shall be redefined as 400 m. [ABSTRACT FROM AUTHOR]

The choice of bandwidth is crucial in circular kernel density estimation. Various bandwidth selection techniques have been proposed in the literature. New bandwidth selectors based on the measure β -divergence for kernel density estimation with circular data are presented in this work. These selectors are obtained by minimizing the mean of the measure β -divergence between the density to be estimated and its estimator. The idea is based on the generalization of the standard method which selects the bandwidth by minimizing the mean integrated squared error (MISE). The performance of the proposed selectors is evaluated through a simulation study and compared with other existing selectors. These selectors are illustrated with some real datasets. [ABSTRACT FROM AUTHOR]

The issue of pedestrian-level wind environments around high-rise buildings is closely related to the comfort and safety of human settlements. In this paper, we study the effects of different wind direction angles and spacing ratios on the wind environment at pedestrian heights around buildings arranged in an equilateral triangle configuration. Three-dimensional steady-state numerical simulation was employed, with the standard k-ε model selected as the turbulence model. Wind speed ratios and different area ratio parameters are used to quantitatively express the degree and range of influence of wind speed by buildings. The results show that the maximum wind speed ratio at the corner of a building is greatly affected by the wind direction angle, with 45°, 135°, and 157.5° being the unfavorable wind direction angles. Conversely, the area ratio of different areas is greatly affected by the spacing ratio. As the spacing ratio increases, the mutual interference effect between buildings weakens, resulting in a better pedestrian wind environment. Owing to the unique layout of the building group, different degrees of ventilation corridors are formed among the three buildings. The wind speed amplification effect in the corridors is more significant, and the areas with poor wind environments are concentrated in these corridors. [ABSTRACT FROM AUTHOR]

The Chinese solar greenhouse (CSG) is a prevalent feature in agricultural practices within China. Nevertheless, the regulation of natural ventilation within this architectural structure remains suboptimal. Consequently, the development of a natural ventilation model becomes imperative for the effective management of the greenhouse environment. Of particular significance within these models is the consideration of the discharge coefficient as a pivotal parameter. Conducting a multi-case investigation into the variable-dependent discharge coefficient is crucial for both practical application and model advancement. This research delved into the impact of various factors, including the upper-lower vents area ratio (Aup/Alow), vent-greenhouse area ratio (Alow/Agreenhouse), lower vent position height (h/H), the incident angle of the external wind, and altitude, on the discharge coefficient (Cd) of CSG. A CFD model was developed for a scaled CSG with validation conducted through field experiments and wind tunnel tests. Results indicated a 61.6% reduction in Cd on average corresponding to an 80% decrease in Aup/Alow. Cd levels remained consistent following the attainment of an Aup/Alow ratio of 1.0. Besides, there was an average increase of 52.5% in Cd levels for every 0.09 decline in h/H, attributed to the blocking effect of the cover. Moreover, the ventilation rate and the pressure coefficient difference were utilized to construct a model of Cd pertaining to greenhouse design and ventilation operation, exhibiting a notable accuracy level of R2 = 0.95. Furthermore, the blocking effect of higher h/H was relieved as the incident angle θ decreased under the windward conditions. The increase in Aup/Alow and the decrease in Alow/Agreenhouse were identified as crucial factors contributing to the growth of Cd under leeward conditions. Ultimately, the high-altitude environment led to a rise in Cd levels in contrast to the low-altitude region. The increasing rate of Cd correlated positively with Alow/Agreenhouse and h/H initially, but exhibited a decline once Alow/Agreenhouse reached 0.036, remaining stable thereafter once h/H reached 0.18. In summary, a comprehensive examination of the discharge coefficient of CSG was undertaken, addressing a significant knowledge deficiency and laying the groundwork for advancements in the natural ventilation model and the intelligent control system for CSG. [ABSTRACT FROM AUTHOR]

BACKGROUND AND OBJECTIVES: Overusing renewable resources for various purposes is making it necessary to use fewer non-renewable ones to generate energy. Finding alternative renewable energy sources is essential for energy production. This study concentrated on using wind direction and speed to produce wind energy among renewable energy sources. Data on wind direction and speed were statistically analyzed to determine the current distribution pattern, which is then used to project the amount of wind energy that will be available in the future.METHODS: This study concentrated on choosing wind direction and speed to minimize the potential for current electricity generation from wind turbines, using data collected between 1981 and 2023. The wind speed and direction distribution pattern was assessed through the Weibull distribution, beta distribution, and three-parameter Weibull distribution. The Anderson-Darling test and the Kolmogorov-Smirnov test were employed in this study to determine the goodness-of-fit of a specific distribution. The forecasting analysis was expanded from 2024 to 2050 based on the three-parameter Weibull distribution and Anderson-Darling test results for future sustainable wind energy production.FINDINGS: The average wind speed was found to be 6.51 meters per second, with a standard deviation of 0.280 meters per second between 1981 and 2023. The wind direction varied between a minimum of 3.56 and a maximum of 356.44 degrees for the same duration. The study discovered that the three-parameter Weibull distribution caused less error in the wind speed data distribution pattern than both the Weibull distribution and beta distribution, based on the results of the Anderson-Darling and Kolmogorov-Smirnov tests. From both the tests on Weibull distribution, beta distribution, and three-parameter Weibull distribution, this study found that the Anderson-Darling test was the most appropriate for forecasting the wind speed corresponding to the wind direction for the periods between 2024 and 2050 to produce sustainable wind energy from the wind turbine.CONCLUSION: The outcomes of this study demonstrate that there is a good likelihood that the parameter Weibull distribution and Anderson-Darling test will be used in other nations to aid in the complementary integration of wind energy. This research has the potential to significantly reduce the amount of environmentally hazardous energy sources used to meet societal requirements. This study offers a trustworthy technique for assessing wind direction and speed, which helps design sustainable wind power plants, construct engineering curricula, and estimate clean, environmentally friendly energy sources.

