Wind Characteristics Research Articles

A Comparative Study of Dynamic Responses of Coastal Long-Span Bridge Under Typhoon with Different Crossing Paths

Coastal long-span bridges are intensively constructed along the western Pacific coast. While the coast is frequently disturbed by typhoons. Given that a coastal long-span bridge will experience a number of typhoons during its service life, it is important to have an understanding of the wind fields formed by typhoons with different crossing paths and corresponding structural dynamic responses. Wind and acceleration data of the Xihoumen Bridge under the influence of onshore, offshore, and landfalling typhoons were acquired by the structural health monitoring system (SHMS). Wind field features of typhoons with three types of paths were compared. The landfalling Typhoon In-fa was used to investigate the differences of wind-induced structural dynamic responses under the influence of each typhoon structural region. The covariance-driven stochastic subspace identification (SSI-COV) was used to identify the modal parameters of the bridge. The results revealed that wind field features at bridge site varied with typhoon crossing paths. The SSI-COV accurately identified the structural frequencies of first 15 vertical modes using the acceleration data under the influence of spiral rainband. The first eyewall has the most detrimental wind conditions to the bridge because the stiffening girder vibrated violently in this region.

Harnessing the Wind Down Under: Applying the UNCLOS Framework to the Regulation of Offshore Wind by Australia and New Zealand

This article considers how the 1982 United Nations Convention on the Law of the Sea (UNCLOS) applies to the production of energy from wind in waters under national jurisdiction, with a focus on the nascent regulatory frameworks for offshore wind in Australia and New Zealand. It explores the particular characteristics of offshore wind—which raise different regulatory issues than traditional offshore activities such as fishing and oil and gas exploitation—and explores how key provisions of UNCLOS will affect the way in which coastal states regulate offshore wind in their waters. Against this backdrop, it considers how the international law framework might apply in the specific context of Australia and New Zealand, where domestic legal frameworks are currently being established to regulate vast and valuable offshore wind resources.

Meteorological and environmental factors that impact pollen counts, allergenicity, and thresholds: A scoping review.

Background: Pollen is a key source of aeroallergens responsible for allergic rhinitis, conjunctivitis, and asthma. Objective: The goal of this scoping review was to summarize current available literature on the factors that affect pollen counts, allergenicity, and thresholds that induce symptoms in individuals who were sensitized. Methods: Several databases showed no published articles with a similar scope as of January 2022. A search of these data bases yielded 373 articles for assessment. These were then reviewed for relevance, and articles were selected to demonstrate the breadth of available data on pollen counts, allergenicity, and thresholds that induce symptoms in individuals who were sensitized. Additional articles were identified through examination of bibliographies of search-identified articles. Results: Several environmental factors have shown a correlation with pollen counts and allergen load, including the distance from the source, wind characteristics, pollen size, terrain, urban environments, air composition (particulate matter, CO₂ levels, ozone, NO₂), and weather conditions (humidity, thunderstorms, precipitation). Pollen thresholds at which symptoms were induced varied by study, pollen type, symptom, disease, and location. In addition, there was heterogeneity in study designs, threshold definition, and outcome measures. Conclusion: This scoping review demonstrates the plethora of variables that influence the relationship between pollen and the symptoms of allergic diseases. Analysis of the available data sheds light on the complex interaction between environmental and biologic factors that affect pollen's role in allergic diseases and provides guidance on multiple areas for further investigation.

Distinct Meteorological Mode Associated with High-PM2.5 Episodes in Seoul, South Korea

Abstract In this study, high–particulate matter (PM2.5) pollution episodes were examined in Seoul, the capital city of South Korea, which, based on the episode characteristics, were influenced by a distinct meteorological mode, long-range transport (LRT), from two-level meteorological observations: surface and 850–500-hPa level. We performed two-step statistical analysis including principal component (PC) analysis of meteorological variables based on the observation data, followed by multiple linear regression (MLR). The meteorological variables included surface temperature (Tsfc), wind speed (WSsfc), and the east–west (usfc) and north–south (υsfc) components of wind speed, as well as wind components at 850-hPa geopotential height (u850 and υ850, respectively) and the vertical temperature gradient between 850 and 500 hPa. Our two-step analysis of data collected during the period 2018–19 revealed that the dominant factors influencing high-PM2.5 days in Seoul (129 days) were upper-wind characteristics in winter, including positive u850 and negative υ850, that were controlled by the presence of continental anticyclones that increased the likelihood of LRT of PM2.5 pollutants. Regional-scale meteorological variables, including surface and upper-meteorological variables on normal and high-PM2.5 days, showed distinct covariation over Seoul, a megacity in the eastern part of northeast Asia with large anthropogenic emissions. Although this study examined only two atmospheric layers (surface and 500–850 hPa), our results clearly detected high-PM2.5 episodes with LRT characteristics, suggesting the importance of considering both geographical distinctiveness and seasonal meteorological covariability when scaling down continental to local response to emission reduction.

