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]

Upper ocean processes play a key role in air-sea coupling, with variability on both short and long time scales. The diurnal cycle associated with diurnal solar insolation and nighttime cooling, may act, along with stochastic wind variability, on upper ocean temperatures and stratification resulting in a diurnal warm layer and a nonlinear rectified effect on longer time scales. This study describes diurnal changes in upper ocean temperature for a location in the equatorial Indian Ocean, using observations from the Dynamics of the Madden-Julian Oscillation field campaign, a high vertical resolution 1-D process model, and a diurnal cycling scheme. Solar forcing is the main driver of diurnal variability in upper ocean temperature and stratification. Yet except during nighttime convection, winds with variability on the order of hours (here referred to as “wind gusts”) regulate how fast surface water is mixed to greater depths when daily mean winds are weak. Wind gusts are much stronger than diurnal winds. Even using stochastic wind gusts but no diurnal winds as input in a 1-D process model yields an estimate of diurnal temperature that compares well with observations. A new version of the Large and Caron (2015) scheme (LC2015) provides an estimate of upper ocean diurnal temperature that is consistent with observations. LC2015 has the advantage of being suitable for implementation in a climate model, with the goal to improve SST estimates, hence the simulated heat flux at the air-sea interface. Yet LC2015 is not very sensitive to the inclusion or omission of the high-frequency component of the wind.

Upper ocean processes play a key role in air-sea coupling, with variability on both short and long time scales. The diurnal cycle associated with diurnal solar insolation and nighttime cooling, may act, along with stochastic wind variability, on upper ocean temperatures and stratification resulting in a diurnal warm layer and a nonlinear rectified effect on longer time scales. This study describes diurnal changes in upper ocean temperature for a location in the equatorial Indian Ocean, using observations from the Dynamics of the Madden-Julian Oscillation field campaign, a high vertical resolution 1-D process model, and a diurnal cycling scheme. Solar forcing is the main driver of diurnal variability in upper ocean temperature and stratification. Yet except during nighttime convection, winds with variability on the order of hours (here referred to as “wind gusts”) regulate how fast surface water is mixed to greater depths when daily mean winds are weak. Wind gusts are much stronger than diurnal winds. Even using stochastic wind gusts but no diurnal winds as input in a 1-D process model yields an estimate of diurnal temperature that compares well with observations. A new version of the Large and Caron (2015) scheme (LC2015) provides an estimate of upper ocean diurnal temperature that is consistent with observations. LC2015 has the advantage of being suitable for implementation in a climate model, with the goal to improve SST estimates, hence the simulated heat flux at the air-sea interface. Yet LC2015 is not very sensitive to the inclusion or omission of the high-frequency component of the wind.

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]

Steel lattice towers have large application in meteorology, and are regularly exposed to loads difficult to determine reliably, like wind and ice, and especially wind gusts and accompanied structural vibrations. EN 1993-3-1 treats such structures and requires checking of vibrations, but does not supply methodology for it, neither allowed values for deformation and vibrations. The paper presents analysis of a tower 110 m high using the Finite Element Method (FEM), in case of wind gust, for iced and non-iced structure, both statically and dynamically. The results were compared and recommendations for future treatment of such sensitive structures were given.

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 examined the hydrological/meteorological controls on largewildfires > 10,000 acres (40.5 km²) during 2017-2020 in Northern California at spatial and temporal scales of the target wildfires' occurrence or growth. This study used the following simple indices for analysis: Moisture Deficit Index (MDI) computed by dividing vapor pressure deficit by soil moisture, MDIWIND computed by multiplying MDI by horizontal wind speed, and MDIGUST computed by multiplying MDI by wind gust speed. The ignition location MDIWIND and MDIGUST showed larger values on the ignition date in fire-years compared to non-fire-years for most of the target wildfires (95.8% and 91.7%, respectively). The peak timing of MDIGUST, which is to evaluate the integrated effect of dry atmosphere/soil and windy condition, coincided with the ignition date for August Complex Fire 2020, Ranch Fire 2018, Claremont-Bear Fire 2020, and Camp Fire 2018. We also found that August Complex Fire 2020, Claremont-Bear Fire 2020, and Camp Fire 2018 occurred in the areas where MDIGUST became spatially and temporally high. Further, strong relationships were found between burned area sizes of the target wildfires and MDI (R = 0.62, p = 0.002), MDIWIND (R = 0.72, p < 0.001), and MDIGUST (R = 0.68, p < 0.001). Overall, the findings in this study implied the strong effect of dry atmosphere/soil and windy conditions on recent large wildfire activities in Northern California. The findings could contribute to a more temporally and spatially detailed forecast of wildfire risks or a better understanding of wildfires' occurrence and growth mechanisms. [ABSTRACT FROM AUTHOR]