The Gaofen-3 (GF-3) is a Chinese Synthetic Aperture Radar (SAR) satellite primarily designed for monitoring marine dynamic environments. Compared to spaceborne scatterometers, it can monitor sea surface wind fields with higher spatial resolution. Currently, the spectrum analysis-based algorithms are widely used to extract wind direction from SAR images, but they are susceptible to image noise. To address this issue, this study proposes a wind direction inversion method based on iterative linear fitting, which fully utilises wind streak information at different wavenumbers and improves wind direction inversion accuracy by iteratively removing data points with high noise levels. Twelve GF-3 SAR images were used to validate the proposed algorithm. Experimental results indicate that the root mean square error (RMSE) of the retrieved wind direction is 17.10°. Compared to the European Centre for Medium-Range Weather Forecast (ECMWF) reanalysis wind, and the mean absolute deviation is 10.93°Compared to the National Data Buoy Center (NDBC) buoy wind. Compared with the conventional local gradient algorithm, the proposed algorithm achieves higher wind direction inversion accuracy. This study provides a technical support for the operational wind field inversion using Gaofen-3 SAR images.

The issue of pedestrian-level wind environments around high-rise buildings is closely related to the comfort and safety of human settlements. In this paper, we study the effects of different wind direction angles and spacing ratios on the wind environment at pedestrian heights around buildings arranged in an equilateral triangle configuration. Three-dimensional steady-state numerical simulation was employed, with the standard k-ε model selected as the turbulence model. Wind speed ratios and different area ratio parameters are used to quantitatively express the degree and range of influence of wind speed by buildings. The results show that the maximum wind speed ratio at the corner of a building is greatly affected by the wind direction angle, with 45°, 135°, and 157.5° being the unfavorable wind direction angles. Conversely, the area ratio of different areas is greatly affected by the spacing ratio. As the spacing ratio increases, the mutual interference effect between buildings weakens, resulting in a better pedestrian wind environment. Owing to the unique layout of the building group, different degrees of ventilation corridors are formed among the three buildings. The wind speed amplification effect in the corridors is more significant, and the areas with poor wind environments are concentrated in these corridors.

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.

Biogeographical barriers to gene flow are central to plant phylogeography. In East Asia, plant distribution is greatly influenced by two phylogeographic breaks, the Mekong-Salween Divide and Tanaka-Kaiyong Line, however, few studies have investigated how these barriers affect the genetic diversity of species that are distributed across both. Here we used 14 microsatellite loci and four chloroplast DNA fragments to examine genetic diversity and distribution patterns of 49 populations of Populus rotundifolia, a species that spans both the Mekong-Salween Divide and the Tanaka-Kaiyong Line in southwestern China. Demographic and migration hypotheses were tested using coalescent-based approaches. Limited historical gene flow was observed between the western and eastern groups of P. rotundifolia, but substantial flow occurred across both the Mekong-Salween Divide and Tanaka-Kaiyong Line, manifesting in clear admixture and high genetic diversity in the central group. Wind-borne pollen and seeds may have facilitated the dispersal of P. rotundifolia following prevalent northwest winds in the spring. We also found that the Hengduan Mountains, where multiple genetic barriers were detected, acted on the whole as a barrier between the western and eastern groups of P. rotundifolia. Ecological niche modeling suggested that P. rotundifolia has undergone range expansion since the last glacial maximum, and demographic reconstruction indicated an earlier population expansion around 600 Ka. The phylogeographic pattern of P. rotundifolia reflects the interplay of biological traits, wind patterns, barriers, niche differentiation, and Quaternary climate history. This study emphasizes the need for multiple lines of evidence in understanding the Quaternary evolution of plants in topographically complex areas.

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. [ABSTRACT FROM AUTHOR]

The dynamic characteristics of different airflows on micro-scales have been explored from many perspectives since the late 1970s. On the one hand, most analytical tools and research subjects in previous contributions vary significantly: some only focus on fluctuant velocity features, while others pay attention to directional features. On the other hand, despite the wide variety of existing analytical methods, they are not systematically classified and organized. This paper aims to establish a system including state-of-the-art tools for airflow analysis and to further design a holistic toolkit named Airflow Analytical Toolkit (AAT). The AAT contains two tools, responsible for analyzing the velocity and direction characteristics of airflows, each of which is integrated with multiple analytical modules. To assess the performance of the developed toolkit, we further take typical natural and mechanical winds as cases to show its excellent analytical capability. With the help of this toolkit, the great differences in velocity and directional characteristics among different airflows are identified. The comparative results reveal that not only is the velocity of natural wind more fluctuating than that of mechanical wind, but its incoming flow direction is also more varying. The AAT, serving as a powerful and user-friendly instrument, will hopefully offer great convenience in data analysis and guidance for a deeper understanding of the dynamic characteristics of airflows, and further remedy the gap in airflow analytical tools. [ABSTRACT FROM AUTHOR]

Wind power has unpredictable, intermittent traits due to meteorological conditions and environmental factors. Large-scale grid integration of wind energy will undoubtedly challenge system stability. This study developed a fireworks algorithmbackpropagation (FWA-BP) neural network model to forecast wind power using wind speed, direction, and power as model inputs. Optimization of the BP network weights and thresholds occurred through the fireworks algorithm. Compared to a standard BP network, the FWA-BP model yielded improved prediction accuracy seen through a lower mean squared error. This implies that the approach introduced in this paper significantly enhances global search capabilities, prediction accuracy, and speed. It contributes to enhancing the reliability of the power system, optimizing resource allocation, and improving wind power scheduling, with substantial potential and economic significance. [ABSTRACT FROM AUTHOR]