Categorization of High‐Wind Events and Their Contribution to the Seasonal Breakdown of Stratification on the Southern New England Shelf

AbstractHigh‐wind events predominantly cause the rapid breakdown of seasonal stratification on the continental shelf by the end of October. In particular the timing of events leads to considerable interannual variability in the stratification breakdown with a standard deviation of 15 days. Although previous studies have shown how coastal stratification depends on local wind‐forcing characteristics, the locally observed ocean forcing has not yet been linked to regional atmospheric weather patterns that determine the local wind characteristics. Establishing such a connection is a necessary first step toward examining how an altered atmospheric forcing due to climate change affects coastal ocean conditions. Here, we propose a categorization scheme for high‐wind events that links atmospheric forcing patterns with changes in stratification. We apply the scheme to the Southern New England shelf utilizing observations from the Ocean Observatories Initiative Coastal Pioneer Array (2015–2022). Impactful wind forcing patterns occur predominantly during early fall, have strong downwelling‐favorable winds, and are primarily of two types: (a) Cyclonic storms that propagate south of the continental shelf causing anticyclonically rotating winds, and (b) persistent large‐scale high‐pressure systems over East Canada causing steady north‐easterly winds. These patterns are associated with opposite temperature and salinity contributions to destratification, implying differences in the dominant processes driving ocean mixing based on a high‐wind pattern's overall strength and wind direction steadiness. The high‐wind event categorization scheme allows a transition from solely focusing on local wind forcing to considering realistic atmospheric weather patterns when investigating their impact on stratification in the coastal ocean.

Evaluation of XGBoost vs. other Machine Learning models for wind parameters identification

Wind energy is one of the most promising renewable energies. But wind is a quite unstable resource due to its continuous variation and random nature. This uncertainty affects the production cost. Therefore, accurate forecasting of wind and energy is very interesting for energy markets. In this work, we test a recent and powerful intelligent technique, extreme gradient boosting (XGBoost), for wind prediction. The forecasting models of some wind features with XGBoost are compared with Support Vector Regression (SVR), Gaussian Process Regression (GPR) and Neural Networks (NN) models. Specifically, the three features predicted are the active power generated by the turbine, the wind speed, and the wind direction. The results conclude that these techniques are useful for wind and energy forecasting, with XGBoost being the most outstanding one, especially for short-term predictions.

Development of design typhoon profile for offshore wind turbine foundation design in Southern China

For offshore wind development in Southern China, typhoon represents the extreme environmental loading condition to be considered in the design of the turbine structures. For the geotechnical design of the foundations, it is specified by the design code that the effect of cyclic loading on the soil strength and stiffness must be considered. This necessitates the complete time history of loads on the foundation experienced during a design typhoon. Idealised storm profiles with staged mean wind speeds and significant wave heights are commonly used as input to time-domain simulations to generate the foundation load history. However, no such design typhoon profile is provided in the Chinese design standard which creates considerable difficulty and uncertainty in the design process. In this paper, the methodology and the rationale that were applied to derive the design typhoon profiles for an offshore wind project in Guangdong province are presented. The wind and wave characteristics generated by typhoon are described in detail and the implications for the foundation design are discussed. To demonstrate application of the proposed typhoon profile, a case study for the ULS design of a suction bucket jacket is presented.

