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In view of the problems of complex process and weak representation in regular atmospheric wind field detection, this paper adopts deep learning method, uses global high-altitude meteorological detection data, and establishes a deep learning model for calculating wind direction and speed with different altitudes and temperatures by using keras software package. The model is verified by using third-party independent sounding data from a meteorological observatory in Shanghai. The calculation accuracy of the model above 2000 m is 0.9830 and the value of loss function is 0.0482. The accuracy under 2000 m is 0.9164 and the value of loss function is 0.0377. There are significant differences in the performance of the model between under 2000 meters and above 2000 meters due to surface friction. The model shows that wind direction and speed of different height layers can be calculated by using only height and temperature at the same height. This model can also be used to check whether the quality of regular wind detection work is good or not with big old data.

Due to its maneuverability and agility, the shipborne high-frequency surface wave radar (HFSWR) provides a new way of monitoring large-area marine dynamics and environment information. However, wind direction ambiguity is problematic when using monostatic shipborne HFSWR for wind direction inversion. In this article, an unambiguous wind direction measurement method based on wind direction interval limitation is proposed. The two first-order spectral wind direction estimation methods are first presented using the relationship between the normalized amplitude differences or ratios of the broadened Doppler spectrum and the wind direction. Moreover, based on the characteristic of a small wind direction estimation error in a large included angle between the spectral wind direction and the radar beam, the wind direction interval is obtained by counting the distribution of radar-measured wind direction within this included angle. Furthermore, the eliminated ambiguity of wind direction is transformed to judge the relationship between the wind direction interval and the two curves, which represent the relationship between the spreading parameter and the wind direction. Therefore, the remote sensing monitoring of ocean surface wind direction fields can be realized by shipborne HFSWR. The simulation results are used to evaluate the performance of the proposed method and the multi-beam sampling method for wind direction inversion. The experimental results show that the errors of wind direction estimated by the multi-beam sampling method and the equivalent dual-station model are large, and the proposed method can improve the accuracy of wind direction measurement. Three widely used wave directional spreading models have been applied for performance comparison. The wind direction field measured by the proposed method under a modified cosine model agrees well with that observed by the China-France Oceanography Satellite (CFOSAT).

For horizontal-axis wind turbines, wind turbines typically alignment nacelle to the wind using yaw system, realizing max energy capture. If the wind turbine’s nacelle has a large error to the wind, the captured wind energy loss will be large, and it will also cause an increase in the load of the unit, which will pose a major risk to the safety of the wind turbine. Affecting the wind measurement error in addition to the performance of the sensor itself, the installation position of the wind measurement equipment also accounts for an important factor, this paper on the basis of the calculation of fluid dynamics simulation results, through the lateral and longitudinal comparison of the simulation results, pointed out the best installation location of the wind direction sensor, optimize the loss caused by wind measurement error of wind turbine in design, and provide guidance for the installation of the wind direction sensor on site.

HF radar systems have the potential to measure the wind direction, in addition to surface currents and wave fields. However, studies on HF radar for wind direction determination are rare in the scientific literature. Starting with the results presented in Saviano et al. (2021), we here expand on the reliability of the multiannual wind direction data retrieved over two periods, from May 2008 to December 2010 and from January to December 2012, by a network of three SeaSonde high-frequency (HF) radars operating in the Gulf of Naples (Central Tyrrhenian Sea, Western Mediterranean Sea). This study focuses on the measurements obtained by each antenna over three range cells along a coast–offshore transect, pointing to any potential geographically dependent measurement. The scarcity of offshore wind measurements requires the use of model-generated data for comparative purposes. The data here used are obtained from the Mediterranean Wind–Wave Model, which provides indications for both wave and wind parameters, and the ERA5@2km wind dataset obtained by dynamically downscaling ERA5 reanalysis. These data are first compared with in situ data and subsequently with HF-retrieved wind direction measurements. The analysis of the overall performance of the HF radar network in the Gulf of Naples confirms that the HF radar wind data show the best agreement when the wind speed exceeds a 5 m/s threshold, ensuring a sufficiently energetic surface wave field to be measured. The results obtained in the study suggest the necessity of wind measurements in offshore areas to validate the HF radar wind measurements and to improve the extraction algorithms. The present work opens up further investigations on the applications of wind data from SeaSonde HF radars as potential monitoring platforms, both in coastal and offshore areas.

