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This article proposes an approach, based on infinite Fourier series, to constructing tractable densities for the bivariate circular analogues of copulas recently coined ‘circulas’. As examples of the general approach, we consider circula densities generated by various patterns of nonzero Fourier coefficients. The shape and sparsity of such arrangements are found to play a key role in determining the properties of the resultant models. The special cases of the circula densities we consider all have simple closed-form expressions involving no computationally demanding normalizing constants and display wide-ranging distributional shapes. A highly successful model identification tool and methods for parameter estimation and goodness-of-fit testing are provided for the circula densities themselves and the bivariate circular densities obtained from them using a marginal specification construction. The modelling capabilities of such bivariate circular densities are compared with those of five existing models in a numerical experiment, and their application illustrated in an analysis of wind directions.

Roughness-induced emission from ocean surfaces is one of the main issues that affects the retrieval accuracy of sea surface salinity remote sensing. In previous studies, the correction of roughness effect mainly depended on wind speeds retrieved from scatterometers or those provided by other means, which necessitates a high requirement for accuracy and synchronicity of wind-speed measurements. The aim of this study is to develop a novel roughness correction model of ocean emissivity for the salinity retrieval application. The combined active/passive observations of normalized radar cross-sections (NRCSs) and emissivities from ocean surfaces given by the L-band Aquarius/SAC-D mission, and the auxiliary wind directions collocated from the National Centers for Environmental Prediction (NCEP) dataset are used for model development. The model is validated against the observations and the Aquarius standard algorithms of roughness-induced emissivity correction. Comparisons between model computations and measurements indicate that the model has better accuracy in computing wind-induced brightness temperature in the upwind/downwind directions or for the surfaces with smaller NRCSs, which can be better than 0.3 K. However, for crosswind directions and larger NRCSs, the model accuracy is relatively low. A model using HH-polarized NRCSs yields better accuracy than that using VV-polarized ones. For a fair comparison to the Aquarius standard algorithms using wind speeds retrieved from multi-source data, the maximum likelihood estimation is employed to produce results combining our model calculations and those using other sources. Numerical simulations show that combined results basically have higher accuracy than the standard algorithms.

Mesoscale eddies are widely distributed in the global ocean. They affect the ocean flow field and material transport, and play an important role in the energy transfer between the ocean and the atmosphere. With the development of high-resolution satellite observations, many regional studies are emerging on the coupling effects between mesoscale eddies and the atmosphere. In this study, each identified global eddy (2010–2016, about 13 million eddies) is collocated and normalized with sea surface temperature (SST, 2010–2014), sea surface wind (2010–2016), sea surface air temperature at 2 m (2010–2016), water vapor (2010–2014), evaporation rate (2010–2016), cloud liquid water (2010–2014), and rainfall rate (2010–2014). Four normalization methods are used: non-rotated normalization, and normalizations based on wind direction, flow direction, and eddy egg direction alignment. Furthermore, the eddy explained variations of the air–sea parameters are calculated to obtain their spatial distribution. The eddy explained variation ranges of the seven parameters are 24%–78%, 12%–21%, 3%–35%, 8%–22%, 9%–18%, 0–53%, and 0–58%, respectively. The influence of mesoscale eddies on the air–sea interface can be summarized as a vertical mixing mechanism. This study is novel in that it explores the overlying air–sea distribution from the perspective of glo-bal eddies. The numerical distributions of climatological air–sea parameters are determined by utilizing the multiyear composite overlying air–sea distribution over global eddies using the eddy coordinate system, and the contribution of eddies to this pattern is analyzed. This study is important for the investigation of global climate change.

The present work aims to track maximum wind velocity to capture the maximum power in a horizontal axis wind turbine using a fuzzy logic controller. The fuzzy logic equipped yaw control system consists of the following components: (i) wind direction sensor, (ii) wind turbine tower with angle position sensor, and (iii) construction of controller and driver circuit. The potentiometer resistance difference was used to track the wind direction and nacelle head position, respectively. The prototype setup was validated using real-time datasets. The accuracy of the fuzzy yaw control in track the wind direction is ± 1.5°. The proposed model is simple, low cost, and accurate positioning the nacelle head using fuzzy logic yaw control system.

Based on the orientation of the rotor, horizontal axis wind turbines (HAWTs) and vertical axis wind turbines (VAWTs) are two broad families of wind turbines in the world. The HAWT is today’s mainstream wind turbine type that has been widely used. Nevertheless, there continues to be widespread interest in VAWTs in recent years because of their unparalleled advantages over HAWTs such as simple and compact design, constant operation regardless of the wind direction, lower rotation speed, lower noise, etc. However, due to their complex unsteady aerodynamics, VAWTs are generally less efficient than HAWTs, resulting in a lower efficiency of energy conversion. To improve the airflow over the blade surface and thus enhance the aerodynamic performance of VAWTs, a wide variety of different types of flow control strategies (active or passive) have been developed and implemented over the years. The purpose of this paper is to provide a survey of recent numerical and experimental studies regarding the effectiveness of different passive flow control methods for manipulating the flow field around the SB-VAWT blade in order to enhance its aerodynamic performance. Special attention has been devoted to comparing and assessing these passive flow control techniques with respect to their underlying mechanism, effectiveness, and operational complexity. Then, identified research gaps and suggestions for future research are also presented.

In places with complex topography, the reproduction of atmospheric dynamics is challenging and resource demanding. Recently, machine learning has been successfully implemented to forecast pollution and weather variables. LSTM (long short-term memory) networks have the potential to improve the forecasting precision on different theoretical fields. Despite this advantage, they have not been widely used in the tropics for this purpose. This research aims to implement a LSTM to forecast PM2.5 and meteorological variables in a tropical mountainous city. The model was trained with 7 years of data from the local air quality monitoring network. The implemented model forecasts 42 days, evaluated using statistical scores and benchmarks. More than 95% of PM2.5 values, radiation (99%), air temperature (98%), relative humidity (95%), wind speed (94%), and the u-component (91%) have excellent or good benchmarks. The v-component and the wind direction got the lowest percentage of excellent or good values (50%). We compared our results with other models that have focused on forecasting these variables in similar places and observed that the LSTM approach results are the best, especially for PM2.5 and wind direction. We found its accuracy can be affected by rapid changes in the tendency of the data that do not occur as a consequence of the diurnal tide. The LSTM model was validated as a tool to predict meteorological variables and PM2.5 (24 h in advance) in a tropical mountainous city and can be used as a reliable input in air quality early alert systems.

Meteotsunamis pose a unique threat to coastal communities and often lead to damage of coastal infrastructure, deluge of nearby property, and loss of life and injury. The Great Lakes are a known hot-spot of meteotsunami activity and serve as an important region for investigation of essential hydrodynamic processes and model forecast requirements in meteotsunami-induced coastal flooding. For this work, we developed an advanced hydrodynamic model and evaluate key model attributes and dynamic processes, including: (1) coastal model grid resolution and wetting and drying process in low-lying zones, (2) coastal infrastructure, including breakwaters and associated submerging and overtopping processes, (3) annual/seasonal (ambient) water level change, and (4) wind wave-current coupling. Numerical experiments are designed to evaluate the importance of these attributes to meteotsunami modeling, including a “representative storm” scenario in the context of regional climate change in which a meteotsunami wave is generated under high ambient lake-level conditions with a preferable wind direction and speed for wind-wave growth. Results demonstrate that accurate representation of coastal topography and fully resolving associated hydrodynamic processes are critical to forecasting the realistic hazards associated with meteotsunami events. As most of existing coastal forecast systems generally do not resolve many of these features due to insufficient model grid resolution or lack of essential model attributes, this work shows that calibrating or assessing existing forecast models against coastal water level gauges alone may result in underestimating the meteotsunami hazard, particularly when gauging stations are sparse and located behind harbor breakwaters or inside estuaries, which represent dampened or otherwise unrepresentative pictures of meteotsunami intensity. This work is the first hydrodynamic modeling of meteotsunami-induced coastal flooding for the Great Lakes, and serves as a template to guide where resources may be most beneficial in forecast system development and implementation.