Physical mechanism of electrode coatings to suppress wire particle’s firefly motion under DC stress and selecting criterion for in situ applications

The Wire metal particles in gas-insulated equipment such as GIS (Gas-Insulated Substation) and GIL (Gas-Insulated transmission lines) under DC stress present a special firefly motion behaviour, namely a long-term levitation on the surface of conductors. However, the electrode coating, as a key device available to suppress the movement of particles, lacks of a theoretical basis as well as an effective selecting criterion to suppress wire particle’s firefly. In this paper, a test platform is established to obtain the charge dynamics and ionic wind characteristics of the wire particles on the coated electrodes. Experimental results illustrate that, the polarity alternation of wire particle charge due to the space charge accumulation near the wire particles as well as the ionic wind generated by the wire particles, is the key impact factor upon firefly motion. The coated electrodes will reduce the surface conduction current and thereby suppress the particle corona discharge, consequently suppressing the firefly motion by limiting the charge acquisition and ionic wind speed of the wire particles. Based on the above research, a selecting criterion of electrode coating material is further proposed. Electrode coatings is primarily expected to increase the corona onset voltage of the wire particles on the coating surface, so as to lower the ionic wind at the end of the particles less than 1 m s−1. Regarding the high voltage conductor, the coating material with relatively large conductivity is recommended, while for the grounded enclosure, the coating material with smaller conductivity is preferable as to constrain the particles adhere to the enclosure, thereby preventing the firefly motion of wire particle. Both the clarified physical mechanism and the proposed selecting criterion of electrode coating presents theoretical and quantitative guidance for DC GIL design from a specific point of view in suppressing the firefly motion.

Multi-instrument study of a spread-F event at Arecibo linked to solar wind variations

Using five diverse data sets, we demonstrate that apparently local ionospheric spread-F activity, observed with an HF radar and the Arecibo Observatory (AO) Incoherent Scatter Radars (ISR) under geomagnetically quiet conditions, is likely the local manifestation of a mesoscale or larger ionospheric response to relatively weak solar wind activity. The solar wind activity included a weak pressure pulse and Interplanetary Magnetic Field (IMF) realignment events. The mid-latitude spread-F activity was observed with a 4.42 MHz radar located near AO and the dual-beam 430 MHz AO ISRs. Additionally, the Coupling, Energetics and Dynamics of Atmospheric Regions (CEDAR) Madrigal Global Navigation Satellite System-Total Electron Content (GNSS-TEC), the NASA OMNI, and the SuperMAG datasets were used to establish the (mesoscale/global) wide-context of the local, deep-context radar results. While the ISR event appears to be “classical” local spread-F, often attributed to Perkins-like plasma instabilities, the HF radar reveals large ionospheric structures coincident with the ISR event. However, that the apparently AO-local event was part of a very dynamic mesoscale event that lasted for over ten hours, is shown via independent AO-sector and AO-conjugate sector keograms constructed using detrended vertical TEC (delta-vTEC) data. The keograms reveal a prominent F-region feature that appears to propagate from west-to-east and then back to the west passing over AO twice. The ISR manifestation of this mesoscale event is indistinguishable from decades of similar ISR results which were assumed to be strictly “local”. The strong non-local (mesoscale) properties revealed by the delta-vTEC keograms suggest a possible space weather influence. Study of the relevant NASA OMNI solar wind and superMAG magnetometer data points to modest solar wind features including IMF realignment that may have electrodynamically launched, with little time delay, the observed mesoscale ionospheric events. Given the apparent rapid ionospheric response to the solar wind features, we suggest that limited latitudinal scale, prompt penetration electric fields (PPEF), associated with highly-localized partial ring current activity, be considered in the system-of-systems context of our observations. In any case, the need to examine magnetospheric/ionospheric electrodynamics across a wide range of instruments and data sets is demonstrated.

Physical intelligence-based working mode adaptable triboelectric nanogenerator for effective wind energy harvesting in broad range