A complete framework of predicting the attributes of sea clutter under different operational conditions, specified by wind speed, wind direction, grazing angle, and polarization, is proposed for the first time. This framework is composed of empirical spectra to characterize sea-surface profiles under different wind speeds, the Monte Carlo method to generate realizations of sea-surface profiles, the physical-optics method to compute the normalized radar cross-sections (NRCSs) from individual sea-surface realizations, and regression of NRCS data (sea clutter) with an empirical probability density function (PDF) characterized by a few statistical parameters. JONSWAP and Hwang ocean-wave spectra are adopted to generate realizations of sea-surface profiles at low and high wind speeds, respectively. The probability density functions of NRCSs are regressed with K and Weibull distributions, each characterized by two parameters. The probability density functions in the outlier regions of weak and strong signals are regressed with a power-law distribution, each characterized by an index. The statistical parameters and power-law indices of the K and Weibull distributions are derived for the first time under different operational conditions. The study reveals succinct information of sea clutter that can be used to improve the radar performance in a wide variety of complicated ocean environments. The proposed framework can be used as a reference or guidelines for designing future measurement tasks to enhance the existing empirical models on ocean-wave spectra, normalized radar cross-sections, and so on. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Wind direction nowcasting is crucial in various sectors, particularly for ensuring aviation operations and safety. In this context, the TELMo (Time-series Embeddings from Language Models) model, a sophisticated deep learning architecture, has been introduced in this work for enhanced wind-direction nowcasting. Developed by using three years of data from multiple stations in the complex terrain of an international airport, TELMo incorporates the horizontal u (east–west) and v (north–south) wind components to significantly reduce forecasting errors. On a day with high wind direction variability, TELMo achieved mean absolute error values of 5.66 for 2-min, 10.59 for 10-min, and 14.79 for 20-min forecasts, processed within a swift 9-ms/step timeframe. Standard degree-based analysis, in comparison, yielded lower performance, emphasizing the effectiveness of the u and v components. In contrast, a Vanilla neural network, representing a shallow-learning approach, underperformed in all analyses, highlighting the superiority of deep learning methodologies in wind direction nowcasting. TELMo is an efficient model, capable of accurately forecasting wind direction for air traffic operations, with an error less than 20° in 97.49% of the predictions, aligning with recommended international thresholds. This model design enables its applicability across various geographical locations, making it a versatile tool in global aviation meteorology. [ABSTRACT FROM AUTHOR]

The cultivation of cashew crops carries numerous economic advantages, and countries worldwide that produce this crop face a high demand. The effects of wind speed and wind direction on crop yield prediction using proficient deep learning algorithms are less emphasized or researched. We propose a combination of advanced deep learning techniques, specifically focusing on long short-term memory (LSTM) and random forest models. We intend to enhance this ensemble model using dynamic time warping (DTW) to assess the spatiotemporal data (wind speed and wind direction) similarities within Jaman North, Jaman South, and Wenchi with their respective production yield. In the Bono region of Ghana, these three areas are crucial for cashew production. The LSTM-DTW-RF model with wind speed and wind direction achieved an R2 score of 0.847 and the LSTM-RF model without these two key features R2 score of (0.74). Both models were evaluated using the augmented Dickey-Fuller (ADF) test, which is commonly used in time series analysis to assess stationarity, where the LSTM-DTW-RF achieved a 90% level of confidence, while LSTM-RF attained an 87.99% level. Among the three municipalities, Jaman South had the highest evaluation scores for the model, with an RMSE of 0.883, an R2 of 0.835, and an MBE of 0.212 when comparing actual and predicted values for Wenchi. In terms of the annual average wind direction, Jaman North recorded (270.5 SW°), Jaman South recorded (274.8 SW°), and Wenchi recorded (272.6 SW°). The DTW similarity distance for the annual average wind speed across these regions fell within specific ranges: Jaman North (±25.72), Jaman South (±25.89), and Wenchi (±26.04). Following the DTW similarity evaluation, Jaman North demonstrated superior performance in wind speed, while Wenchi excelled in wind direction. This underscores the potential efficiency of DTW when incorporated into the analysis of environmental factors affecting crop yields, given its invariant nature. The results obtained can guide further exploration of DTW variations in combination with other machine learning models to predict higher cashew yields. Additionally, these findings emphasize the significance of wind speed and direction in vertical farming, contributing to informed decisions for sustainable agricultural growth and development. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In order to explore the wind load characteristics acting on solar photovoltaic panels under extreme severe weather conditions, based on the Shear Stress Transport (SST) κ-ω turbulence model, numerical calculations of three-dimensional incompressible viscous steady flow were performed for four installation angles and two extreme wind directions of the solar photovoltaic panels. The wind load characteristics on both sides of the photovoltaic panels were obtained, and the vortex structure characteristics were analyzed using the Q criterion. The results indicate that, under different installation angles, the windward side pressure of the solar photovoltaic panel is generally higher than the leeward side. The leeward side is prone to forming larger vortices, increasing the fatigue and damage risk of the material, which significantly impacts the solar photovoltaic panel. As the installation angle increases, the windward side pressure of the solar photovoltaic panel also gradually increases. Therefore, optimal installation methods include installing the panel facing the wind at angles of 30° and 45°, or installing it facing away from the wind at a 60° angle, to minimize the impact of wind load on the solar photovoltaic panel. [ABSTRACT FROM AUTHOR]

Wildfires pose a significant threat to natural ecosystems, human lives, and properties worldwide. Developing effective prevention and control strategies is crucial for minimizing the risk of catastrophic wildfire events. In this context, computational modelling has emerged as a valuable tool for simulating and analyzing wildfire behavior, which can aid in identifying potential areas for intervention and prevention measures. This article presents a cellular automata model for simulating forest fires in Sete Cidades National Park, Brazil. The model takes into account the wind direction and speed, vegetation type, and probability of burning in a two-dimensional lattice representing the forest area of interest. The simulation output is analyzed to determine the extent of the damage caused by the fire and to identify critical parameters for wildfire propagation. The results highlight the importance of considering wind direction and speed when developing prevention and control strategies for wildfires. By providing insights into the behavior of wildfires, computational modeling can support decision-making processes and facilitate the development of effective policies to manage the risk of wildfires.