Recent studies have shown that extreme wind gusts are ubiquitous within the eyewall of intense tropical cyclones (TCs). These gusts pose a substantial hazard to human life and property, but both the short-term (i.e., during the passage of a single TC) and long-term (over many years) risk of encountering such a gust at a given location is poorly understood. Here, simulated tower data from large-eddy simulations of idealized TCs in a quiescent (i.e., no mean flow or vertical wind shear) environment are used to estimate these risks for the offshore region of the United States. For both a category 5 TC and a category 3 TC, there is a radial region where nearly all simulated towers experience near-surface (the lowest 200 m) 3-s gusts exceeding 70 m s−1 within a 10-min period; on average, these towers respectively sample peak 3-s gusts of 110 and 80 m s−1. Analysis of an observational dropsonde database supports the idealized simulations, and indicates that offshore structures (such as wind turbines) in the eyewall of a major hurricane are likely to encounter damaging wind speeds. This result is then incorporated into an estimate of the long-term risk, using analyses of the return period for major hurricanes from both a best-track database and a statistical–dynamical model forced by reanalysis. For much of the nearshore region of the Gulf of Mexico and southeastern U.S. coasts, this analysis yields an estimate of a 30%–60% probability of any given point experiencing at least one 70 m s−1 gust within a 30-yr period. [ABSTRACT FROM AUTHOR]

This study presents rare measurements and analysis of a nocturnal thunderstorm downburst on the 213-m tall Cabauw tower in the Netherlands. The event occurred between 0200 and 0300 UTC 12 March 2008 and was measured using four ultrasonic 10-Hz anemometers positioned at 3, 60, 100, and 180 m above ground level. One-second gusts in the outflow exceeded 30 m s−1 at 60 m and above. This wind event was accompanied by an abrupt change of wind direction from southwest to west. While the shift in wind direction corresponded with the change of upwind surface roughness, the time series of turbulence intensity and other turbulence characteristics were not affected. The statistical properties of this event were compared against the largest European database of thunderstorm winds measured in the Mediterranean. The study also demonstrated that primary and secondary vortex structures—secondary vortex being rarely observed in actual downbursts—developed at the forward edge of the cold outflow. The estimated diameter of the downdraft was 1200 m at 70 m above ground level. The measured velocity profiles and friction velocity were compared against theoretical predictions of the Monin–Obukhov similarity theory (MOST). MOST without stratification adjustment overestimated measured friction velocity twofold. Alternative values for surface roughness during the outflow were derived based on the measured friction velocity and MOST-based fit of measured velocity profiles. Ceilometer and radar measurements were supplementary data in this analysis. Significance Statement: We analyzed the occurrence of a strong nocturnal downburst measured on a 213-m tall meteorological tower in the Netherlands (0200–0300 UTC 12 March 2008). The near-surface (3-m) wind gusts in the outflow exceeded 20 m s−1. We showed the existence of the primary and secondary circulation vortices at the leading edge of the cold outflow. The secondary vortex that is smaller and formed ahead of the primary vortex is rarely observed in actual events. The classical approach used in numerical weather prediction models to estimate the influence of shear stress on the mean flow overestimated the friction velocity twofold. Future studies should focus on more cases of nocturnal thunderstorm winds. [ABSTRACT FROM AUTHOR]