Triboelectric nanogenerators (TENGs) offer a promising solution for harvesting wind energy, which is a renewable source of natural energy that can be dissipated. However, designing a wind energy harvester presents a significant challenge due to the dynamic characteristics of ambient wind, including irregularity and broad wind speed ranges. In this work, we suggested a spontaneous working mode changeable TENG (SM-TENG) designed for effectively harvesting wind energy within a broad range of wind speeds ranging from a breeze of 2 m/s to a gale of 25 m/s. The SM-TENG consists of a physical intelligence-based rotator that can switch contact and separation mode depending on the input wind speed. Under a breeze, the SM-TENG can generate triboelectricity by fluttering a dielectric film based on vertical contact separation (F-TENG) due to intentional pressure imbalance. Meanwhile, when wind speed exceeds a certain wind speed of 5 m/s, the SM-TENG begins to harvest more wind by rotating the rotator based on the sliding contact separation (R-TENG), indicating that the SM-TENG can harvest wind by selecting the appropriate working mode. We adopted the Taguchi method to enhance performance by identifying the optimal parameters, affecting the desired output. In addition, the SM-TENG can successfully power a light-emitting diode display and a Bluetooth thermometer within a wide range of wind speeds. Therefore, this work may provide a strategy for designing a wind energy harvester capable of harvesting in a wide range of wind as well as 0could contribute to the feasibility of Internet of Things through distributed energy.

Review article: A European perspective on wind and storm damage – from the meteorological background to index-based approaches to assess impacts

Abstract. Wind and windstorms cause severe damage to natural and human-made environments. Thus, wind-related risk assessment is vital for the preparation and mitigation of calamities. However, the cascade of events leading to damage depends on many factors that are environment-specific and the available methods to address wind-related damage often require sophisticated analysis and specialization. Fortunately, simple indices and thresholds are as effective as complex mechanistic models for many applications. Nonetheless, the multitude of indices and thresholds available requires a careful selection process according to the target sector. Here, we first provide a basic background on wind and storm formation and characteristics, followed by a comprehensive collection of both indices and thresholds that can be used to predict the occurrence and magnitude of wind and storm damage. We focused on five key sectors: forests, urban areas, transport, agriculture and wind-based energy production. For each sector we described indices and thresholds relating to physical properties such as topography and land cover but also to economic aspects (e.g. disruptions in transportation or energy production). In the face of increased climatic variability, the promotion of more effective analysis of wind and storm damage could reduce the impact on society and the environment.

Predicting the Continuous Spatiotemporal State of Ground Fire Based on the Expended LSTM Model with Self-Attention Mechanisms

Fire spread prediction is a crucial technology for fighting forest fires. Most existing fire spread models focus on making predictions after a specific time, and their predicted performance decreases rapidly in continuous prediction due to error accumulation when using the recursive method. Given that fire spread is a dynamic spatiotemporal process, this study proposes an expanded neural network of long short-term memory based on self-attention (SA-EX-LSTM) to address this issue. The proposed model predicted the combustion image sequence based on wind characteristics. It had two detailed feature transfer paths, temporal memory flow and spatiotemporal memory flow, which assisted the model in learning complete historical fire features as well as possible. Furthermore, self-attention mechanisms were integrated into the model’s forgetting gates, enabling the model to select the important features associated with the increase in fire spread from massive historical fire features. Datasets for model training and testing were derived from nine experimental ground fires. Compared with the state-of-the-art spatiotemporal prediction models, SA-EX-LSTM consistently exhibited the highest predicted performance and stability throughout the continuous prediction process. The experimental results in this paper have the potential to positively impact the application of spatiotemporal prediction models and UAV-based methods in the field of fire spread prediction.

Numerical Study of the Flow Characteristics of Downburst-like Wind over the 3D Hill Using Different Turbulence Models

With the rapid development of computational fluid dynamics (CFD) technology, it has been widely used to study the wind field characteristics of downbursts in mountainous areas. However, there is little guidance on the selection of different turbulence models for simulating downburst wind fields over hills using CFD, and few comparative studies have been conducted. This paper used nine turbulence models to simulate the wind field of a downburst over a 3D quadratic ideal hill. The simulated values of average and transient winds were compared with wind tunnel test data, and the flow characteristics at different moments under a downburst were analyzed. The flow characteristics in the wake region of the downburst over the hill are also quantitatively analyzed using the proper orthogonal decomposition (POD) method. The results show that approximately 85% of the results from the LES and REA models fall within a 30% error range, so the large eddy simulation (LES) model and the realizable k-ε model (REA) are more accurate in simulating the mean wind field, and the transient wind field simulated by the LES model is also in good agreement with the experimental data. In addition, this paper reveals the evolution mechanism of the transient wind field structure over a hill model under a downburst and finds that the first-order mode obtained by POD may be related to the acceleration effect on the hilltop.