Zizania latifolia has been observed to generate floating mats due to water level fluctuations and wind-waves, leading to significant detrimental impacts on both water quality and biodiversity. The key to controlling and monitoring the spread and movement of Zizania floating is the early detection of outbreaks and the clarification of its formation mechanisms in response to water levels and wind actions. The temporal and spatial resolutions of conventional remote sensing monitoring methods are challenging to satisfy the requirements for continuous and accurate tracking of Zizania floating, which undergoes daily movement. This study develops a timely and accurate monitoring method for Zizania floating by calculating the Normalized Difference Vegetation Index (NDVI) from high spatiotemporal resolution PlanetScope (PS) imagery and building a decision tree on the Google Earth Engine (GEE) platform. Taking the Zizania floating outbreak in Honghu Wetland as the subject of our study, we extracted the continuous movement trajectories of Zizania floating from May to September 2019. Subsequently, we analyzed the interrelationship between the formation of Zizania floating and hydrometeorological factors. We then investigated the effects of Zizania floating on water quality and biodiversity. The results show that: (1) The integration of PS imagery and the GEE platform holds the potential to simplify the workload of remote sensing data processing. Based on the difference in NDVI between water bodies and Zizania floating, the dynamic expansion and reduction areas of the Zizania floating were successfully extracted by setting the threshold at 0.3, enabling timely and accurate tracking. (2) Abrupt fluctuations in water levels during brief time intervals serve as the primary trigger for Zizania floating. Additionally, wind speed during the same timeframe acts as a catalyst for the emergence of Zizania floating, with wind direction influencing its movement. (3) The outbreak of Zizania floating significantly elevates the concentrations of total nitrogen (TN), total phosphorus (TP), chemical oxygen demand (CODmn), and total ammonia nitrogen (TAN), while concurrently reducing the dissolved oxygen (DO) concentration in water bodies. The prolonged presence and movement of Zizania floating has reduced the biodiversity of the wetland ecosystem. Zizania floating mats present unique challenges due to their unpredictability, complicating the monitoring and tracking processes. It is necessary to develop a prompt, accurate, and cost-effective detection program for Zizania floating to reduce the risk it poses to wetland ecosystems.

This study is devoted to the development of a logical system to improve the efficiency of wind turbines with the identification of patterns and relationships between wind speeds and directions in urban conditions. The results of the study made it possible to identify approaches to use wind energy efficiently to produce electricity with minimal losses for the power supply of urban facilities. Data Mining methods were applied, the dependencies between weather data were examined. Machine Learning algorithms have also been applied to forecast wind speed and direction in order to increase the efficiency of power generation. A thorough exploratory data analysis was carried out, including visualization of meteorological data and the study of their statistical indicators. To predict wind speed, numerical indicators such as air temperature, wind direction, pressure tendency and atmospheric pressure were taken. Linear Regression, Decision Tree and Support Vector Machine were taken as Machine Learning models. The results revealed that the Random Forest turned out to be the most effective, with a mean squared error of 0.302. According to the results of the research, it was found that the initial data on the characteristics of the wind are highly distorted when it circulates in the urban environment due to the influence of urban development. However, the use of a logical system made it possible to predict and adapt the operation of wind turbines to changes in wind characteristics with minimal losses. A pivotal outcome of this study is the creation of a real-time data processing system, enabling accurate predictions of wind characteristics from speed and direction measuring devices. Additionally, a MATLAB Simulink-based computer model was developed to explore the logical system's influence on wind turbine performance. The findings underscore the efficacy of employing forecasting within a logical system framework to harness wind energy efficiently, contributing to sustainable urban energy solutions. [ABSTRACT FROM AUTHOR]

Numerical simulations and in-situ measurements represent two important and synergistic pillars for the study of flow and transport in plant canopies. Due to model limitations and parameter uncertainty, the alignment of model predictions with actual observations is challenging in practice. The present work proposes a Bayesian uncertainty quantification (UQ) framework that estimates the approaching wind angle parameter for large-eddy simulation (LES) of flow in plant canopies by assimilating data from in-situ measurements. The framework is applied to LES of flow within and above realistic plant canopy, with plant area density derived from light detection and ranging measurements. Uncertainty on approaching wind direction is characterized via a Markov chain Monte Carlo procedure, and propagated through Monte Carlo sampling to wind speed and resolved Reynolds stresses. Given the substantial computational cost of LES, a surrogate model based on an exiguous number of LESs is used for flow simulations within the UQ framework. As a result of the analysis, the UQ solution is given by probability density functions of selected flow statistics at different heights. Profiles of mean ± standard deviation for the considered flow statistics exhibit excellent agreement with corresponding observations, proving that the proposed approach is able to calibrate the approaching wind angle parameter, and that the quantified uncertainty captures discrepancies between observations and model results. Overall, the present work highlights the potential of UQ to enhance predictions of exchange processes between vegetation canopy and atmosphere. [ABSTRACT FROM AUTHOR]