Measurements of atmospheric turbulence at a site in Ferryland (Newfoundland) during the C-FOG (Coastal-Fog) field campaign in September–October 2018 are used to study meteorological parameters, turbulence statistics, internal boundary layers, and scaling laws for turbulent mixing in the coastal zone. We observe stable/unstable shallow internal boundary layers with a region of unstable/stable stratification above with onshore flow from a relatively warm/cold sea onto the cold/heated land during the night/day. This study compares surface fluxes and other turbulence statistics as well as different scaling laws with and without fog. While both complexity of the coastal landforms and foggy conditions nominally violate assumptions underlying Monin–Obukhov similarity theory (MOST), our observations show that the non-dimensional standard deviations of the velocity components and the dissipation rate of turbulence kinetic energy obey MOST reasonably well for all measurement levels, stability conditions, and wind directions for both fog and no fog cases. However, the data scatter for the normalized dissipation rate is somewhat greater compared with the normalized standard deviations of the wind components. The bias and relatively larger scatter of normalized standard deviations for scalars in near-neutral conditions is likely associated with the underlying inhomogeneous coastal surface. According to the C-FOG data, during a fog event the moisture flux data become irregular and the latent heat flux is often negative (downward). Our observations also demonstrate poor agreement between normalized standard deviations of specific humidity with MOST for foggy conditions; its statistical dependence on the MOST stability parameter is weak at best in fog.

Good representation of turbulence in urban canopy models is necessary for accurate prediction of momentum and scalar distribution in and above urban canopies. To develop and improve turbulence closure schemes for one-dimensional multi-layer urban canopy models, turbulence characteristics are investigated here by analyzing existing large-eddy simulation and direct numerical simulation data. A range of geometries and flow regimes are analyzed that span packing densities of 0.0625 to 0.44, different building array configurations (cubes and cuboids, aligned and staggered arrays, and variable building height), and different incident wind directions ($$0^\circ $$ 0 ∘ and $$45^\circ $$ 45 ∘ with regards to the building face). Momentum mixing-length profiles share similar characteristics across the range of geometries, making a first-order momentum mixing-length turbulence closure a promising approach. In vegetation canopies turbulence is dominated by mixing-layer eddies of a scale determined by the canopy-top shear length scale. No relationship was found between the depth-averaged momentum mixing length within the canopy and the canopy-top shear length scale in the present study. By careful specification of the intrinsic averaging operator in the canopy, an often-overlooked term that accounts for changes in plan area density with height is included in a first-order momentum mixing-length turbulence closure model. For an array of variable-height buildings, its omission leads to velocity overestimation of up to $$17\%$$ 17 % . Additionally, we observe that the von Kármán coefficient varies between 0.20 and 0.51 across simulations, which is the first time such a range of values has been documented. When driving flow is oblique to the building faces, the ratio of dispersive to turbulent momentum flux is larger than unity in the lower half of the canopy, and wake production becomes significant compared to shear production of turbulent momentum flux. It is probable that dispersive momentum fluxes are more significant than previously thought in real urban settings, where the wind direction is almost always oblique.

Ocean wind fields form a significant input to ocean wave models. This study evaluates the accuracy of two major reanalysis wind fields: NCEP-NCAR reanalysis-II (NNR-II) and ECMWF ERA5 wind datasets in the marine domain of West Africa. The objective is to identify the reanalysis data that best represents the wind regimes of the sub-region for use in climate studies and ocean wave modelling. The reanalysis datasets were validated against in situ measurements from PIRATA meteorological buoys in the region. Both reanalysis datasets indicate good agreement with in situ measurements and capture the variability in the wind fields. However, ERA5 wind fields outperform the NNR-II wind fields and better represents the variability in wind fields in the region. They display higher correlation coefficients and R-squared values as well as lower bias and RMSE values for all wind components at all PIRATA buoy locations. Correlation coefficients of 0.92, 0.87, 0.94, and 0.98; R-squared values of 0.83, 0.76, 0.89, and 0.96; mean bias of −0.34±0.75 ms−1, 0.25±33.75°, 0.07 ± 0.86 ms−1, and −0.21±0.96 ms−1; and RMSE values of 0.82 ms−1, 33.75°, 0.87 ms−1, and 0.98 ms−1 were observed for ERA5 resolved wind speeds, wind directions, and zonal and meridional winds respectively. NNR-II also recorded correlation coefficients of 0.64, 0.7, 0.73, and 0.9; R-squared values of 0.19, 0.39, 0.32, and 0.79; mean bias of 0.12±1.77 ms−1, 8.91±53.43°, 0.55±2.09 ms−1, and −0.31±2.15 ms−1; and RMSE values of 1.77 ms−1, 54.17°, 2.16 ms−1, and 2.17 ms−1 for resolved wind speeds, wind directions, and zonal and meridional winds, respectively. NNR-II winds tend to highly overestimate zonal wind speeds and underestimate meridional wind speeds. Meridional winds are better predicted compared to zonal winds for both NNR-II and ERA5 winds. There was a general overestimation of lower wind speeds and underestimation of higher wind speeds on the part of both reanalysis datasets although this assertion varied with geographical location. To enhance the accuracy of resolved wind velocities and directions in the region, there is the need to improve the estimation of zonal winds in general by both NNR-II and ERA5 winds but with much efforts needed for NNR-II. In effect, ERA5 reanalysis winds better describe the wind regime of West Africa for climate studies and ocean wave modelling.

The use of isolated vaulted canopy roofs (VCR) for schools, emerging shelters, sports and recreational centers, among others, is quite popular worldwide. However, the design of such structures to resist wind effects is usually a problem due to the scarce technical information available in the literature. As an alternative resource, in the present research, wind effects on this type of structures immersed in the atmospheric boundary layer (ABL) are studied numerically by using the 3D Reynolds averaged Navier Stokes (RANS) approach considering different aspect ratios and wind directions. Since wind tunnel testing information for these structures is limited, experimental results of a Gabled Canopy Roof (GCR) as well as some experimental results for VCR are used as a validation source for the numerical models. Based on this validation, the simulation results of the mean wind loads of VCR appear to be reasonably good. The numerical results are used to study the mean net-pressure coefficients (CPN) over the VCR models and to establish the theoretical bases for coding them. It was found that the CPN are very sensitive to the VCR curvature and wind direction (90°, 75°, and 60°), and to a less extent to the plan and elevation geometric ratios. Further, it was also found that the use of GCR CPN for the design of VCR, as is usually done in professional practice, could lead to important differences in the magnitude of pressure coefficients and pressure distribution regarding actual VCR CPN and its distribution. A detailed analysis of the CPN for VCRs is presented.

