Search

Under the European Space Agency (ESA) support, INCAS has taken the initiative to develop an Ascent and Descent Autonomous Maneuverable Platform (ADAMP) which will serve as an in-flight testing platform for reusable space technologies. This paper is focusing on activities aimed at assessing the robustness of the control system of the ADAMP in the presence of aerodynamic disturbances, with an emphasis on stability and disturbance rejection. Considering the ADAMP's inherent aerodynamic instability, the way aerodynamic forces and moments are incorporated in the control design formulation plays a critical role in the effectiveness of the adopted control solution in the presence of wind gusts and potential interaction with sloshing modes. To showcase these phenomena, two alternative control design methodologies are employed in the paper: the baseline strategy relies on robust self-scheduled structured H-Infinity optimization, while the second approach is based on nonlinear sliding mode theory. Different structured H-Infinity controllers are designed and analyzed in the frequency domain, providing a clear understanding of the impact of the aerodynamic effects in terms of stability margin degradation. These controllers are then thoroughly compared with the sliding mode alternative via nonlinear worst-case simulation of typical ascent and descent flights in the presence of strong wind gusts. [ABSTRACT FROM AUTHOR]

For short-term forecasting of weather periods characterized by strong winds with gusts, it is proposed to use results of calculations based on the TSUNM3 (Tomsk State University Nonhydrostatic Mesoscale Meteorological Model) local weather numerical prediction model in combination with semiempirical formulas for estimating the scales of wind gust speeds. The comparison of the calculations and observations of meteorological parameters obtained for the conditions under consideration at the meteorological stations of the Atmosfera Common Use Center of the Institute of Atmospheric Optics, the AMMS-RF (airfield meteorological measuring system) of the Tomsk airport, and meteorological stations of the Tomsk CHEM showed the prospects of using the model for numerical forecasting of this dangerous weather phenomenon. The results of the work are to be used for the development of an information and predictive system for early warning of dangerous wind gusts. [ABSTRACT FROM AUTHOR]

Strong wind events cause significant societal damage ranging from loss of property and disruption of commerce to loss of life. Over portions of the United States, the strongest winds occur in the cold season and may be driven by interactions with the terrain (downslope winds, gap flow, and mountain wave activity). In the first part of this two-part series, we evaluate the High-Resolution Rapid Refresh (HRRR) model wind speed and gust forecasts for the 2016–22 winter months over Wyoming and Colorado, an area prone to downslope windstorms and gap flows due to its complex topography. The HRRR model exhibits a positive bias for low wind speeds/gusts and a large negative bias for strong wind speeds/gusts. In general, the model misses many strong wind events, but when it does predict strong winds, there is a high false alarm probability. An analysis of proxies for surface winds is conducted. Specifically, 700- and 850-mb (1 mb = 1 hPa) geopotential height gradients are found to be good proxies for strong wind speeds and gusts at two wind-prone locations in Wyoming. Given the good agreement between low-level height gradients and surface wind speeds yet a strong negative bias for strong wind speeds and gusts, there is a potential shortcoming in the boundary layer physics in the HRRR model with regard to predicting strong winds over complex terrain, which is the focus of the second part of this two-part study. Last, the sites with the largest strong wind speed bias are found to mostly sit on the leeward side of high mountains, suggesting that the HRRR model performs poorly in the prediction of downslope windstorms. Significance Statement: We investigate the performance of the High-Resolution Rapid Refresh (HRRR) model with respect to strong wintertime wind speeds and gusts over the complex terrain of Wyoming and Colorado. We show that the overall performance of the HRRR model is low with regard to strong wind speed and wind gust forecasts across the investigated winter seasons, with a large negative bias in predicted strong wind speeds and gusts and a small positive bias for weak wind speeds and gusts. The largest biases are found to be on the leeward side of high mountains, indicating poor prediction of downslope winds. This study also utilizes National Weather Service forecasting metrics to understand their performance with respect to strong wind forecasts, and we find that they provide skill in forecasting these events. [ABSTRACT FROM AUTHOR]