The cultivation of cashew crops carries numerous economic advantages, and countries worldwide that produce this crop face a high demand. The effects of wind speed and wind direction on crop yield prediction using proficient deep learning algorithms are less emphasized or researched. We propose a combination of advanced deep learning techniques, specifically focusing on long short-term memory (LSTM) and random forest models. We intend to enhance this ensemble model using dynamic time warping (DTW) to assess the spatiotemporal data (wind speed and wind direction) similarities within Jaman North, Jaman South, and Wenchi with their respective production yield. In the Bono region of Ghana, these three areas are crucial for cashew production. The LSTM-DTW-RF model with wind speed and wind direction achieved an R2 score of 0.847 and the LSTM-RF model without these two key features R2 score of (0.74). Both models were evaluated using the augmented Dickey-Fuller (ADF) test, which is commonly used in time series analysis to assess stationarity, where the LSTM-DTW-RF achieved a 90% level of confidence, while LSTM-RF attained an 87.99% level. Among the three municipalities, Jaman South had the highest evaluation scores for the model, with an RMSE of 0.883, an R2 of 0.835, and an MBE of 0.212 when comparing actual and predicted values for Wenchi. In terms of the annual average wind direction, Jaman North recorded (270.5 SW°), Jaman South recorded (274.8 SW°), and Wenchi recorded (272.6 SW°). The DTW similarity distance for the annual average wind speed across these regions fell within specific ranges: Jaman North (±25.72), Jaman South (±25.89), and Wenchi (±26.04). Following the DTW similarity evaluation, Jaman North demonstrated superior performance in wind speed, while Wenchi excelled in wind direction. This underscores the potential efficiency of DTW when incorporated into the analysis of environmental factors affecting crop yields, given its invariant nature. The results obtained can guide further exploration of DTW variations in combination with other machine learning models to predict higher cashew yields. Additionally, these findings emphasize the significance of wind speed and direction in vertical farming, contributing to informed decisions for sustainable agricultural growth and development.

High-frequency (HF) surface wave radar has been widely used for measuring sea surface current, but its accuracy and spatial coverage of sea wave measurement are limited. A wave height mapping method based on HF radar-measured surface currents is proposed in this article. The tidal currents are first estimated and subtracted from the radar-measured surface currents, and then the geostrophic currents are removed. The remaining currents, along with the wind directions, are used as input data to a cascaded long short-term memory network. This network contains two stages. The first stage divides the input data into two groups of high and low sea states, and the second stage performs wave height inversion for each group separately. By combining the inversion results of the two groups, wave height maps are obtained. The algorithm is verified using the experimental data from the southwestern Taiwan Strait. Using the numerical wave height fields as the ground truth, the proposed algorithm has a root-mean-square error of 0.35 m and a correlation coefficient of 0.90.

[Introduction] In order to solve the safety problem of towing transportation of floating OWT（ offshore wind turbine ), the towing characteristics of fully submersible floating OWT are studied. [Method] The model of the fully submersible floating OWT towing system was established through Moses software. For the towing process, five groups of sea conditions were set up for simulation, the numerical model calculations were carried out, the influence of wind speed and wind wave direction on towing response of fully submersible floating OWT was studied. According to the analysis results, some suggestions were put forward to help the towing safety of the fully submersible floating OWT. [Result] The results show that, compared with rolling, the pitch and heave of the fully submersible floating OWT are more obviously affected by the wind speed, and the greater the wind speed, the greater the motion amplitude; when the wind wave direction is 90°, the rolling of OWT is greatly influenced by the wind wave direction, and when the wind wave direction is 0°, the pitch and heave of the OWT is greatly influenced by the wind wave direction. [Conclusion] On the premise of ensuring that the towing process is within the towing window period, it is suggested that the fully submersible floating OWT should be towed against the wind in practical engineering to reduce the risk of vibration and resonance of OWT. At the same time, we should try our best to avoid the situation that the wind direction is perpendicular to the towing direction, which will easily lead to a large degree of rolling and make the towing process more dangerous. When towing against the wind, the motion response and towing force of the fully submersible floating OWT can still meet the requirements of safe towing at a large wind speed of 30.9 m/s exceeding the average sea condition.

In this article, an intelligent method for inverting wind direction from quad-polarized Gaofen-3 (GF-3) synthetic aperture radar (SAR) images is proposed. Specifically, 11300 acquired in wave (WAV) mode are used to retrieve the wind directions using a spectrum-transformation approach and prior information from European Centre for Medium-Range Weather Forecasts reanalysis at version 5 (ERA-5) data at 1-h intervals with 0.25° grids. The dependence of the wind direction on the polarimetric correlation coefficient (PCC) between the co- (vertical-vertical and horizontal-horizontal) and cross-polarization [vertical-horizontal and horizontal-vertical] channels is studied. It is found that the PCCs in four combination polarizations have asymmetric characteristics with respect to the wind direction with correlation coefficients of greater than 0.4 or less than −0.4. Following this rationale, the scheme for inverting wind direction from quad-polarized SAR is trained according to machine learning, in which the matrix PCCs, wind directions, azimuthal angles, and slopes from SAR intensity spectra at the peaks are used as inputs. Subsequently, this intelligent approach is applied to 1300 images in quad-polarization stripmap mode, and the retrieval results are validated against advanced scatterometer (ASCAT) measurements. The statistical analysis shows that the root-mean- squared error (RMSE) of the wind direction is 17.7°, the COR is 0.98, and the scatter index is 0.11. In addition, the wind speeds inverted using a geophysical model function CSARMOD-GF are compared with well-calibrated ASCAT products, resulting in an RMSE of 1.85 m/s, a COR of 0.78, and an SI of 0.28 for the wind speed. Thus, this article provides an automatic scheme for inverting wind from quad-polarized GF-3 SAR images without any external information.