Focusing on the effect of street morphology on the ventilation efficiency, this paper presents 3D computational fluid dynamics (CFD) simulations of airflow and pollutant dispersion within urban-like three-way intersections, four-way intersections and roundabouts. The steady-state Reynolds-averaged Navier-Stokes (RANS) k-ε turbulence model is adopted and eight directions of the approaching wind are considered. The ventilation efficiency is evaluated using the ventilation indices purging flow rate (PFR) and the net escape velocity (NEV). Results show the sensitivity of the ventilation efficiency to the type of intersection, to the wind direction and to the number of branches. Specifically, the ventilation efficiency of the investigated three-way intersections is found to be better than that of the other intersections, especially when the angle between the streets is large, while that of roundabouts is also considerable, even with a similar average wind velocity, among the cases evaluated in this paper. Further, the influence of the wind direction for the three-way and four-way intersections is greater than that on roundabouts. Studies on the ventilation efficiency at urban intersections are not common in the literature and this work may help urban planners to better design such hub nodes of urban traffic, where traffic-related pollutants are not easily dispersed, thus avoiding harm to the health of pedestrians and surrounding residents.

Air temperature forecasting has been a vital climatic factor required for different applications in many areas such as energy, industry, agriculture, health, environment, and meteorology. This study compares the performances of two static neural networks (NNs) used for the prediction of hourly ambient air temperatures in a coastal area of Turkey. Thirteen parameters from Land Surface Diagnostics and Surface Flux Diagnostics Collections from the MERRA-2 reanalysis dataset including pressure, surface specific humidity, wind speed, wind direction, air density at surface, evaporation, planetary boundary layer height, total precipitable water vapor, total precipitation, total cloud area fraction, total column ozone, greenness fraction, and leaf area index were used as input parameters for the models. Feed-Forward Neural Network (FFNN) and Cascade Forward Neural Network (CFNN) models were applied to forecast hourly ambient air temperatures at 2 m height from the surface. The results indicated that the most accurate and reliable predictions were obtained by the CFNN model with 30 neurons, while the lowest prediction performance was obtained by the FFNN model with 5 neurons. The root mean squares error (RMSE), mean absolute error (MAE) and coefficient of determination (R-2) values for training (and testing) of the CFNN model with 30 neurons were 0.358 (0.376), 0.273 (0.283), and 0.997 (0.992), respectively, whereas the same parameters were 0.430 (0.447), 0.334 (0.343), and 0.996 (0.989) for the FFNN model with 5 neurons. The CFNN model had a lower RMSE and MAE, and a higher R-2 than the FFNN model. These results showed that increasing the number of neurons of hidden layers from 5 to 30 provided better model performance.

The reinforcement design is the most critical and time-consuming process in structural design of hyperbolic cooling tower (HCT) shell due to the complicate internal force distributions and arbitrary direction of wind load. The internal forces of a representative HCT under different loads, including gravity, wind, thermal actions of winter and summer, horizontal and vertical earthquakes with reasonable load values, were illustrated individually and compared systematically. It was found that all the internal forces follow uniform or simple latitude distribution except the wind effects and, therefore, the structural reinforcement design is an easy process when wind load is excluded. However, the participation of wind load make it unpredictable where the most unfavorable internal force condition locates in latitude direction. It is even inevitable that many wind directions must be involved to combine with other loads in reinforcement design calculation. In this study, a virtual uniform distribution is proposed to replace the actual irregular distribution of wind effects, and then, the design calculation procedure is simplified greatly because the multiple wind direction involvement and calculation on the whole latitude shell are both avoided. Meanwhile, the incensement of reinforcement amount got from the simplified procedure is about only 2% and the distribution is more reasonable.

Correcting the forecast bias of numerical weather prediction models is important for severe weather warnings. The refined grid forecast requires direct correction on gridded forecast products, as opposed to correcting forecast data only at individual weather stations. In this study, a deep learning method called CU-net is proposed to correct the gridded forecasts of four weather variables from the European Centre for Medium-Range Weather Forecast Integrated Forecasting System global model (ECMWF-IFS): 2-m temperature, 2-m relative humidity, 10-m wind speed, and 10-m wind direction, with a forecast lead time of 24 h to 240 h in North China. First, the forecast correction problem is transformed into an image-to-image translation problem in deep learning under the CU-net architecture, which is based on convolutional neural networks. Second, the ECMWF-IFS forecasts and ECMWF reanalysis data (ERA5) from 2005 to 2018 are used as training, validation, and testing datasets. The predictors and labels (ground truth) of the model are created using the ECMWF-IFS and ERA5, respectively. Finally, the correction performance of CU-net is compared with a conventional method, anomaly numerical correction with observations (ANO). Results show that forecasts from CU-net have lower root mean square error, bias, mean absolute error, and higher correlation coefficient than those from ANO for all forecast lead times from 24 h to 240 h. CU-net improves upon the ECMWF-IFS forecast for all four weather variables in terms of the above evaluation metrics, whereas ANO improves upon ECMWF-IFS performance only for 2-m temperature and relative humidity. For the correction of the 10-m wind direction forecast, which is often difficult to achieve, CU-net also improves the correction performance.

This study investigates a turbulent wind-induced dynamic behavior of a soundproof tunnel structure with un-symmetric shape. The combined computational fluid dynamics (CFD) and fluid-structure interaction (FSI) simulations proposed in this study may allow us not only to investigate realistic wind effects but also to provide various parameters for the structural design. The CFD and FSI simulation were verified in comparisons with a full-scale experiment of soundproof frames. Subsequent simulation results present that displacements, stresses, and shear forces induced from the FSI analysis are heavily dependent on curved shapes and the wind direction of the soundproof tunnel. From parametric results, the technique proposed in this study is computationally efficient in characterizing behaviors for complex structures such as soundproof tunnels with un-symmetric shapes.

We give an exploratory analysis of daily 10 meter wind speed averages for all weather stations in Alberta, Canada with observations from January 2008 to May 2019. The data is provided by the Alberta Climate Information Service (ACIS), Alberta Agriculture and Forestry. Our goal is to categorize the wind patterns in Alberta for the purpose of incorporating this knowledge into spatiotemporal modeling of wind across Alberta. We aim to identify the relationship between prevalent wind directions, geographical locations and seasons. We use seasonal clustering to identify stations with similar statistical properties. The linkage criterion between two sites is based on the Euclidean distance of the two-dimensional moments of their wind speed distributions. We identify four types of behavior emerging through the year, and give a seasonal summary of each behavior. We interpret the wind behaviors in terms of the terrain.

Over the years, many organizations across the globe have conducted various studies pertaining to air pollution and its ill effects. The results of these studies substantially conclude that a plethora of people succumbs to the adversities caused by the ever-increasing air pollutants. In this investigation, M5P, random forest (RF)- and Gaussian process (GP)-based approaches are used to predict the tropospheric ozone for Amritsar, Punjab state of India, metropolitan area. The models proposed were based on ten input parameters viz. particulate matter PM2.5, particulate matter PM10, sulphur dioxide (SO2), nitrogen dioxide (NO2), nitric oxide (NO), ammonia (NH3), temperature (T), solar radiation (SR), wind direction (WD) and wind speed (WS), while the tropospheric ozone (O3) was an output parameter. Three most popular statistical parameters such as correlation coefficient (CC), mean absolute error (MAE) and root mean square error (RMSE) were used for the assessment of the developed models. In comparison, it was found that better results were achieved with random forest-based model with CC value as 0.8850, MAE value as 0.0593 and RMSE value as 0.0772 for testing stage. The suggested models are expected to save cost of instrument, cost of labour work, time and contribute to greater accuracy. A result of sensitivity investigation concludes that the solar radiation is the most influencing parameter in estimating the actual values of O3 based on the current data set.