The probabilistic forecast of wind gusts poses a significant challenge during the post-processing of numerical model outputs. Comparative analysis of probabilistic forecasting methods plays a crucial role in enhancing forecast accuracy. Within the context of meteorological services for alpine skiing at the 2022 Beijing Winter Olympics, The ECM-WF ensemble products were used to evaluate six post-processing methods. These methods include ensemble model output statistics (EMOS), backpropagation neural networks (BP), particle swarm optimization algorithms with back-propagation neural networks (PSO), truncated normal distributions, truncated logarithmic distributions, and generalized extreme value (GEV) distributions. The performance of these methods in predicting gust probabilities at five observation points along a ski track was compared. All six methods exhibited a substantial reduction in forecast errors compared to the original ECMWF products; however, the ability to correct the model forecast results varied significantly across different wind speed ranges. Specifically, the EMOS, truncated normal distribution, truncated logarithmic distribution, and GEV distribution demonstrated advantages in low wind-speed ranges, whereas the BP and PSO methods exhibit lower forecast errors for high wind-speed events. Furthermore, this study affirms the rationality of utilizing the statistical characteristics derived from ensemble forecasts as probabilistic forecast factors. The application of probability integral transform (PIT) and quantile–quantile (QQ) plots demonstrates that gust variations at the majority of observation sites conform to the GEV distribution, thereby indicating the potential for further enhanced forecast accuracy. The results also underscore the significant utility of the PSO hybrid model, which amalgamates particle swarm optimization with a BP neural network, in the probabilistic forecasting of strong winds within the field of meteorology. [ABSTRACT FROM AUTHOR]

Regional understanding of severe surface winds produced by convective processes [severe convective winds (SCWs)] is important for decision-making in several areas of society, including weather forecasting and engineering design. Meteorological studies have demonstrated that SCWs can occur due to a number of different mesoscale and microscale processes, in a range of large-scale atmospheric environments. However, long-term observational studies of SCW characteristics often have not considered this diversity in physical processes, particularly in Australia. Here, a statistical clustering method is used to separate a large dataset of SCW events, measured by automatic weather stations around Australia, into three types, associated with strong background wind, steep lapse rate, and high moisture environments. These different types of SCWs are shown to have different seasonal and spatial variations in their occurrence, as well as different measured wind gust, lightning, and parent-storm characteristics. In addition, various convective diagnostics are tested in their ability to discriminate between measured SCW events and nonsevere events, with significant variations in skill between event types. Differences in environmental conditions and wind gust characteristics between event types suggests potentially different physical processes for SCW production. These findings are intended to improve regional understanding of severe wind characteristics, as well as environmental prediction of SCWs in weather and climate applications, by considering different event types. Significance Statement: The purpose of this study is to improve regional understanding of different types of severe wind events in Australia, specifically those associated with atmospheric convection. We did this by constructing a dataset of 413 severe convective wind events, using weather station and radar data within 20 regions around Australia. We then split those events into three different types, based on the environmental conditions that they occur within. We found that each event type tends to occur at different times of the year and in different regions, while also having different wind gust and lightning characteristics. In addition, the atmospheric conditions that are helpful for prediction of severe wind events differs between each type. These results are intended to be useful for prediction of severe wind events associated with convection and assessing their variability, characteristics, and impacts, in both weather forecasting and climate analysis. [ABSTRACT FROM AUTHOR]

The devastating winds in extratropical cyclones can be assigned to different mesoscale flows. How these strong winds are transported to the surface is discussed for the Mediterranean windstorm Adrian (Vaia), which caused extensive damage in Corsica in October 2018. A mesoscale analysis based on a kilometer-scale simulation with the Meso-NH model shows that the strongest winds come from a cold conveyor belt (CCB). The focus then shifts to a large-eddy simulation (LES) for which the strongest winds over the sea are located in a convective boundary layer. Convection is organized into coherent turbulent structures in the form of convective rolls. It is their downward branches that contribute most to the nonlocal transport of strong winds from the CCB to the surface layer. On landing, the convective rolls break up because of the complex topography of Corsica. Sensitivity experiments to horizontal grid spacing show similar organization of boundary layer rolls across the resolution. A comparative analysis of the kinetic energy spectra suggests that a grid spacing of 200 m is sufficient to represent the vertical transport of strong winds through convective rolls. Contrary to LES, convective rolls are not resolved in the kilometer-scale simulation and surface winds are overestimated due to excessive momentum transport. These results highlight the importance of convective rolls for the generation of surface wind gusts and the need to better represent them in boundary layer parameterizations. [ABSTRACT FROM AUTHOR]