In this paper, a robust control strategy is developed for unmanned helicopter prototype to simultaneously deal with random wind disturbances and remote communication delays. First, in the helicopter system modeling, the wind gust is not only treated as bounded external load disturbance, but propeller force-thrust coefficient variation that is caused by wind disturbance is also considered. Then, by equivalently treating the remote communication delays as random input delays, a robust attitude controller with energy-to-energy performance is developed for the helicopter system. Lyapunov-Krasovskii functions are utilized to analyze the stability of the proposed robust attitude controller. The gains of controller are calculated by solving linear matrix inequalities. Finally, by selecting Quanser's three degrees of freedom(3-DOF) helicopter prototype as the test bench, three groups of comparative tests are carried out respectively, in which electric fan is utilized to generate external wind gusts while random delays are added to the feedback loop. Compared with conventional H-infinity controller, the effectiveness as well as superiority of proposed control approach is well verified. [ABSTRACT FROM AUTHOR]

Flapping wings have attracted significant interest for use in miniature unmanned flying vehicles. Although numerous studies have investigated the performance of flapping wings under quiescent conditions, effects of freestream disturbances on their performance remain under-explored. In this study, we experimentally investigated the effects of uniform vertical inflows on flapping wings using a Reynolds-scaled apparatus operating in water at Reynolds number ≈ 3600. The overall lift and drag produced by a flapping wing were measured by varying the magnitude of inflow perturbation from JVert = −1 (downward inflow) to JVert = 1 (upward inflow), where JVert is the ratio of the inflow velocity to the wing's velocity. The interaction between flapping wing and downward-oriented inflows resulted in a steady linear reduction in mean lift and drag coefficients, [Formula: see text] and [Formula: see text], with increasing inflow magnitude. While a steady linear increase in [Formula: see text] and [Formula: see text] was noted for upward-oriented inflows between 0 < JVert < 0.3 and JVert > 0.7, a significant unsteady wing–wake interaction occurred when 0.3 ≤ JVert < 0.7, which caused large variations in instantaneous forces over the wing and led to a reduction in mean performance. These findings highlight asymmetrical effects of vertically oriented perturbations on the performance of flapping wings and pave the way for development of suitable control strategies.

Very strong Typhoon Goni (2015) passed over the Yaeyama Islands in southwestern Japan during the rapid intensification stage on 23 August. Surface data collected by the dense network of weather stations as well as Doppler radar observations over the islands revealed a finescale structure in the inner core of the typhoon near the surface. Goni had a clear eye surrounded by a square-shaped eyewall with intense convection. The surface observations revealed that several vortices with a diameter of ~7–10 km accompanied by a pressure deficit were present inside the eye. From the Doppler velocity field, mesovortices with diameters of approximately 10 km were found at the apexes of the square-shaped eyewall. These mesovortices and the inner rainbands emanating outward from the apexes of the polygonal eyewall generally exhibited features typical of vortex Rossby waves. The mesovortices were accompanied by a pressure deficit at the surface and enhanced surface winds. The data also indicated the first observational evidence of near-surface mixing between the eye and eyewall through the mesovortices, that is, the transport of high equivalent potential temperature in the eye toward the eyewall. The radar data revealed that many radar-reflectivity filaments that had a pleated shape with lengths of a few kilometers extended perpendicularly from the inner edge of the eyewall at low levels. The filaments associated with wind perturbations at low levels caused significant wind gusts accompanied by sudden pressure drops and shifts in wind direction at the surface. [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.