Quantification of the terms in the transport equations of turbulence kinetic energy k and heat in the real urban roughness sublayer is inherently difficult due to variation of terms with thermal stability, wind speed, wind direction, and horizontal heterogeneity of most urban sites concerning morphometric parameters and land use. There is also a paucity of observations in the real urban environment, specifically pertaining to the heat budget, which motivate such budget analyses to understand the physical processes, inform urban development, and parametrize microclimate models. The budget terms of k and heat in the urban roughness sublayer were quantified using field observations conducted in the Reek Walk, Guelph, Canada, inside a quasi two-dimensional urban canyon located at the University of Guelph, from 15 July 2018 to 5 September 2018. The budget terms were analyzed under four stability classes, from thermally unstable to stable conditions, and under different wind speeds, from very low to high wind speed conditions. The budget terms were further analyzed under varying wind directions in eight sectors with respect to the canyon axis. For k, the budget terms quantified were storage, advection, buoyant production/consumption, shear production/consumption, turbulent transport, and dissipation. It was found that the main transport mechanism for k was driven by the turbulent transport that relocated k from the shear layer above roof (speculated but not measured) to the urban canyon, where it was balanced by shear production/consumption and dissipation terms. The advection term had lower magnitude to other terms but was greater in magnitude than the buoyant production/consumption term. For heat, the budget terms quantified were storage, advection, and flux divergence. It was found that the main transport mechanism for heat was driven by advection, where either warm or cold air masses were transported to the urban canyon depending on wind direction. The advection was found to be balanced by flux divergence. For both k and heat the storage terms were at least one order of magnitude smaller than other budget terms. For k it was found that with decreasing wind speeds, the residual (unexplained) portion of the budget increased, suggesting more difficulties in the Reynolds decomposition approach and budget apportionment in such cases. The findings for the k budget were in agreement with previous studies, while the findings for the heat budget could inspire further investigations. It was noted that the advective transfer mechanisms for heat could be overlooked in simplified urban microclimate models, but such transfer mechanisms need to be accounted for.

The article develops a new method of spatiotemporal modelling of possible locations of ragweed areas and estimation of pollen emission intensity of these areas. The method uses spline approximation with automatic computations of typical pollen production models in the R language. It applies a number of approaches eliminating the uncertainty of various parameters that should be taken into consideration for this modelling. The authors have consolidated some experimental data on the amount of Ambrosia pollen in the air depending on environmental conditions, and found some new possible ragweed pollen sources on the territory of the city of Vinnytsia and the adjacent villages. The study used spline approximation of the first (lines) and second (parabolas) orders were applied. Data of bi-hourly pollen concentrations were obtained at the pollen monitoring site of National Pirogov Memorial Medical University (Vinnytsia). To perform the approximation, wind speed, wind direction and relative humidity were taken into the consideration. The authors used the polar coordinate system to formalize the problem. PRA Finder computer program was developed, which has been successfully tested on real data and for which a certificate of copyright registration has been obtained in Ukraine. As a result of the method application, the known areas were corroborated and new possible plots were revealed. The comparison of the forecasted areas for 2014 with the actual ones showed a close match. However, it should be taken into consideration that the proposed method is viable only when applied under the condition that the wind velocity does not exceed 4 m/sec and the relative humidity is between 20 and 90%.

Tropospheric ozone (O3), as an air pollutant is increasing at an alarming rate in urban areas. The concentration of ozone is affected by precursor pollutants, such as particulate matter (PM10, PM2.5), carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon dioxide (CO2), and nitric oxide (NO), and meteorological parameters, such as air temperature (AT), relative humidity (RH), global solar radiation (SR), wind direction (WD), and wind speed (WS) of the area. Ozone is a secondary pollutant and strong oxidizing agent injurious to human health. The present study aimed to identify the most crucial factors that influence ozone formation and to develop an ozone prediction model using artificial neural network with optimal inputs. The data obtained from Limbayat, real-time air pollutants monitoring station of Surat city, have been used to evolve the model, followed by feature selection techniques, namely, sensitivity analysis, Boruta algorithm, and recursive feature elimination algorithm (RFE). Finally, 6/14 influencing parameters have been identified using an attribute selection approach. Interestingly, “hour of the day” was found the most prominent and governing parameter among the 14 parameters after applying various feature selection techniques in the experiments. The result showed that the efficiency of the prediction model was 79.4% when six parameters were used in the machine learning algorithms.

Sea surface wind (SSW) observations from a newly developed “Black Pearl” wave glider, the Chinese-French Oceanography Satellite (CFOSAT), the HY-2A microwave scatterometer, and a recently released high-resolution atmospheric reanalysis (ERA5) are evaluated with respect to in-situ buoy observations (115.46°E, 19.85°N) from the South China Sea. Buoy observations from June to November 2019 are used to evaluate the wind estimates from the different platforms. The comparisons show that the HY-2A and CFOSAT scatterometer wind speeds have mean root mean square errors (RMSEs) of approximately 1.6 and 1.6 m/s, respectively, and the corresponding mean wind direction RMSEs are approximately 19° and 17°, which indicates that these satellite retrievals meet the requirements of design engineering missions. The wind speed and wind direction RMSEs of ERA5 are approximately 1.9 m/s and 33°, respectively. The correlation coefficients between the HY-2A, CFOSAT, and ERA5 wind speeds and the buoy observations are 0.86, 0.85, and 0.84, respectively, and the corresponding coefficients of the wind direction are 0.98, 0.98, and 0.93, respectively, at a 95% confidence level. However, the wind sensor in the wave glider provides relatively poor-quality observations compared with the buoy measurements and has higher wind speed and wind direction RMSEs of 2.9 m/s and 50.1°, respectively. Taylor diagrams are utilized to illustrate comprehensive wind comparisons between the multiplatform observations and buoy observations. The results help identify the basic biases in SSWs among different products and enhance confidence in the future use of SSW data for studies of upper ocean dynamics and climate analysis. Suggestions are also offered to help improve the design of next-generation wave gliders.

Heat pump water heaters are highly efficient hot water supply systems that effectively utilize the heat of outdoor air via heat pump technology. Many studies have been conducted to optimize the design and operation of heat pump water heaters from the perspective of climate change mitigation. Air-source heat pump water heaters, which absorb heat from the outdoor air and emit cold exhaust, can also be expected to alleviate the urban heat island effect; however, this has not been studied extensively. To estimate the impact of cold exhaust on building-scale climate, we conducted a multipoint measurement of the outdoor thermal environment around a low-rise apartment building equipped with air-source heat pump water heaters, in both summer and winter. Observations showed a substantial cooling effect that decreased air temperatures by 1°C within the site boundary on summer nights when multiple heat pump water heaters operated concurrently. The analysis revealed that the sensitivity of the ambient temperatures to cold exhaust depends strongly on local atmospheric conditions. The most influential factor was the wind direction: the sensitivity increased significantly when the exhaust outlet location was at the lee side of the building. Naturally, the wind speed also affected the sensitivity, which tended to be higher when the wind speed was lower. The convective stability near the ground surface, however, showed no significant influence over the sensitivity.