To quantify the hazard or risks associated with severe convective wind gusts, it is necessary to have a reliable and spatially complete climatology of these events. The coupling of observational and global reanalysis (ERA-Interim) data over the period 2005–2015 is used here to facilitate the development of a spatially complete convective wind gust climatology for Australia. This is done through the development of Bayesian Hierarchical models that use both weather station-based wind gust observations and seasonally averaged severe weather indices (SWI), calculated using reanalysis data, to estimate seasonal gust frequencies across the country while correcting for observational biases specifically, the sparse observational network to record events. Different SWI combinations were found to explain event counts for different seasons. For example, combinations of Lifted Index and low level wind shear were found to generate the best results for autumn and winter. While for spring and summer, the composite Microburst Index and the combination of most unstable CAPE and 0–1 km wind shear were found to be most successful. Results from these models showed a minimum in event counts during the winter months, with events that do occur mainly doing so along the southwest coast of Western Australia or along the coasts of Tasmania and Victoria. Summer is shown to have the largest event counts across the country, with the largest number of gusts occurring in northern Western Australia extending east into the Northern Territory with another maximum over northeast New South Wales. Similar trends were found with an extended application of the models to the period 1979–2015 when utilizing only reanalysis data as input. This implementation of the models highlights the versatility of the Bayesian hierarchical modelling approach and its ability, when trained, to be used in the absence of observations. [ABSTRACT FROM AUTHOR]

The Women’s Slope Style event of 11–12 February 2018 at the PyeongChang 2018 Olympic Winter Games posed considerable challenges to the competitors and decision-makers, requiring sub-kilometer and sub-minute weather predictions in complex terrain. The gusty wind conditions were unfair and unsafe as the competitors could not achieve sufficient speed to initiate or complete their jumps. The term micro-nowcasting is used here to reflect the extreme high-resolution nature of these science and service requirements. The World Meteorological Organization has conducted several research development and forecast demonstration projects to advance, accelerate and promote the art of nowcasting. Data from compact automatic weather stations, located along the field of play, reported every minute and were post-processed using time series, Hovmöller and wavelet transforms to succinctly present the information. The analyses revealed dominant frequencies of about 20 min, presumed to be associated with vortex shedding from the mountain ridges, but were unable to directly capture the gusts that affected the competitors. The systemic challenges from this and previous projects are reviewed. They include the lack of adequate scientific knowledge of microscale processes, gaps in modeling, the need for post-processing, forecast techniques, managing ever-changing service requirements and highlights the role of observations and the critical role of the forecaster. These challenges also apply to future high-resolution operational weather and warning services.

Wind gusts are sudden, brief increases in wind speed that have important implications for wind power generation, building design, aviation and marine safety. However, wind gusts in the Plateau and coastal plain are very different. In this paper, the gust characteristics are explored and compared at two sites in the same latitude—Xining, a city in the eastern Tibetan Plateau, and Qingdao, a city in the coast in China—using Doppler lidar data. The results indicate that the wind gusts in Xining are more intense and occur at a higher height than those in Qingdao. Though mean winds and turbulence significantly influence gusts, the turbulence intensity is responsible for the differences in gust, and high turbulence in the eastern part of the Tibetan Plateau is inferred. These results provide observational evidence for wind gusts over the complex terrain of the Tibetan Plateau and are useful for studying their impact on important aspects, such as flight safety. [ABSTRACT FROM AUTHOR]

Wind gusts are common small‐scale weather phenomena characterized by a rapid increase in wind speed over a short period of time. Wind gusts affect a wide range of interests, including aviation safety, wind energy and professional skiing, while strong gusts can also cause severe weather events and limit visibility. However, the characteristics of gusts in the lower troposphere are not yet well understood, especially at the top of the boundary layer. In this paper, the gust parameters in the lower atmosphere at different heights and their diurnal variation characteristics are examined based on Doppler lidar data from the gentle coastal topography of Shandong Province, China. The results show that all the gust parameters have distinct stratification characteristics, with significantly different diurnal variations below 400 m, between 400 and 1,200 m, and above 1,200 m (above the ground, same below). Wind gusts are considered to be the result of turbulent disturbances superimposed on the background airflow, and strong turbulence could cause the instantaneous winds to significantly deviate from the mean airflow. The magnitude of the peak gust is mainly controlled by the mean flow, but the gust factor, which is the ratio of the peak gust to the mean flow, is almost independent of the mean flow and has a strong positive correlation with the turbulence intensity. These results imply that gusts of different properties occur at varying altitudes, with differences depending on turbulence intensity and mean airflow, and provide an observational basis for studies of gust structure. Plain Language Summary: Wind gusts are phenomena where sudden increases in wind speed occur over a short duration, and these gusts may threaten aviation safety and wind energy security. Near‐surface gusts have been noticed and studied widely; however, gusts occurring above hundreds of meters in altitude remain poorly understood. Based on Doppler lidar data from the coastal plain of Shandong, China, gusts occurring at different heights in the lower troposphere can be monitored continuously. The results indicate that wind gusts show obvious stratification characteristics, with significant diurnal variations at different levels. Gust magnitude is influenced by background wind and turbulence. Peak gusts are dominated mainly by the mean wind. However, when turbulence is strong, gusts may deviate significantly from the mean wind. The gust factor shows an obvious positive correlation with turbulence intensity. These results may enhance the understanding of wind gust characteristics in the lower troposphere and provide an observational basis for structural studies of gusts. Key Points: The gust parameters in the lower troposphere exhibit special vertical three‐layer stratification characteristics and diurnal variationThe gust amplitude and gust factor demonstrate different characteristics with height in the lower troposphereThe peak gust depends mainly on the background wind field, but its amplitude is significantly correlated with turbulence intensity [ABSTRACT FROM AUTHOR]