Submeso motions add complexities to the structure of the stable boundary layer. Such motions include horizontal meandering and gravity waves, in particular when the large-scale flow is weak. The coexistence and interaction of such submeso motions is investigated through the analysis of data collected in Antarctica, in persistent conditions of strong atmospheric stratification. Detected horizontal meandering is frequently associated with temperature oscillations characterized by similar time scales (30 min) at all levels (2, 4.5 and 10 m). In contrast, dirty gravity waves superimposed on horizontal meandering are detected only at the highest level, characterized by time scales of a few minutes. The meandering produces an energy peak in the low-frequency spectral range, well fitted by a spectral model previously proposed for low wind speeds. The coexistence of horizontal and vertical oscillations is observed in the presence of large wind-direction shifts superimposed on the gradual flow meandering. Such shifts are often related to the variation of the mean flow dynamics, but also to intermittent events, localized in time, which do not produce a variation in the mean wind direction and that are associated with sharp decreases in wind speed and temperature. The noisy gravity waves coexisting with horizontal meandering persist only for a few cycles and produce bursts of turbulent mixing close to the ground, affecting the exchange processes between the surface and the stable boundary layer. The results confirm the importance of sharp wind-direction changes at low wind speed in the stable atmosphere and suggest a possible correlation between observed gravity waves and dynamical instabilities modulated by horizontal meandering.

Large-eddy simulation of turbulent flow and gas dispersion in a cubical canopy is used to investigate the effect of wind-direction fluctuations on gas dispersion. Square blocks are set at regular intervals on the bottom surface, with line sources placed within the first, second, third, fifth and seventh rows. Large-eddy simulation without wind-direction fluctuations produces a good prediction of the mean streamwise velocity component, and the standard deviations of the fluctuations in the streamwise and spanwise velocity components, obtained from a wind-tunnel experiment. Wind-direction fluctuations marginally affect the mean streamwise velocity component above the canopy in the first row, and do not significantly affect the component beyond the third row. The standard deviations of the fluctuations in the streamwise and spanwise velocity components above the canopy are also affected by wind-direction fluctuations, but within the canopy the components are less sensitive to the fluctuations beyond the third row. The spatially-averaged concentrations within the canyon with wind-direction fluctuations before the third row are marginally greater than concentrations without the fluctuations, but they are essentially identical beyond the fifth row. The low-frequency turbulent flow that passes through the canyon is generated with and without wind-direction fluctuations.

Wake steering is a wind farm control strategy wherein upstream turbines are misaligned with the wind direction to redirect their wakes away from downstream turbines, increasing overall wind plant power. Wake steering is often analyzed assuming steady mean wind directions across the wind farm. However, in practice, the wind direction varies considerably over time because of large-scale weather phenomena. Wind direction variability causes the increase in power production from wake-steering to be less than predicted by steady-state models, but more robust wake-steering strategies can be designed that account for variable wind conditions. This paper compares the achieved yaw offsets and power gains from two different 2-turbine wake-steering experiments at a commercial wind farm with model predictions using the FLOw Redirection and Induction in Steady State (FLORIS) control-oriented model, assuming both fixed and variable wind directions. The impact of wind direction variability is modeled by including wind direction and yaw uncertainty in the FLORIS calculations. The field results match the trends predicted, assuming wind direction variability. Specifically, the yaw offsets achieved in the intended control regions are lower than desired, resulting in less power gain, while a slight loss in power occurs for wind directions outside of the intended control region because of unintentional yaw misalignment. The agreement between the model and field results suggests that the wind direction variability model can be used to design wake-steering controllers that are more robust to variable wind conditions present in the field.