The purpose of this study is to identify and characterize individual sources of pollutants such as PM10, SO2, NOx, and CO in the urban area in Karadeniz (Turkey) using the bivariate polar plots method. In addition, the relationship between the meteorological conditions and the pollutants was determined based on correlation analysis in the region. Bivariate polar plots are a graphical method used to demonstrate the dependence of pollutant concentrations on wind direction measured at stations. Thanks to these graphics, resource types and properties can be determined. Wind flow and pollution data were used to provide information on wind and pollutant interactions in the study area. As a result of the study, it was founded that the main source of pollutants is intensive anthropogenic activities such as urban, street traffic, agricultural activities, and natural resources. It has been concluded that the highway in the region is not an important source of pollutants. In addition, the pollutant relations were examined with meteorological data, and it was discovered that temperature and relative humidity were effective for all pollutants.

A coherent detection Doppler wind lidar (CDWL) system simulation for both ground-based and space-based platforms is established in this study by using the PcModwin and BACKSCAT software to construct an atmospheric model in autumn and winter. Based on the light detection and ranging (LIDAR) equation, the balanced coherent detection principle, the error theory, and the calculation of signal-to-noise ratio (SNR), the truncation ratio and the single pulse energy under different conditions were optimized. Meanwhile, the wind speed error and the wind direction error of the space-based platform were determined. The simulation results indicate that at an orbit height of 400 km, with the pulse energy at 225 mJ, and the telescope aperture at 1 m, the space-based coherent wind LIDAR could detect the wind speed at the altitude of 0–5 km, and the maximum horizontal wind speed error was 3.7 m/s.

The measurement accuracy of wind direction and wind speed is very important to the unmanned sailboat control, but the mature mechanical wind sensor and ultrasonic wind sensor both have great defects to be applied to the unmanned sailboat. Inspired by previous works on neural networks, we propose a low-cost, real-time, and robust wind measurement system based on computer vision (CV). This CV-wind-sensor includes an airflow rope and a camera, which can be simply deployed on the sailboat. We implement a prototype system on the FPGA platform and run a series of experiments that demonstrate the promising performance of our system. For example, the absolute measurement loss of the CV sensor in this paper is basically kept below 0.4 m/s, which shows a great advantage of measurement accuracy compared with the mechanical sensor.

This study aims to characterize the wind regime in the state of Rio de Janeiro (SRJ), Brazil, and relate it to the physiographic aspects and influence of meteorological systems, based on 12 Brazilian National Institute of Meteorology (INMET) automatic meteorological stations, for the period from 2008 to 2019. Box plots are used for the assessment of monthly and hourly patterns. Similarity was found among the stations at a monthly and daily scale. On the monthly scale, there were two well-defined cycles, a minimum (between February and July) and a maximum (between August and November), both greatly influenced by frontal systems (FS). On the hourly scale, there was a decrease in the intensity of the wind during the night and early morning, followed by an intensification in wind patterns between 11 h to 20 h, except for the Arraial do Cabo and Petropolis stations. The stations that are located near the Serra do Mar ridge and Sepetiba Bay presented prevailing N, SW, W, ENE and NE winds, respectively. Arraial do Cabo station is located near the state’s shoreline and in the resurgence region, with predominant NE and E wind directions. Petropolis station is located in the Mountainous region, with NNW and NW wind directions. The logistic regression model identified that the rainfall, temperature (air, maximum and wet bulb) and relative humidity contributed to the probability of wind velocity above the 70 percentile (WS70) occurring in the SRJ. The South American Convergence Zone (SACZ) and FS acted as triggers of the WS70 in different areas of the state, where the influence of the SACZ was stronger than that of the FS.

Wind energy plays a major role in meeting the world’s growing power demand, due to which wind speed forecasting is essential for power system management, energy trading and maintaining the balance between consumption and generation for a stable electricity market. In this article, three different types of predicting techniques have been implemented for estimating wind speed by means of different meteorological parameters. Group method of data handling (GMDH), multi linear regression (MLR) and artificial neural network (ANN) models have been developed. For these models, data sets of 05 years (12 datasets from each year) were collected from the National Renewable Energy Laboratory (NREL). Five different types of input variables, which are ambient temperature (Ta), atmospheric pressure (Pr), wind direction (WD), relative humidity (RH) and precipitation (Pc) were selected as independent variables in all models. The collected wind speed (Wv) is selected as output or dependent variable. In this study, 48 sets of data were picked for training process and 12 datasets were selected for testing. The performances of models were examined using statistical parameters such as RMSE, MAPE and R2. MLR, GMDH and ANN techniques accurately performed with values of correlation coefficient (R) being obtained as 0.90552, 0.95542 and 0.97617 respectively. Comparative study of all models reveals that out of these three techniques, ANN performs the best. In the ANN model, the values of RMSE, MAE and R2 obtained were 0.17476, 0.12984 and 0.95210 respectively, which are optimal results when compared to those of other models. After ANN, GMDH performed better than MLR. Above analysis reveals that the wind speed was predicted with the highest accuracy by the neural technique.

This paper presents predictions of run-up scale on coastal structures using a multiple linear regression equation. The Wonnapha Beach seawall in Chon Buri, Thailand, is used as the study site. Instead of exactly measuring wave run-up on structures, run-up levels were observed and categorized into five scales (from 0 to 4). Run-up scales, together with the data of tidal levels, offshore waves, and winds, are analyzed using multiple linear regression. A new parameter, “effective wind speed,” is introduced by merging wind direction and speed into one parameter explaining more physical meaning of wind influences on wave run-up. The regression formula can reproduce observed run-up scales with an $$R_{{}}^{2}$$ of 0.589 and the mean absolute error of 0.62. For this study, the predictive formula can be applied as a real warning for the near future since simultaneous data is used; however, applying this approach while using forecasted input data would be a useful and timely warning tool for alerting people who live near coastal defenses about potentially hazardous run-up conditions.

A large amount of dust particles produced by the wind in an open-air pile is one of the important reasons for air pollution. Studying the law of dust diffusion in local areas is of great significance for the atmospheric particulate control. In this study, a pile of sodium carbonate in a large open-air pile in Weifang, China, is regarded as the research object. The dispersion characteristics of dust particles around the pile under the action of unidirectional wind are studied through wind tunnel test and numerical simulation. The complex atmospheric environment is simplified as unidirectional wind, and the influence of different wind speeds on the dispersion of particles with diverse sizes in the pile is studied. Although a large gap exists between the assumption and the real atmospheric environment, this study provides a reference for the evaluation of the pollution scope of blowing dust and prevention and control of pollution. Results show that a high-concentration range of the dust exists near the pile behind the wind direction and may continue to spread to the height due to the influence of a whirlpool, and the dispersion distance and width can increase with the increase in wind speed. The increase in particle diameter increases the kinetic energy loss of particles for the fluid. Under the same starting speed, the dispersion distance of dust decreases with the increase in particle diameter. With the increase in particle diameter, the dust concentration distribution presents the trend of interior hollowing and high-concentration area fragmenting.

In part I, the clear air echo in front of the squall line is caused by turbulence diffraction, which makes the ZDR echo characteristics different from particle scattering. To study the turbulence deformation phenomenon that is affected by environmental wind, the turbulence-related method is used to analyze the characteristics of three-dimensional turbulence energy spectrum density, and the parametric model of turbulence integral length scale and environmental wind speed is established. The results show that the horizontal scale of turbulence is generally larger than the vertical scale. The turbulence is nearly isotropic in the horizontal direction, presenting a flat ellipsoid with the vertical orientation of the rotation axis when there is no horizontal wind or the horizontal velocity is small. When horizontal wind exists, the turbulence scale increases along the dominant wind direction. The turbulence scale is positively correlated with the wind speed. The power function is used to fit the relationships of turbulence integral length scale and horizontal wind speed, which obtains the best fitting effect, and the goodness of fit (GF) is above 0.99 in each direction. The deforming turbulence can cause 8–9 dB ZDR anomalies in the echo of dual polarization radar, which the ratio of scales in the dominant wind and the vertical direction of deforming turbulence (Lu/Lw) is around 4.3. The variation in ZDR depends on the turbulence shape, orientation and the relative position between turbulence and radar. The shape of turbulence derived from radar detection results is consistent with that of the parametric model, which can provide a parametric scheme for turbulence research. The results reveal the mechanism of abnormal ZDR echo caused by deforming turbulence.