The Women's Slope Style event of 11–12 February 2018 at the PyeongChang 2018 Olympic Winter Games posed considerable challenges to the competitors and decision-makers, requiring sub-kilometer and sub-minute weather predictions in complex terrain. The gusty wind conditions were unfair and unsafe as the competitors could not achieve sufficient speed to initiate or complete their jumps. The term micro-nowcasting is used here to reflect the extreme high-resolution nature of these science and service requirements. The World Meteorological Organization has conducted several research development and forecast demonstration projects to advance, accelerate and promote the art of nowcasting. Data from compact automatic weather stations, located along the field of play, reported every minute and were post-processed using time series, Hovmöller and wavelet transforms to succinctly present the information. The analyses revealed dominant frequencies of about 20 min, presumed to be associated with vortex shedding from the mountain ridges, but were unable to directly capture the gusts that affected the competitors. The systemic challenges from this and previous projects are reviewed. They include the lack of adequate scientific knowledge of microscale processes, gaps in modeling, the need for post-processing, forecast techniques, managing ever-changing service requirements and highlights the role of observations and the critical role of the forecaster. These challenges also apply to future high-resolution operational weather and warning services. [ABSTRACT FROM AUTHOR]

Severe winds associated with thunderstorms and convection are a hazard affecting key aspects of society, including emergency management and infrastructure design. Several studies around the world have shown that severe convective winds (SCWs) can occur due to several different processes, in a range of atmospheric environments, with significant regional and temporal variations. However, in eastern Australia, the types of SCWs and their variability have not been assessed outside of individual case studies. Here, a combination of reanalysis, lightning, radar, and station data are used to characterize a set of 36 SCW events in four locations in eastern Australia. These events are objectively chosen based on the strongest measured wind gusts from station data (greater than 25 m s−1) over a 14-yr period, with 6-hourly lightning data and a 30-dBZ radar reflectivity threshold used to infer moist convective processes. Radar data analysis suggests that these SCW events are produced by several different types of parent thunderstorms, with station observations suggesting a range of temporal characteristics for these different event types. A clustering algorithm applied to environmental data is used to suggest three dominant types of events, based on low-level moisture, low-level temperature lapse rate, and deep-layer mean wind speed and vertical shear. Based on the distribution of synoptic conditions and thunderstorm properties for each environmental cluster, it is suggested that these three event types correspond to the following: 1) shallow vertical transport of strong synoptic-scale winds to the surface, 2) downbursts driven by subcloud evaporation, and 3) intense thunderstorms including supercells. Significance Statement: The purpose of this study is to better understand the different types of severe wind events in eastern Australia that are produced by convective storms. We looked at 36 historical cases in four locations and find that severe winds can be produced by very different classes of convective storms. We also suggest that there are three key types of atmospheric environment that are associated with events in this region. These environments vary in terms of the vertical structure of temperature, moisture, and wind speed above the surface. Understanding the different types of environments that lead to severe convective winds can help to reduce uncertainties in future climate projections for this region based on environmental changes. [ABSTRACT FROM AUTHOR]