From the viewpoint of installing small wind turbines (SWTs) on rooftops, this study investigated the effects of wind direction and horizontal aspect ratio (HAR = width/length) of a high-rise cuboid building on wind conditions above the roof by conducting large eddy simulations (LESs). The LES results confirmed that as HAR decreases (i.e., as the building width decreases), the variation in wind velocity over the roof tends to decrease. This tendency is more prominent as the angle between the wind direction and the normal vector of the building’s leeward face with longer roof edge increases. Moreover, at windward corners of the roof, wind conditions are generally favorable at relatively low heights. In contrast, at the midpoint of the roof's windward edge, wind conditions are generally not favorable at relatively low heights. At leeward representative locations of the roof, the bottoms of the height range of favorable wind conditions are typically higher than those at the windward representative locations, but the favorable wind conditions are much better at the leeward representative locations. When there is no prevailing wind direction, the center of the roof is more favorable for installing SWTs than the corners or the edge midpoints of the roof.

Dust storms are one of the important natural hazards that affect the lives of inhabitants all around the world, especially in North Africa and the Middle East. In this study, wind speed, wind direction, and air temperature patterns are investigated in one of the dustiest cities in Sistan Basin, Zahedan City, located in southeast Iran, over a 17-year period (2004–2020) using a WRF model and ground observation data. The city is located near a dust source and is mostly affected by local dust storms. The World Meteorology Organization (WMO) dust-related codes show that the city was affected by local dust, with 52 percent of the total dust events occurring during the period (2004–2021). The city’s weather station reported that 17.5% and 43% were the minimum and maximum dusty days, respectively, during 2004–2021. The summer and July were considered the dustiest season and month in the city. Since air temperature, wind speed, and wind direction are important factors in dust rising and propagation, these meteorological factors were simulated using the Weather Research and Forecasting (WRF) model for the Zahedan weather station. The WRF model’s output was found to be highly correlated with the station data; however, the WRF simulation mostly overestimated when compared with station data during the study period (2004–2020). The model had a reasonable performance in wind class frequency distribution at the station, demonstrating that 42.6% of the wind was between 0.5 and 2, which is in good agreement with the station data (42% in the range of 0.5–2). So, the WRF model effectively simulated the wind class frequency distribution and the wind direction at Zahedan station, despite overestimating the wind speed as well as minimum, maximum, and average air temperatures during the 17-year period.

The bridge girder’s aerodynamic configuration substantially governs its aerodynamic loading and wind-induced vibration characteristics. Extensive research has been performed to optimize the configuration of girders and implement aerodynamic measures to enhance the bridge’s wind resistance. In some practical bridge engineering projects, the aerodynamic configuration of the bridge girder is asymmetric. However, studies investigating the aerodynamic properties of asymmetric girders are limited. In this paper, the aerodynamic loading and vibration characteristics of the Π-shaped girders and box girders with asymmetric bikeways are experimentally studied. Through an extensive series of wind tunnel experiments, the static wind loading coefficients, flutter derivatives, vortex-induced vibration (VIV) responses, and the critical flutter velocities are compared across varying wind direction angles (WDAs). The experimental results demonstrate that the asymmetric girder configurations have different characteristics in both the static wind loading coefficient and flutter derivative in different WDAs. The influence of WDAs on the above-mentioned aerodynamic force coefficients of the asymmetric Π-shaped girder is more pronounced than that on the asymmetric box girder. For the asymmetric Π-shaped girder, the heaving VIV responses at a 0° WDA are smaller than those at a 180° WDA, but the torsional VIV responses at a 0° WDA are larger. Experimental results for critical flutter velocities indicate that the flutter performance at a 0° WDA is better than that at a 180° WDA, especially at positive angles of attack (AOAs) for the two types of asymmetric bridge girders.