Wind energy is a highly effective renewable energy source that can be used to alleviate environmental problems worldwide. However, the variability of wind inhibited the development of wind power generators such as limited directional conditions, restrictions pertaining to the installation sites, occurrence of mechanical failures, and limited wind speed ranges. In here, to overcome these limitation, we developed a stackable disk-shaped wind-rolling triboelectric nanogenerator (DWR-TENG) to generate energy from omnidirectional wind. Its disk-shaped design ensures that the device does not involve any restrictions related to the wind direction or installation. Furthermore, lightweight charged spheres in the device can reduce the occurrence of mechanical failures and expand the wind speed range (operating in 6.5–17.5 m/s) for the generator operation. The performance of the DWR-TENG was evaluated considering various design parameters by conducting experiments and an FEM simulation. Specifically, the DWR-TENG can be easily stacked in several layers, thereby exhibiting an enhanced charging efficiency.

Over the data sparse oceanic region, ocean surface winds from scatterometers on-board satellites play a crucial role to make accurate Numerical Weather Prediction model-based analysis. In the present study, ocean surface winds derived from the recently launched SCATSAT-1 for the year 2017 over the Indian Ocean are validated against the winds from the Moored and RAMA buoys. The validation results for ASCAT winds for the same period are also presented. The comparison of SCATSAT-1 (ASCAT) winds against offshore OMNI buoy winds indicates that the mean differences for wind speed and wind direction are 0.5 m/s and – 1.0° (0.39 m/s and – 4.0°), and the RMSEs are 1.44 m/s and 23.0° (1.17 m/s and 25.0°), respectively. For the coastal OMNI buoys, the SCATSAT-1 (ASCAT) indicated that the mean differences for wind speed and wind direction are 1.6 m/s and – 5° (1.15 m/s and – 0.4°), and the RMSEs are 2.65 m/s and 46 deg, (2.1 m/s and 51°), respectively. Quantified differences are almost similar for the comparison of SCATSAT-1/ASCAT with RAMA buoys. Overall, the quantified differences in the wind speed and direction between the SCATSAT-1 and buoys are closer to the satellite’s mission specifications of 1.8 m/s and 20°, respectively, and at par with the ASCAT accuracies. The capability of SCATSAT-1 winds to capture a cyclonic storm OCKHI during December 2017 also is depicted. The present study endorses that the SCATSAT-1 data are accurate and reliable and can be used in Numerical Weather Prediction (NWP) models and also for other applications.

Studies related to the combined effect of different atmospheric oscillations on the ocean surface waves are limited. The present study focuses on the change in wave climatology due to the combined effect of Boreal Summer Intra-Seasonal Oscillation (BSISO) and El Nino Southern Oscillation (ENSO) using 40 years of reanalysis data on the Asian Summer Monsoon Region (ASMR). Composite analysis of surface wind, significant wave height, wind-sea, swell and mean wave period are analysed corresponding to different ENSO–BSISO phase combinations. The study showed noteworthy differences in wave parameters when ENSO–BSISO phases are analysed together. In El Nino–BSISO combined phase, enhancement of north-easterly wind causes the advancement in the reversal of wind direction (north-easterly to monsoon enhanced south-westerly) and disrupted propagation of positive wind and wave anomalies. Conversely, during La Nina–BSISO co-occurrence, south-westerlies are enhanced and as a result, the negative north-easterly anomalies are disrupted. In all the ENSO–BSISO combined phases, maximum wave height variability occurs at the South China Sea owing to the combined action of the north Indian Ocean (NIO) and Western North Pacific (WNP) surface wind forcing. High fluctuations in Tm over the NIO and WNP are observed during different ENSO–BSISO phase combinations. The phase relationship of Hs with Tm and the significant height of wind-sea (Hsw) and swell (Hss) are assessed to understand the propagation of swells. Due to the effect of multiple atmospheric perturbations, significant changes in Hs occur over the coastal regions of the NIO.

Wind load acting on a building in presence of one or more interfering buildings usually varies when compared to isolated buildings. This effect of variations in wind loads due to presence of other interfering building is termed interference effect. This effect on wind loads is mainly governed by parameters such as geometry, orientation of the structures with respect to wind direction and upstream terrain conditions. The provisions made in design codes and standards with regard to the effect of interfering structures are inadequate for proper estimation of wind loads. As such, the structural designers opt for wind tunnel experiments to get accurate wind loading on these structures. Computational fluid dynamics (CFD) and artificial neural network (ANN) approaches are other alternative methods emerging in recent years. This paper discusses on the ANN approach to predict the pressure coefficients on building faces due to interference, which may be used further in calculating wind loads. Mean pressure coefficients on each face of principal building (building under investigation) are predicted and analyzed for 45o wind angle, in presence of an interfering building at different locations upstream of the principal building. There is a good agreement between the predicted results and those obtained using wind tunnel. The results show that ANN approach can serve as a better tool to predict wind loads at preliminary stage and make an initial estimation for different interfering cases compared to methods like CFD and experimental which are complex, time consuming and costlier.

The cooling performance of air-cooled condenser (ACC) is susceptible to adverse impacts of ambient winds. In this work, three kinds of lateral double-layered deflectors installed under the ACC platform are proposed to weaken the unfavorable effects of cross winds. Through CFD simulation methods, the main parameters of thermo-flow performances of a 2×660 MW direct dry cooling system are obtained, by which it can be concluded that the deflectors can effectively reduce the inlet air temperatures while enhance the mass flow rates of upwind fans due to the guiding effect, especially at high wind speeds, while the improvement of cooling capacity of ACCs in the 0° wind direction is weak. The inclined-vertical deflectors are superior to others in performance improvement of ACCs for all cases, which can reduce the turbine back pressure by 12.15% when the wind speed is 12 m/s, so they can be applied to the performance enhancement of ACCs under windy conditions in practical engineering.

In this study, snow events in the Marmara region were identified for the 2009–2018 period, and then the times at which a vortex causing the formation of a sea-effect snow (SES) band was seen over the Black Sea were determined from Terra, MSG-2 and MSG-3 satellite imagery. During this period, the SES band was found only between 30 and 31 January 2012 over the western Black Sea. This band maintained its dominance over the region for approximately 24 h. The cause and effects of this band and the precipitation that resulted were investigated. Meteorological data published by 12 airports, 16 meteorological stations and 5 radiosonde stations belonging to five countries were analysed within the two-day period to describe the relevant meteorological conditions. In addition, sea surface temperature data and synoptic charts of the upper atmospheric levels were investigated. According to the results, the maximum amount of snow (24 cm) was observed at the Inebolu Station (in Turkey). As a result of the effect of the vortex-type SES band moving over the Marmara and western Black Sea regions, a total of 102 flights belonging to a private airline company were cancelled, sea transportation was disrupted, and vehicular accidents occurred in Istanbul. In all radiosonde measurements, inversion layers were observed between the surface and the 700 hPa level, and wind direction changes occurred mostly between 0° and 50° within the same level range. The temperature difference between the sea surface and the 850 hPa level reached 25.8 °C where the vortex-type SES band core was located. In the synoptic level charts, the dominance of a low-pressure centre during the two-day period was observed in the area where the vortex-type SES band was formed.