Extreme stratospheric polar vortex (SPV) events can influence winter tropospheric circulation for up to 60 d. Their impacts on air temperature have been extensively studied recently. However, there is a research gap in their effects on wind speeds and its extremes. This study aims to evaluate, for the first time, the impacts of such extreme SPV events on observed and modelled near-surface wind gusts across Europe. We have analysed wind gust data coming from: station-based observations (for the Iberian Peninsula and Scandinavia), the ERA5 reanalysis and the SEAS5 and GloSea6 seasonal forecasting systems. We assess their similarities in reproducing 4 parameters of their corresponding distributions: median, standard deviation, skewness and kurtosis. For all these datasets, the results indicate that extreme positive SPV events are followed by negative wind gust anomalies in Southern Europe and positive in Northern Europe. Whereas, negative SPV events (such as Sudden Stratospheric Warmings) have positive gust anomalies in Southern Europe and negative in the north. A central region shows negligible anomalies in both cases. This highlights the ability of SPV as a predictor for short-medium-term forecasting of extreme wind events, which would have direct applications to many socioeconomic and environmental issues such as the estimation of wind-power generation.

The risk posed by heavy rain and strong wind is now suspected to be exacerbated by the way they co-occur, yet this remains insufficiently understood to effectively plan and mitigate. This study systematically investigates the correlations between wintertime (Oct–Mar) extremes relating to wind and flooding at all timescales from daily to seasonal. Meteorological reanalysis and river flow datasets are used to explore the historical period, and climate projections at 12 km resolution are analysed to understand the possible effects of future climate change (2061–2080, RCP 8.5). A new flood severity index (FSI) is also developed to complement the existing storm severity index (SSI). Initially, Great Britain (GB) is taken as a comparatively simple yet informative study area, then analysis is extended to the full European domain.Aggregated across GB, wind gusts and precipitation correlate strongly (rs ∼0.6–0.8) at timescales from daily to seasonal, but peak around 10 days. A later peak is seen when considering correlations between wind gusts and river flows (40–60 days). This time is likely needed for catchments’ soils to saturate. A conceptual multi-temporal, multi-process model of GB wintertime flood-wind co-occurrence is proposed as a basis for future investigation. When historical analysis is extended across Europe we find the timescale of maximum correlation varies strongly between nations, likely as a result of different meteorological drivers.Impact focused correlation (FSI–SSI) is lower (rs∼0.2) but increases notably with climate change at timescales of ∼40 days (rs∼0.4). Tentatively, very severe episodes (i.e., both >99th percentile) appear heavily influenced by climate change, increasing roughly threefold by 2061–2080 (p < 0.05). The return period of such an event is 16 years historically (compared to 56 years if the two hazards were independent), reduces to 5 years in future. Such metrics provide actionable information for insurers and other stakeholders.

The paper researches wind gusts that are caused by changes in pressure on a flat surface. To describe this phenomenon the equations of the state of a continuous medium are used. In the stationary case, these equations in the air are the equation of the decreasing exponent for the pressure gradient. Consequently, the volume force in the air cannot act permanently. It decreases at a distance of about a kilometer. This exponential damping solution is due to wind gusts. The paper concludes that the periodic dumping of volumetric forces leads to turbulence in the air. These disruptions or turbulence are also observed when the air rotates in cyclones and anticyclones. In the water, pressure fluctuation leads to periodic waves.

Inflatable bounce houses, also known as portable inflatable amusement devices, are safety hazards when they are dragged, blown over, or lofted by winds. We have documented 132 cases of wind-related bounce house incidents worldwide for the years 2000–21 that caused at least 479 injuries and at least 28 deaths. Over three-quarters of these incidents happen in the warm season. A subjective effort to categorize the meteorological conditions leading to these incidents reveals that, of the over 70% of incidents for which a specific meteorological cause could be identified, cold-frontal passages, dust devils, or thunderstorm-related winds are most likely to be occurring at the time of the event. In the United States, regulations regarding bounce house safety vary widely. Seventeen states either have no guidelines or specifically exclude inflatables from regulation. Nineteen U.S. states' laws or regulations explicitly cite American Society for Testing and Materials (ASTM) standards, which set limits on the wind speed in which inflatables should be used and, for commercial bounce houses, require the presence of a meteorologically knowledgeable attendant. For events with nearby wind data available, 22% of all incidents occurred with reported wind speeds lower than any ASTM standards, and 51% below the highest ASTM threshold. Increased vigilance is therefore necessary on the part of bounce house providers and consumers to avoid wind-related incidents. We have created a website for public dissemination of this study's data and safety tips. [ABSTRACT FROM AUTHOR]