The increase in atmospheric properties degradation including environmental polarization and ecosystem degradation has been linked with levels of climatic hazards posed by climatic change. The geographical location of the Federal Capital Territory of Nigeria in the North Central geo-political zone of Nigeria within the Savannah vegetation zone of the Wet African sub-region was x-rayed for her atmospheric-climatic-space property of wind, rainfall and the impact of the meteorological element of rainfall on the erosivity of the area. Downscaling of a thirty five (35) years climatic data was done. Modelling and simulation was undertaken using geo-statistical and physical science modelling and simulation packages including QGIS and Statgraphics centurion. Simulated and modelled data were subjected to statistical analysis using descriptive statistics including P-statistics. Result of the finding revealed that there exist a shift in the climatic behaviour of the Federal Capital Territory of Nigeria with projected data significant level at a P-value range at [P-value = 0.654638, P-value = 0.859967 and P-value = 0.859967] of the P-statistics at a 95% significant level (p > = 0.05), hence, validating a past (35 years) and future (12 years projection) change in the climatic behaviour of the area. Wind velocity impact in the area for the past 35 years has been huge, thus presenting a value range at 81.36km/h-12.6km/h which indicated high sea-land-atmospheric nexus impact towards the variability that exist in the climatic wellbeing of the area. Wind directional flux of the area ranges from 22°°-4.8 which also contributed to the change in climatic behavior of the area. There exist very minimal rainfall impact in the erosivity impact in the area, with a coefficient of Variation at CV=0.16%.

Abstract WRF wind forecasts from four operative schemes used by OHMC (Observatorio HidroMeteorológico de Córdoba), a test scheme (WRF-E) and two daily runs with 4 km horizontal resolution were analyzed. Wind simulations were compared with measurements from eight ground stations with anemometers at 10 m high during the period from June, 2019 to June, 2020. WRF-E incorporates more vertical levels, and an activated topo_wind option. The wind speed results show that WRF overestimates wind speed at most stations and the WRF-E model reduces the BIAS and the RMSE when compared with the operational models. The wind direction analysis shows that the higher the wind speed is, the more accurate the models are. In addition, a wind gust forecasting has been implemented and evaluated in this work. Wind gust correlation coefficient values are between 0.3 and 0.6, RMSE is between 3 and 5 m/s, and a positive BIAS(

In nature, wind often occurs simultaneously with rainfall, causing nutrient loss in soil and water on slopes. However, the influence of different rainfall intensities combined with different wind directions on soil erosion and nutrient loss remains unclear. This study utilized wind‐driven rainfall laboratory flume experiments to analyse the influence of different wind directions (windward, leeward and crosswind) and rainfall intensities (30, 60 and 90 mm h−1) with constant wind speed (5 m s−1) on slope runoff, sediment yield and the associated losses of different forms of N (NH4+, NO3−) and P (PO43−‐P). The response of soil erosion, N and P loss to rainfall changes with different wind directions. When the rainfall intensity was constant, runoff, sediment yield and sediment‐associated total nitrogen (TN) and total phosphorus (TP) losses on slopes showed a trend of leeward > crosswind > windless > windward, whereas runoff‐associated TN and TP losses showed a trend of windward > crosswind > windless > leeward. Runoff, sediment yield and sediment‐associated TN and TP losses on leeward slopes were 1.09–1.17, 1.25–1.44, 1.33–1.62, and 1.40–1.66 times than windless slopes. Moreover, with constant wind direction, the runoff, sediment yield and associated N and P losses increased significantly with increasing rainfall intensity. When the rainfall intensity increased from 30 to 60 and 90 mm h−1, the runoff, sediment yield and associated TN and TP losses increased 2.02–2.28, 3.65–5.35, 3.96–34.39 and 4.28–8.45 times, respectively. The effects of rainfall on soil erosion and nutrient loss were significantly greater than those of wind. The loss of different forms of N and P associated with runoff and sediment was consistent with that of TN and TP losses. However, with an increase in rainfall intensity, the growth rates of runoff, sediment yield and associated N and P losses exhibited a gradually weakening trend for all wind directions. An increase in rainfall intensity enhanced the vertical vector force of wind‐driven rainfall, which increasingly decreased the angle between the rain incidence and the normal line of the horizon; therefore, the direction of the rain erosion force gradually approached that of windless conditions. [ABSTRACT FROM AUTHOR]

The Sistan region in Southeastern Iran is one of the world's most sensitive areas when it comes to sandstorms and wind erosion. One of the most influential factors in interpreting sandstorms is sand drift potential (DP), which is directly related to wind speed. Accurately, monitoring this phenomenon is still being determined, considering various temporal scales. Therefore, the main aim of this research is to analyze the trend of DP on monthly and annual scales. Our results showed that monthly variations of DP reached the highest and lowest values in July (609 VU) and January (47 VU), respectively. Blowing sand predominantly moved southeast, and the directional index fluctuated from 0.88 to 0.94. The annual DP was measured equal to 2700 VU, signifying a relatively high value when compared to other arid regions worldwide. The trend analysis results obtained from the Mann–Kendall test revealed both positive trends during the period 1987–2001 and negative ones from 2002 to 2016). However, the positive trend was found statistically insignificant. Furthermore, Sen's slope test results demonstrated that a negative trend could be observed with a steeper slope during July, September, and August, while a positive trend could be observed with a steeper pitch during November, December, and June. We recommend that land managers and stakeholders involved in controlling blowing sand using biological and physical methods should consider these trends in the Sistan region. Implementing nature-based solutions or control strategies should focus on these temporal sequences. [ABSTRACT FROM AUTHOR]