Concentration of road dust re-suspended by passing vehicles can be measured using fine-dust measuring devices installed at various heights of a flux tower. In this study, simulations were performed to analyze the distribution of dust plumes along the height of a flux tower according to its distance from the center of the road, the vehicle speed, wind direction, and wind speed. Simulation results were compared with field measurements of re-suspended road dust collected by a flux tower during the passage of a van-type mobile laboratory vehicle to confirm the accuracy of the simulation. A parametric study was then conducted using the simulation of the mobile lab vehicle to evaluate the effects of vehicle speed, wind direction, and wind speed. To accurately capture re-suspended road dust concentrations, the flux tower must be at least 4.5, 5.1, 5.8, and 6.2 m high when located 2, 4, 6, and 8 m, respectively, from the road center. The results of this study are expected to serve as guidelines in determining the location and height of flux towers when measuring re-suspended road dust.

Modern geodetic terrestrial technology involves automatized observations based on an application of robotic total stations. The benefit of such an advanced geodetic instrument is in its high precision sustainability, manipulation simplicity, maximum flexibility, one-person operability and personal error reduction. The paper is devoted to the analysis of results achieved by using such a modern geodetic instrument in the process of diagnostics of the geometrical position of piers structures built at Klaipeda Seaport. The analysis was carried out to find out the influence of the air, water temperature and wind direction, and speed on the measurement results that should be used in the global seaport monitoring system. Based on the results obtained from this research work a method of displacements was proposed in relation with water temperatures up to wind speed 10 m/s and with the application of the normal distributed displacement measured values of points in order to evaluate the measurement error regarding the temperatures.

We consider nonparametric regression for bivariate circular time series with long-range dependence. Asymptotic results for circular Nadaraya–Watson estimators are derived. Due to long-range dependence, a range of asymptotically optimal bandwidths can be found where the asymptotic rate of convergence does not depend on the bandwidth. The result can be used for obtaining simple confidence bands for the regression function. The method is illustrated by an application to wind direction data.

In the present study, analysis of surface weather parameters and energy fluxes has been carried out over the Indian Antarctic stations Maitri and Bharati. For this purpose, weather parameters taken from three different reanalysis datasets are compared against automatic weather station (AWS) observations during the period from 1991 to 2010. Results suggest that the European centre for medium-range weather forecasts Reanalysis-Interim (ERAI) yields better estimates and further used for long-term analysis over the period from 1979 to 2017. It is found that air temperature at Maitri is always lower than that at Bharati. This difference might be due to the Maitri being located at 100 km inland in comparison with Bharati which is near the coast. The wind direction analysis indicates the predominance of South-East and North-East winds at Maitri and Bharati, respectively. Moreover, monthly net energy fluxes climatology has been prepared using ERAI reanalysis. During January (−14 W/m2) and December (−9 W/m2) net energy fluxes at Maitri are negative (energy flow from surface to atmosphere), whereas at Bharati, during January (7.68 W/m2) and December (15.34 W/m2) net energy fluxes are positive. Net energy flux at Maitri is strongly and negatively influenced by snow layer temperature whereas at Bharati, snow layer temperature influences net energy flux during winter.

Deserts are ideal places to develop ground-mounted large-scale solar photovoltaic (PV) power station. Unfortunately, solar energy production, operation, and maintenance are affected by geomorphological changes caused by surface erosion that may occur after the construction of the solar PV power station. In order to avoid damage to a solar PV power station in sandy areas, it is necessary to investigate the characteristics of wind-sand movement under the interference of solar PV array. The study was undertaken by measuring sediment transport of different wind directions above shifting dunes and three observation sites around the PV panels in the Hobq Desert, China. The results showed that the two-parameter exponential function provides better fit for the measured flux density profiles to the near-surface of solar PV array. However, the saltation height of sand particles changes with the intersection angle between the solar PV array and wind direction exceed 45°. The sediment transport rate above shifting dunes was always the greatest, while that around the test PV panels varied accordingly to the wind direction. Moreover, the aeolian sediment transport on the solar PV array was significantly affected by wind direction. The value of sand inhibition rate ranged from 35.46% to 88.51% at different wind directions. When the intersection angle exceeds 45°, the mean value of sediment transport rate above the solar PV array reduces to 82.58% compared with the shifting dunes. The results of our study expand our understanding of the formation and evolution of aeolian geomorphology at the solar PV footprint. This will facilitate the design and control engineering plans for solar PV array in sandy areas that operate according to the wind regime.

Three archived reanalysis wind vectors at 10 m height in the wind speed range of 2–15 m/s, namely, the second version of the National Centres for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSv2), European Centre for Medium-Range Weather Forecasting Interim Reanalysis (ERA-I) and NCEP-Department of Energy (DOE) Reanalysis 2 (NCEP-2) products, are evaluated by a comparison with the winds measured by moored buoys in coastal regions of the South China Sea (SCS). The buoy data are first quality controlled by extensive techniques that help eliminate degraded measurements. The evaluation results reveal that the CFSv2 wind vectors are most consistent with the buoy winds (with average biases of 0.01 m/s and 1.76°). The ERA-I winds significantly underestimate the buoy wind speed (with an average bias of −1.57 m/s), while the statistical errors in the NCEP-2 wind direction have the largest magnitude. The diagnosis of the reanalysis wind errors shows the residuals of all three reanalysis wind speeds (reanalysis-buoy) decrease with increasing buoy wind speed, suggesting a narrower wind speed range than that of the observations. Moreover, wind direction errors are examined to depend on the magnitude of the wind speed and the wind speed biases. In general, the evaluation of three reanalysis wind products demonstrates that CFSv2 wind vectors are the closest to the winds along the north coast of the SCS and are sufficiently accurate to be used in numerical models.

Due to the frequent urban air pollution episodes over Kuwait recently, decision-makers and government agencies are struggling for sustainable strategies to optimize urban land use and land cover change (LUCC) and improve air quality. This article is targeting to identify the underlying relationships between dust concentration variations and LUCC, using the numerical modelling approach. The RegCM4 and WRF-CHEM models were employed to explore the impacts of land use change over Kuwait to be Evergreen Broad-leaf instead of Desert. Results reveal that both models performed good estimate in two severe dust storm cases, as they detected these cases with reasonable concentrations compared to the reanalysis data with positive correlation, and the overall mean dust concentrations in the target area declined by approximately 6–50% using RegCM4 and 25% with WRF in both dust episodes. Besides, the LUCC affected the wind directions around the area of LUCC; however, it had no impact on the wind’s strength. These results suggested that LUCC caused by an increase in long trees might be an important factor for the PM10 concentration reduction in Kuwait and would need to be investigated over a longer period.