In this paper, a predictive sliding mode control (PSMC) strategy for the quadrotors tracking trajectory problem is proposed. This strategy aims to combine the advantages of sliding mode control (SMC) and non-linear model predictive control (NMPC) to improve the tracking control performance for quadrotors in terms of optimality, inputs/states constraints satisfaction, and strong robustness against disturbances. A comparative study of three popular controllers: the SMC, NMPC, and the integral backstepping control (IBC) is performed with different criteria. Accordingly, IBC and SMC show less computational time and strong robustness, while NMPC has minimum control effort. The discrete Dryden turbulence model is used as a benchmark model to represent the wind effect on the trajectory tracking accuracy. The effectiveness of the proposed method PSMC has been proven and compared with discrete-time sliding mode control (DSMC) and NMPC in several scenarios. Simulation results show that under both wind turbulence and time-variant uncertainties, the PSMC outperforms the other controllers by providing simultaneously disturbance rejection and guarantee that the control inputs are within bounded constraints. [ABSTRACT FROM AUTHOR]

Wind gusts present challenges to operational meteorologists, both to forecast accurately and also to verify. Strong wind gusts can damage structures and create costly risks for diverse industrial sectors. The meteorologically stratified gust factor (MSGF) model incorporates site-specific gust factors (the ratio of peak wind gust to mean wind speed) with wind speed and direction forecast guidance. The MSGF model has previously been shown to be a viable operational tool that exhibits skill (improvement over climatology) in forecasting peak wind gusts. This study assesses the performance characteristics of the MSGF model by evaluating peak gust predictions during several types of gust-producing weather phenomena. Peak wind gusts were prepared and verified for seven specific weather conditions over an 8-yr period at 16 sites across the United States. When coupled with two forms of model output statistics (MOS) wind guidance, the MSGF model generally shows skill in predicting peak wind gusts at forecast projections ranging from 6 to 72 h. The model performed best during high pressure and nocturnal conditions and was also skillful during conditions involving snow. The model did not perform well during the "rain with thunder" weather type. TheMSGF model is a viable tool for the operational prediction of peak gusts for most gust-producing weather types. SIGNIFICANCE STATEMENT: Wind gusts are an important and potentially costly environmental hazard. Wind gusts affect many industrial sectors, including transportation, power generation, forestry, construction, and insurance, but predicting gusts remains a challenging component of weather forecasting. Recent studies have demonstrated that the meteorologically stratified gust factor (MSGF) model shows skill in predicting peak gusts. This study shows that the MSGF model is skillful at predicting peak gusts during specific types of gust-producing weather phenomena at forecast projections up to 72 h, providing further confirmation that the MSGF model is a viable tool for the operational prediction of peak gusts. [ABSTRACT FROM AUTHOR]

This study delivers the first empirical data-driven analysis of the impact of turbulence induced gustiness on the fine dust emissions from a measuring field. For quantification of the gust impact, a new measure, the Gust uptake Efficiency (GuE) is introduced. GuE provides a percentage of over- or under-proportional dust uptake due to gust activity during a wind event. For the three analyzed wind events, GuE values of up to 150% could be found, yet they significantly differed per particle size class with a tendency for lower values for smaller particles. In addition, a high-resolution correlation analysis among 31 particle size classes and wind speed was conducted; it revealed strong negative correlation coefficients for very small particles and positive correlations for bigger particles, where 5 μm appears to be an empirical threshold dividing both directions. We conclude with a number of suggestions for further investigations: an optimized field experiment setup, a new particle size ratio (PM1/PM0.5 in addition to PM10/PM2.5), as well as a comprehensive data-driven search for an optimal wind gust definition in terms of soil erosivity. [ABSTRACT FROM AUTHOR]

The average daily values of wind speed and wind gust speed in the years 2001-2018 in Siedlce were analyzed. In the study area, the frequency of individual wind directions was determined and, according to criteria set by the Government Centre for Security, the frequency of hazards from damaging wind gusts was evaluated. The following wind gusts were considered: >20 m/s, 25 m/s and 35 m/s. It was found that, in the Siedlce area, the prevailing wind direction was west-south-westerly (WSW), and the average annual wind speed in the long-term study period was 2.99 m/s. March, April and May were the months when the highest speeds of maximum wind gusts (>35 m/s) were recorded. Extreme wind gusts the speed of which poses a hazard to the environment, national economy as well as human life and health, occurred in both the cold and warm seasons of the year. [ABSTRACT FROM AUTHOR]

Wind gusts are sudden, brief increases in wind speed that have important implications for wind power generation, building design, aviation and marine safety. However, wind gusts in the Plateau and coastal plain are very different. In this paper, the gust characteristics are explored and compared at two sites in the same latitude—Xining, a city in the eastern Tibetan Plateau, and Qingdao, a city in the coast in China—using Doppler lidar data. The results indicate that the wind gusts in Xining are more intense and occur at a higher height than those in Qingdao. Though mean winds and turbulence significantly influence gusts, the turbulence intensity is responsible for the differences in gust, and high turbulence in the eastern part of the Tibetan Plateau is inferred. These results provide observational evidence for wind gusts over the complex terrain of the Tibetan Plateau and are useful for studying their impact on important aspects, such as flight safety.