Monitoring measurements of aerosol characteristics and meteorological parameters in the lower atmospheric layer in the winter periods of 2016–2022 in Akademgorodok located in the southeast of Tomsk were used to analyze the conditions for the occurrence of extreme concentrations of submicron particles and black carbon in their composition in the surface layer. It is shown that when surface air temperature inversion and weak (down to 1.5 m/s) wind (air stagnation) occur in combination, the average aerosol characteristics under study increase by as much as a factor of three, and by as much as a factor of 7–8 relative to the seasonal averages in the case of a weak northwesterly wind. It is found that in situations with multiday air stagnation, the typical daily behavior, characterized by the afternoon minimum, changes. The concentrations increase until 15:00 LT up to the values 1.8 times larger than the nighttime minimum. On the other winter days, the maximum occurs at 10:00 LT, with the concentrations of submicron (black carbon) particles being a factor of 1.2 (a factor of 1.5) larger than the nighttime minimum. [ABSTRACT FROM AUTHOR]

Tropical cyclones (TC) are the most intense interactions between the ocean and atmosphere. Synthetic aperture radar (SAR) can capture high resolution marine atmospheric and oceanic features. Therefore, high-resolution sea surface wind information can be retrieved from SAR imagery through various methods. In this study, a new modified local gradient (ILG-Mod) method is proposed for TC wind direction retrieval. It combines SAR image smoothing and local gradient computation in the spectral domain, and changes the way to obtain the main local gradient direction in the spatial domain. Nineteen TC images acquired from Sentinel-1 SAR have been collected for validation. The proposed method is compared with the local gradients (LG) method, modified local gradients (LG-Mod) method, and the improved local gradients (ILG) method. In addition, ERA5 (the fifth generation European Centre for Medium-Range Weather Forecasts) reanalysis data and CCMP (the Cross-Calibrated Multi-Platform) data are used for validation. Statistical results show that wind directions retrieved by the ILG-Mod method are highly consistent with the validated data. Comparisons with validated data reveal bias, root mean square error (RMSE), and correlation coefficient (r) values of 17.09°, 19.63° and 0.96, respectively. The proposed ILG-mod method is therefore suitable to retrieve wind direction of TCs. [ABSTRACT FROM AUTHOR]

Pollen concentration in the atmosphere is strongly affected by changes in meteorological factors, as well as urban and suburban vegetation in the study area. The identification of atmospheric pollen sources and estimates on their concentrations are critical for the quality of life for individuals affected by pollen allergy. In this study, we evaluated the effect of wind on the concentration levels of dominant pollen types (Cupressaceae/Taxaceae, Olea europaea, Pinaceae, Poaceae, Quercus spp., and Urticaceae) recorded in the atmosphere of İzmir, Turkey's 3rd largest city, in accordance with land cover over a 3-year period. Our results showed higher pollen concentrations for all taxa on days when wind direction was from the southeast toward the city. Elevations around the city, where arboreal taxa are widely distributed, particularly contributed to pollen concentrations through winds blowing from their directions. Besides these environmental elements, plants used in urban landscaping, such as Pinaceae, Olea europaea, and Cupressaceae/Taxaceae also contributed to pollen concentrations in the city's atmosphere. Although we observed no significant transport from the northern part of the city, we have determined that the agricultural lands in the western and southern parts contribute to the pollen concentrations of Poaceae and Olea europaea. Additionally, we recorded the highest pollen transport for all taxa when wind speed was within the range of 1.6–3.3 m/s, while pollen concentrations declined in higher wind speeds. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In recent years, much attention has been paid to climate change issues. Regional climate change can be manifested in the changes in the main climate-forming factors, including wind. Studying the climatic characteristics of wind over the Caspian Sea is considered the most important task, since wind affects the variability of currents, the formation of hydrological and hydrochemical processes, and the information about the wind regime is necessary for the operation of marine organizations, the specifics of which is the dependence of all stationary and mobile objects in the sea on hydrometeorological conditions. The paper presents the analysis of the frequency of wind directions and wind speeds for individual months, seasons, years, and different gradations of wind speed. For the climatic characteristics of wind speed for consecutive 30-year periods, the comparative analysis of changes in climatological normals is carried out, and wind characteristic deviations relative to the standard reference period are revealed. [ABSTRACT FROM AUTHOR]

This paper examines the analysis of meteorological observations concerning wind direction and intensity. These observations are typically found in regular weather reports, which play a crucial role in the safe arrival and departure of aircraft, and provide vital information about current conditions along airport runways. To ensure accuracy, the locations of wind sensors along runways must be specified in routine weather reports. Furthermore, reported wind information data should be linked to the specific sections of the runway for which the data are representative. In cases where wind observations are available from multiple tracks, the relevant track information should also be included in routine weather reports, alongside the corresponding wind data. For surface wind conditions, the observations included in the METAR reports should accurately represent the entire runway, and it is important to indicate the specific runway or runway sections to which the observations pertain.

Abstract Accurate and reliable assessment of wind energy potential has important implication to the wind energy industry. Most previous studies on wind energy assessment focused solely on wind speed, whereas the dependence of wind energy on wind direction was much less considered and documented. In this paper, a copula‐based method is proposed to better characterize the direction‐related wind energy potential at six typical sites in Hong Kong. The joint probability density function (JPDF) of wind speed and wind direction is constructed by a series of copula models. It shows that Frank copula has the best performance to fit the JPDF at hilltop and offshore sites while Gumbel copula outperforms other models at urban sites. The derived JPDFs are applied to estimate the direction‐related wind power density at the considered sites. The obtained maximum direction‐related wind energy density varies from 41.3 W/m2 at an urban site to 507.9 W/m2 at a hilltop site. These outcomes are expected to facilitate accurate micro‐site selection of wind turbines, thereby improving the economic benefits of wind farms in Hong Kong. Meanwhile, the developed copula‐based method provides useful references for further investigations regarding direction‐related wind energy assessments at various terrain regions. Notably, the proposed copula‐based method can also be applied to characterize the direction‐related wind energy potential somewhere other than Hong Kong.