In this work, we report metals concentrations in 80 PM10 samples collected at four sites in the Mexico City Metropolitan Area (MCMA): Tlalnepantla (NE), Xalostoc (NE), Merced (C), and Pedregal (S), during the dry/cold season (October to January) for the 2004–2014 period. Mean PM10 mass concentration (66.1 µg m−3) significantly exceeds the annual mean air quality guidelines recommended by the World Health Organization. The statistical analysis of concentration data and meteorological parameters allows us to recognize the importance of wind intensity speed (Wsp) and wind direction conditions in the enrichment of PM concentrations. The proximity and magnitude of the emitting source is also relevant for PM concentration. Such conditions favored that higher metal concentration was recognized at the north of the studied area. By means principal component analysis (PCA) was difficult to identify the groups of metals associated with specific sources (anthropogenic and geogenic) given the high complexity of the study area and the long period of time evaluated. Metal concentration trend shows an important positive trend for Pt, V and Cr, while PM10, Ni, Cu, Ag and Sb show a trend of moderate increase. In contrast, Pb and Co registered a strong percentage reduction, while Hg, Mn, As and Cd show a slight reduction, probably resulting from the implementation of regulatory measures and influenced by urban changes associated at the north of the studied area. The results of this research provide information that should be considered for evaluating the impact of anthropogenic sources and applying regulatory measures to control emissions.

Heliostats represent a significant proportion of the cost of central tower concentrating solar power systems; hence, there is a need to reduce the cost of these. One of the challenges faced in doing this is to ensure that any new design is still able to cope with the loads imposed upon it, particularly the aerodynamic loads. Numerous studies have reported the aerodynamic loadings encountered by heliostats for multiple operational conditions. However, these studies only extracted and reported these values without relating their findings of the variation in wind loads with heliostat orientation to the airflow characteristics around the structure. Besides, there is a marked absence of studies that rigorously explore in any detail the wind incidence effect and the impact that has on the aerodynamic behaviour of a heliostat. In this study, computational fluid dynamics was utilized to investigate the effect of wind incidence angles on a heliostat operating at varying tilt angles, to better characterize the aerodynamic loading of these structures and to relate these loads to the wind flow field around the heliostat. The results, validated against wind tunnel test data, showed that the tilt and wind incidence angle had a significant influence on the aerodynamic coefficients that varied strongly across the multiple operational conditions investigated. Moreover, the airflow field around the heliostat structure showed markedly different behaviour characteristics with the change in wind incidence angle. Considering a full range of tilt angles between 90° and − 90°, and incidence angles ranging from 0° to 90° with an angular resolution of 11.25°, the work delivers a fuller characterization of the lift, drag, base overturning moment and hinge moment coefficients than previously available. In achieving this, it delivers a generalized correlation for each of the coefficients based on the heliostat’s orientation with respect to the wind. When paired with existing gust loading relationships, these formulations provide a useful analytical tool to assess structural loads on a heliostat.

Urban wind environments are closely related to air pollution and outdoor human comfort. The urban natural ventilation potential (NVP) is an important factor in urban planning and design. However, for ventilation studies on urban scales, neither macroscale numerical simulations (i.e., WRF, MM5, etc.) nor microscale computational fluid dynamics (CFD) simulations can conduct efficient analyses. Based on the similarity between water flows and airflows, an efficient approach is proposed in this paper to map the urban NVP. Through integrating the urban terrain model, urban form model, and prevailing wind pressure model, an airflow digital elevation model (AF-DEM), which represents the resistance to airflow and can be used for a hydrological simulation, is generated and applied to evaluate the urban airflow patterns under different terrain, urban form and ambient wind conditions. The objective was to develop a simulation platform that can efficiently predict the distribution of natural ventilation corridor and NVP. The stream network calculated through the simulation is regarded as potential ventilation corridors within the city, and an index calculated from the coverage rate of wind corridors (CRW) is proposed for evaluating the relative NVP. Taking Nanjing as a case study, 8 AF-DEMs based on different wind directions and wind speed conditions are generated, and their corresponding ventilation corridor maps are constructed. The results are in good agreement with the empirical evidence, indicating that the hydrological model, though a rudimentary approximation of the actual airflows, was effective in revealing the natural ventilation corridor and characterize the relative NVP. Moreover, the implementation of this novel method is simple and convenient, and it has great application potential and value in urban design and management.

Air pollution has been a rising concern of the 21st due to its effects to public health. Air Monitoring Stations are state-of-the-art equipment used to measure airborne pollutants concentration i.e. carbon monoxide, nitrogen oxide, sulphur dioxide, particulate matter (PM10) and ozone (O3), as well as the meteorological parameters (i.e. ambient air temperature, relative humidity, wind speed and wind direction). Effects of climate change will affect the ambient temperature and humidity, which may induce a direct effect on air quality. In light of this, feed forward artificial neural network was employed to simulate the dynamic variations of PM10 and O3 with relative humidity, temperature, and windspeed data being the inputs under 12 different training algorithms. Based on the results obtained, Bayesian regularization with 12 hidden neurons is the optimized network structure, with mean absolute percentage error in testing dataset of O3 and PM10 at 51.31% and 36.49%, respectively. The models performed better in O3 prediction as it is a photochemical reaction where ozone concentration varies according to temperature, the effect of meteorological parameters is significant. On the other hand, PM10 is not heavily dependent on meteorological parameters as the diversity of particulate matter components where most of its sources are dormant to changes in climate.

Mountainous topography and solar elevation angle have a significant influence on the wind circulation and energy exchange over a complex region. Here, the seasonal variation of mountain-valley wind circulation (MVWC) and surface layer parameters have been presented over a hilly station of northeastern India. For the study, quick-response sonic anemometer data have been utilized. The solar elevation angle varies from 85° to 43° in different seasons and controls the MVWC over the terrain. MVWC was dominated in winter (WN), pre-monsoon (PM), and post-monsoon (PMN) seasons, while the synoptic wind predominates the wind direction over the terrain in monsoon (MN). In WN and PM, nighttime heat transmission from the atmosphere to the ground was −10 to −20 W m−2, while the minimum in MN and PMN was −3 to −7 W m−2. The net seasonal daily mean sensible heat flux (H) values from the surface to the atmosphere were 42 W m−2, 58 W m−2, 35 W m−2, and 34 W m−2 in WN, PM, MN, and PMN. In MN, the average daytime temperature was 8 °C, 0.5 °C, and 3 °C higher and at night was 9.5 °C, 2 °C, and 4.5 °C higher than the WN, PM, and PMN. In WN, PM, and PMN, the momentum flux had a higher value of about 2 to 3 h after the maximum value in H was attained at the 6-m and 10-m levels. The daytime turbulence kinetic energy was higher by 99%, 56%, 72%, and 93% than the nighttime during WN, PM, MN, and PMN.

Windbreaks are a major component of and play an important role in agroforestry ecosystems. One of the most important functions is to reduce wind erosion and protect crops from wind damage. In this study, we discussed a method to evaluate the efficiency of wind protection by a windbreak. First, we chose a windbreak model that was only related to the structural parameters of the windbreak. There were windbreak average widths (w), optical porosities (θo), barrier heights (h) and distances from the barrier along the wind direction xh. Then, we established a method that was used to extract the inputs of the windbreak model by using remote sensing (RS) and a geographic information system (GIS). The windbreak average width (w) was extracted by using object-based image analysis with an average accuracy of 72.428%. The optical porosity (θo) and barrier height (h) were estimated by using quantitative remote sensing technology with average accuracy of 86.9592% and 79.0497%, respectively. Finally, the efficiency of windbreak wind protection was evaluated by using this windbreak model based on RS and GIS technology. The results indicated that this windbreak model can be used to evaluate the efficiency of the wind protection of the windbreak by considering the properties of the windbreak itself. This study can provide a useful guide for studying the wind protection provided by windbreaks based on spatial and temporal scales using RS and GIS.