Wind gusts, and in particular intense gusts, are societally relevant but extremely challenging to forecast. This study systematically assesses the skill enhancement that can be achieved using artificial neural networks (ANNs) for forecasting of wind gust occurrence and magnitude. Geophysical predictors from the ERA5 reanalysis are used in conjunction with an autoregressive term in regression and ANN models with different predictors, and varying model complexity. Models are derived and assessed for the warm (April–September) and cold (October–March) seasons for three high passenger volume airports in the United States. Model uncertainty is assessed by deriving models for 1000 different randomly selected training (70%) and testing (30%) subsets. Gust prediction fidelity in independent test samples is critically dependent on inclusion of an autoregressive term. Gust occurrence probabilities derived using five-layer ANNs exhibit consistently higher fidelity than those from regression models and shallower ANNs. Inclusion of the autoregressive term and increasing the number of hidden layers in ANNs from 1 to 5 also improve the model performance for gust magnitudes (lower RMSE, increased correlation, and model standard deviations that more closely approximate observed values). Deeper ANNs (e.g., 20 hidden layers) exhibit higher skill in forecasting strong (17–25.7 m s−1) and damaging (≥25.7 m s−1) wind gusts. However, such deep networks exhibit evidence of overfitting and still substantially underestimate (by 50%) the frequency of strong and damaging wind gusts at the three airports considered herein. Significance Statement: Improved short-term forecasting of wind gusts will enhance aviation safety and logistics and may offer other societal benefits. Here we present a rigorous investigation of the relative skill of models of wind gust occurrence and magnitude that employ different statistical methods. It is shown that artificial neural networks (ANNs) offer considerable skill enhancement over regression methods, particularly for strong and damaging wind gusts. For wind gust magnitudes in particular, application of deeper learning networks (e.g., five or more hidden layers) offers tangible improvements in forecast accuracy. However, deeper networks are vulnerable to overfitting and exhibit substantial variability with the specific training and testing data subset used. Also, even deep ANNs reproduce only half of strong and damaging wind gusts. These results indicate the need for future work to elucidate the dynamical mechanisms of intense wind gusts and advance solutions to their prediction. [ABSTRACT FROM AUTHOR]

Wind gusts are the serious parameters that need to be considered for the aerodynamic study of any wind turbine. This article investigates the performance of a new vertical axis wind turbine with non-cylindrical helical turbine blades for an unsteady gusty wind prevailing in urban and suburban environments. In this work, three-dimensional CFD analysis with dynamic mesh has been carried out for two gust velocities of 0.6 m/s and 2 m/s with a mean free stream velocity of 4 m/s. The behavior of the proposed helical turbine during start-up and steady operation was analyzed. The results show that the gust introduced during the start-up period aids in torque production, enabling it to reach the steady-state faster. The turbine reached the steady-state 54.71% earlier under gusty wind than under constant wind. However, on the scale of Cp, the turbine performed well under constant wind during the start-up period. The gust injection during the steady-state period showed a 1.97% increase in Cp of the turbine than under constant wind velocity. [ABSTRACT FROM AUTHOR]

Postprocessing ensemble weather predictions to correct systematic errors has become a standard practice in research and operations. However, only a few recent studies have focused on ensemble postprocessing of wind gust forecasts, despite its importance for severe weather warnings. Here, we provide a comprehensive review and systematic comparison of eight statistical and machine learning methods for probabilistic wind gust forecasting via ensemble postprocessing that can be divided in three groups: state-of-the-art postprocessing techniques from statistics [ensemble model output statistics (EMOS), member-by-member postprocessing, isotonic distributional regression], established machine learning methods (gradient-boosting extended EMOS, quantile regression forests), and neural network–based approaches (distributional regression network, Bernstein quantile network, histogram estimation network). The methods are systematically compared using 6 years of data from a high-resolution, convection-permitting ensemble prediction system that was run operationally at the German weather service, and hourly observations at 175 surface weather stations in Germany. While all postprocessing methods yield calibrated forecasts and are able to correct the systematic errors of the raw ensemble predictions, incorporating information from additional meteorological predictor variables beyond wind gusts leads to significant improvements in forecast skill. In particular, we propose a flexible framework of locally adaptive neural networks with different probabilistic forecast types as output, which not only significantly outperform all benchmark postprocessing methods but also learn physically consistent relations associated with the diurnal cycle, especially the evening transition of the planetary boundary layer. [ABSTRACT FROM AUTHOR]