Effects of spatiotemporal correlations in wind data on neural network-based wind predictions

This paper investigates the influence of incorporating spatiotemporal wind data on the performance of wind forecasting neural networks. While previous studies have shown that including spatial data enhances the accuracy of such models, limited research has explored the impact of different spatial and temporal scales of input wind data on the learnability of neural network models. In this study, convolutional neural networks (CNNs) are employed and trained using various scales of spatiotemporal wind data. The research demonstrates that using spatiotemporally correlated data from the surrounding area and past time steps for training a CNN favorably affects the predictive performance of the model. The study proposes correlation analyses, including autocorrelation and Pearson correlation analyses, to unveil the influence of spatiotemporal wind characteristics on the predictive performance of different CNN models. The spatiotemporal correlations and performances of CNN models are investigated in three regions: Korea, the USA, and the UK. The findings reveal that regions with smaller deviations of autocorrelation coefficients (ACC) are more favorable for CNNs to learn the regional and seasonal wind characteristics. Specifically, the regions of Korea, the USA, and the UK exhibit maximum standard deviations of ACCs of 0.100, 0.043, and 0.023, respectively. The CNNs wind prediction performances follow the reverse order of the regions: UK, USA, and Korea. This highlights the significant impact of regional and seasonal wind conditions on the performance of the prediction models.

Variations in global zonal wind from 18 to 100 km due to solar activity and the quasi-biennial oscillation and El Niño–Southern Oscillation during 2002–2019

Abstract. Variations of global wind are important in changing the atmospheric structure and circulation, in coupling of atmospheric layers, and in influencing the wave propagations. Due to the difficulty of directly measuring zonal wind from the stratosphere to the lower thermosphere, we derived a global balance wind (BU) dataset from 50∘ S to 50∘ N and during 2002–2019 using the gradient wind theory and SABER temperatures and modified by meteor radar observations at the Equator. The dataset captures the main feature of global monthly mean zonal wind and can be used to study the variations (i.e., annual, semi-annual, ter-annual, and linear) of zonal wind and the responses of zonal wind to quasi-biennial oscillation (QBO), El Niño–Southern Oscillation (ENSO), and solar activity (F10.7). The same procedure is performed on the MERRA-2 zonal wind (MerU) to validate BU and its responses below 70 km. The annual, semi-annual, and ter-annual oscillations of BU and MerU have similar amplitudes and phases. The semi-annual oscillation of BU has peaks around 80 km, which are stronger in the southern tropical region and coincide with previous satellite observations. As the increasing of the values representing QBO wind, both values of representing BU and MerU (short for BU and MerU) change from increasing to decreasing with the increasing height and extend from the Equator to higher latitudes. Both BU and MerU increase with the increasing of the values of multivariate ENSO index (MEI) and decrease with increasing F10.7​​​​​​​ in the southern stratospheric polar jet region below 70 km. The responses of winds to ENSO and F10.7 exhibit hemispheric asymmetry and are more significant in the southern polar jet region. While above 70 km, BU increases with the increasing of MEI and F10.7. The negative linear changes of BU at 50∘ N are absent in MerU during October–January. The discussions on the possible influences of the temporal intervals and sudden stratospheric warmings (SSWs) on the variations and responses of BU illustrate the following: (1) the seasonal variations and the responses to QBO are almost independent on the temporal intervals selected; (2) the responses to ENSO and F10.7 are robust but slightly depend on the temporal intervals; (3) the linear changes of both BU and MerU depend strongly on the temporal intervals; (4) SSWs affect the magnitudes but do not affect the hemispheric asymmetry of the variations and responses of BU at least in the monthly mean sense. The variations and responses of global zonal wind to various factors are based on BU, which is derived from observations, and thus provide a good complement to model studies and ground-based observations.