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.

The article is devoted to the influence of terrain, landscapes, and meteorological conditions on the establishment of air quality monitoring points in the relevant territory of the Lviv region. The main factors affecting the distribution of the concentration of pollutants in atmospheric air during the design of air monitoring stations have been determined. It was established that the level of the surface concentration of pollutants increases with an increase in the power of the emission source, the density, and size of impurity particles, and decreases with an increase in the height and radius of the orifice of the emission source, the temperature of the emission, and the rate of release of impurities from the source.

Some documented precipitation changes are associated with the hydrological cycle, including changes in the magnitude of precipitation. Studying the effect of spatial, temporal, wind speed and wind direction on precipitation magnitude at specified points within Melbourne city – Australia could reveal how they affect precipitation magnitudes. To achieve this aim, a long rainy day was adopted to record the measurements at different places and times throughout the day and night. The study method involved measuring wind speed and direction at every measuring point by using a hand-held device, while precipitation values were recorded using the nearest ground weather station. The study revealed precipitation magnitudes at night were higher than during daylight hours. On the other hand, fast winds increase precipitation magnitude, and wind direction has an effect on precipitation either increasing or decreasing depending on upwind and leeward sides, respectively. Respective to temporal parameters, precipitation magnitudes at night showed the highest. Wind speed directly affects precipitation values but the direction has a moderate effect on precipitation. Generally, spatiotemporal, wind speed and direction have a distinct effect on precipitation with a small time scale. HIGHLIGHTS A new methodology was used by selecting continuous storms which extended more than 20 h.; A long rainy day was adopted to study the effect of spatial, temporal parameters and some weather features on precipitation magnitudes at specified points.; Spatiotemporal, wind speed and direction have a distinct effect on precipitation around the greening areas within an urban area with a small time scale.;

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. [ABSTRACT FROM AUTHOR]

We study the problem of estimating a regression function when the predictor and/or the response are circular random variables in the presence of measurement errors. We propose estimators whose weight functions are deconvolution kernels defined according to the nature of the involved variables. We derive the asymptotic properties of the proposed estimators and consider possible generalizations and extensions. We provide some simulation results and a real data case study to illustrate and compare the proposed methods. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this study, we present an extension to existing numerical retrackers of synthetic-aperture radar (SAR) altimetry signals. To our knowledge at the time of writing this manuscript, it offers the most consistent retrieval of geophysical parameters compared to low-resolution mode (LRM) retracking results. We achieve this by additionally estimating the standard deviation of vertical wave-particle velocities σ v and a new parameter u x , linked to a residual Doppler in the returned radar echoes, which can be related to wind speed and direction. Including this new parameter into the SAR stack retracker mitigates sea surface height estimation errors by up to two centimeters for Sentinel-6MF SAR mode results. Additionally, we found a closed-form equation to describe u x as a function of eastward and northward wind variables, which allows mitigating the effects of this parameter on a SAR stack within level 1B processing and generating a lookup table to correct sea surface height estimates in SAR mode. This additionally opens up the door to estimating the wind speed and direction from SAR altimetry stacks. Additionally, we discuss how this new retracker performs with respect to different planned future baseline processor changes of Sentinel-6MF, namely F09 and F10, by attempting to imitate their level 2 processing. This is achieved by processing cycles 017 to 051 (nearly a full year) of Sentinel-6MF level 1A data on a global scale. We observe that the new retracking method is, on average, more accurate with respect to LRM. However, there is a slight increase in measurement noise due to the introduction of an additional parameter. To ensure that the results of the new retracker are not biased, we retrack using both the new method and the SINCS-OV ZSK retracker on Sentinel-6MF stack data produced in a Monte Carlo simulation. We analyze the simulation results with respect to accuracy, precision, and correlations between estimated parameters. We show that the accuracy of the new retracker is better than SINCS-OV ZSK but less precise, which could be related to higher correlation coefficients—especially with respect to the new parameter u x —between estimated parameters. [ABSTRACT FROM AUTHOR]

Accurate and reliable assessment of wind energy potential has important implication to the wind energy industry. Most previous studies on wind energy assessment focused solely on wind speed, whereas the dependence of wind energy on wind direction was much less considered and documented. In this paper, a copula‐based method is proposed to better characterize the direction‐related wind energy potential at six typical sites in Hong Kong. The joint probability density function (JPDF) of wind speed and wind direction is constructed by a series of copula models. It shows that Frank copula has the best performance to fit the JPDF at hilltop and offshore sites while Gumbel copula outperforms other models at urban sites. The derived JPDFs are applied to estimate the direction‐related wind power density at the considered sites. The obtained maximum direction‐related wind energy density varies from 41.3 W/m2 at an urban site to 507.9 W/m2 at a hilltop site. These outcomes are expected to facilitate accurate micro‐site selection of wind turbines, thereby improving the economic benefits of wind farms in Hong Kong. Meanwhile, the developed copula‐based method provides useful references for further investigations regarding direction‐related wind energy assessments at various terrain regions. Notably, the proposed copula‐based method can also be applied to characterize the direction‐related wind energy potential somewhere other than Hong Kong. [ABSTRACT FROM AUTHOR]