This study delivers the first empirical data-driven analysis of the impact of turbulence induced gustiness on the fine dust emissions from a measuring field. For quantification of the gust impact, a new measure, the Gust uptake Efficiency (GuE) is introduced. GuE provides a percentage of over- or under-proportional dust uptake due to gust activity during a wind event. For the three analyzed wind events, GuE values of up to 150% could be found, yet they significantly differed per particle size class with a tendency for lower values for smaller particles. In addition, a high-resolution correlation analysis among 31 particle size classes and wind speed was conducted; it revealed strong negative correlation coefficients for very small particles and positive correlations for bigger particles, where 5 μm appears to be an empirical threshold dividing both directions. We conclude with a number of suggestions for further investigations: an optimized field experiment setup, a new particle size ratio (PM1/PM0.5 in addition to PM10/PM2.5), as well as a comprehensive data-driven search for an optimal wind gust definition in terms of soil erosivity.

This study analyses quality controlled and homogenized near-surface wind speed series (SWS) at 87 meteorological stations distributed across Spain and Portugal for 1961–2019. Multidecadal variability analysis of both mean and gusts SWS confirms for the first time in the region the cessation of the stilling (decline of SWS) and a possible weak reversal phenomenon (increase in SWS) in the last decades, varying its onset from 1999 to 2018, depending on the season and variable. Different atmospheric circulation drivers are evaluated to explain the stilling and reversal phenomena. Among the chosen teleconnection indices, the Western Mediterranean Oscillation index (WeMOi) is the one that presents the highest (positive) correlation with SWS, although the correlation (negative) with the North Atlantic Oscillation index and the Mediterranean Oscillation index in winter is also statistically significant (p < 0.05). Results show that the interannual variability of the WeMOi could exert a strong influence on both the stilling and reversal phenomena, as it displays statistically significant negative trends for 1961–2010 and positive ones (non-significant) for 2010–2019, in agreement with the observed SWS trends. The use of the Jenkinson and Collison weather type classification reveals the marked influence of certain weather types in modulating SWS changes (i.e. , west, northwest, and anticyclonic types). We proposed the increase in atmospheric thermal stability and the northward shift of the jet stream as principal causes of the stilling phenomenon in this region. Our results improve our understanding of wind changes, and highlight the importance of regional assessments to discern their socioeconomic and environmental impacts. [ABSTRACT FROM AUTHOR]

Understanding the complex behaviours inherent to the wind is a key challenge in improving the accuracy of short-term prediction models. Turbulent wind gusts are one of several factors expected to contribute to uncertainties in forecasts and can also cause turbine damage in extreme cases. This study aims to (1) evaluate how spatial gust properties map to temporal variations in power generation at the wind turbine and farm scale and (2) assess the validity of Taylor's frozen turbulence hypothesis for gust impact prediction. First, a gust identification scheme building upon existing research is presented. Gusts are extracted from LiDAR-based near-horizontal wind fields collected throughout a year-long measurement campaign at an onshore wind farm. The tendency of gusts to induce wind power ramps is investigated, and their propagation speed is compared against the background flow. Results show that gusts are associated with increased power variability at the turbine scale and the wind farm scale, with the most noticeable effect being at the turbine level. Gusts with length scales less than one kilometre generally propagate at the rate of the background flow, whereas larger gusts travel marginally faster. The study emphasises the importance of gusts for short-term power forecasting applications and affirms the use of Taylor's frozen turbulence hypothesis for gust impact prediction. • Gusts are characterised spatially using LiDAR data collected throughout a year-long measurement campaign. • Gusts are associated with increased power variability, both at the turbine scale and the wind farm scale. • Gusts with length scales less than one kilometre tend to propagate at the rate of the mean wind flow, whereas larger gusts travel marginally faster. [ABSTRACT FROM AUTHOR]