Determination of Basic Wind Speed for the Design of Buildings in Cambodia

Basic wind speed is a very essential parameter used for conversion into wind loads on building structures. In Cambodia, the information on basic wind speed remains uncertain due to insufficient fundamental studies on the wind characteristics associated with regional climatic conditions. The aim of this paper is to assess and discuss the basic wind speeds for structural wind-resistant design in Cambodia by using statistical and probabilistic approaches. The datasets have been collected from National Centers for Environmental Information datasets, National Oceanic and Atmospheric Administration under World Meteorological Organization, and Mekong River Commission in forms of hourly wind speeds. The hourly wind speeds were then statically converted to 3-second gusts speeds using Gaussian Distribution Transformation. The extreme value distributions namely, Gumbel and Gringorten were used to analyze the extreme speed in accordance with a return period. The results showed that with a return period of 10 to 1000 years, the basic wind speed varies from a minimum of 22m/s to a maximum of 53m/s, respectively. These results provided a new aspect over traditionally uncertain basic wind speed selection and can be an alternative for the estimation of wind loads for the design of building structures in Cambodia.

Assessment of Wind Power Potentials in Chittagong, Bangladesh Using Weibull Distribution Function

Wind resource assessment is a key stage in planning for a wind energy project to predict the output power. Therefore, a full understanding of the wind characteristics at the potential site is required. This study examines the potential wind power of twelve meteorological stations in the Chittagong division over a tenure of ten-year ranges from 2010 to 2019. The statistical analyzer as the Weibull two-parameter distribution method has been utilized to determine the characteristics of wind speed and wind energy potential at the selected locations. The "WRPLOT" software has also been used to display the wind direction and the corresponding wind speed direction. Among these stations, Ambagan has a high wind factor, ranging from 2.1 to 3.2 m/s. The highest wind speed for Ambagan station was recorded in April (3.26 m/s) and the lowest was in November (1.06 m/s) compared to other selected stations. The Weibull form parameter (k) and scale parameter (c) at Ambagan station had the highest values of 1.5 and 3.5 m/s, respectively. With additional analysis and observation of these parameters, it became possible to predict the location in Chittagong with the greatest wind energy potential. In terms of wind direction, the prevailing wind direction of all stations is generally in the south. Furthermore, the results concluded that all of the analyzed parameters considered for wind behavior were numerically higher for Ambagan in contrast to other stations of the Chittagong division. This study clearly indicates that the Ambagan station should be used to maximize the potential of wind energy to attain the future goals of sustainable development for Bangladesh.

Statistical Characteristics of Local Circulation Winds Observed using Climate Data in the Complex Terrain of Chilgok, Gyeongbuk

Statistical Characteristics of Local Circulation Winds Observed using Climate Data in the Complex Terrain of Chilgok, Gyeongbuk

Field measurement study on classification for mixed intense wind climate in mountainous terrain

The wind in mountainous terrain is a mixed system, including large-scale synoptic wind and local-scale periodic thermally-driven wind. Placing all kinds of intense winds into one group may misestimate the mountain wind field's extreme wind speed and wind parameters. So, there is an urgent demand for classifying intense winds into homogenous groups. For this reason, a classification framework and extraction program for intense winds in mountain terrain according to their wind speed and temperature features is undertaken. A series of local-scale periodic thermally-driven winds, sudden intense winds, and large-scale cooling windstorms are identified and extracted from the long-term field measurements in a mountain area. Further, the mean and fluctuating characteristics of various intense winds are compared. The time–frequency changes of turbulent kinetic energy in several typical strong wind processes are studied through the evolutionary power spectrum. Remarkable differences are founded in wind parameters for intense winds with different scales.

Feasibility analysis of underground flow battery storage in bedded salt rocks of China

Flow batteries are a promising method for large-scale energy storage. This paper proposes an underground flow battery storage (UFBS) system that uses a salt cavern as an electrolyte reservoir and combines wind and solar power. The types of flow batteries suitable for UFBS were investigated. According to the distribution characteristics of wind and solar energy and available salt caverns, potential areas for UFBS were identified, located in eastern, southern, and northwestern China. Taking the Jintan salt mine in Jiangsu Province as an example, a 220 MW/1100 MWh dual-cavern UFBS system is proposed. The effective volume of a single cavern is 170,000 m3, and the design cycle life is 20 years. A numerical simulation model was established to analyze the long-term stability of the system under different operating conditions. It was found that after 20 years, the maximum roof settlement and waist deformation were 0.113 m and 0.224 m, respectively, and the volume loss rate was <1.78 %. The system had good stability. Finally, the techno-economic profits of the UFBS system were calculated and the problem of permeability was examined. This study provides a novel and feasible solution for large-scale energy storage in salt-rich regions of China.