The frequency of European convective windstorms, environments in which they form, and their convective organizational modes remain largely unknown. A climatology is produced using 10 233 severe convective wind reports from the European Severe Weather Database between 2009 and 2018. Severe convective wind days have increased from 50 days yr-1 in 2009 to 117 days yr-1 in 2018, largely because of an increase in reporting. The highest frequency of reports occurred across central Europe, particularly Poland. Reporting was most frequent in summer, when a severe convective windstorm occurred every other day on average. The preconvective environment was assessed using 361 proximity soundings from 45 stations between 2006 and 2018, and a clustering technique was used to distinguish different environments from nine variables. Two environments for severe convective storms occurred: Type 1, generally low-shear-high-CAPE (convective available potential energy; mostly in the warm season) and Type 2, generally high-shear-low CAPE (mostly in the cold season). Because convective organizational mode often relates to the type of weather hazard, convective organizational mode was studied from 185 windstorms that occurred between 2013 and 2018. In Type-1 environments, the most frequent convective mode was cells, accounting for 58.5% of events, followed by linear modes (29%) and the nonlinear noncellular mode (12.5%). In Type-2 environments, the most frequent convective mode was linear modes (55%), followed by cells (36%) and the nonlinear noncellular mode (9%). Only 10% of windstorms were associated with bow echoes, a lower percentage than other studies, suggesting that forecasters should not necessarily wait to see a bow echo before issuing a warning for strong winds. SIGNIFICANCE STATEMENT: One of the hazards of convective storms (those that can produce lightning, hail, tornadoes, etc.) is strong winds, which were associated with 78 confirmed fatalities in Europe between 2009 and 2018. We mapped 10233 reports of strong convective winds over Europe during this 10-yr period, finding that all European countries (except Iceland) experienced strong convective winds, with the greatest threat over central Europe. Such windstorms were reported every other day on average during the summer, but occurred during all seasons, including winter. We found specific indicators of the conditions and types of storms that produced the strong winds. These results are important because forecasters will better understand the conditions in which such convective windstorms occur, leading to better forecasts and warnings. [ABSTRACT FROM AUTHOR]

In the last two decades we can notice a significant increase of severe anemological events, which are mostly connected with mesoscale convective systems and a cold front of a deep low-pressure system. One of them are very strong winds with speeds more than 25 m/s. They caused material damage and threatening people's lives. The most dangerous are winds generated by mesoscale convective systems where radar reflectivity signatures of bow echo/derecho appeared. In this paper the area of occurrence of such phenomenon in Poland are described and the features of bow echo signatures on radar images are presented and explained. Additionally one of the most severe event and still very weakly known episode of 11th August 2017 derecho in Poland is analysed. The damage data from European Severe Weather Database (ESWD) were analysed to confirm if the August 11th storm met derecho criteria. To identify the radar reflectivity signatures inside MCC the data from the Polish Institute of Meteorology and Water Management shared on the radar-opadow.pl site were used. The CAPPI 1 km data were very useful to determine the convective forms. After that the data from synoptic station were examined for presenting the running of selected meteorological elements. Finally, some information about material damage in infrastructures and forests are mentioned.

The ERA5 reanalysis product has been compared with hourly near-surface wind speed and gust observations across Sweden for 2013–2017. ERA5 shows closer agreement than the previous ERA-Interim reanalysis with regard to both mean wind speed and gust measurements, although significant discrepancies are still found for inland and mountainous regions. Therefore, attempts have been made to improve formulations of the gust parametrization used in ERA5 by adding an elevation-dependency and by adjusting the convective gust contribution. Major improvements, especially over mountain regions, are achieved when the elevation differences among the stations are considered. Closer agreement between the observed and parametrized gusts is reached when the convective gust contribution is also tuned. The newly designed gust parametrization was also tested for Norway, which is characterized by more complex topography. Wind gusts from the selected Norwegian stations are more realistically simulated when both the elevation-dependency and the tuned convective contribution are implemented, although the parametrized gusts are still negatively biased. Such biases are not explained by the different in gust duration in recorded wind gusts between Sweden and Norway. [ABSTRACT FROM AUTHOR]

Gust prediction is an important element of weather forecasting services, yet reliable methods remain elusive. Peak wind gusts estimated by the meteorologically stratified gust factor (MSGF) model were evaluated at 15 locations across the United States during 2010–17. This model couples gust factors, site-specific climatological measures of "gustiness," with wind speed and direction forecast guidance. The model was assessed using two forms of model output statistics (MOS) guidance at forecast projections ranging from 1 to 72 h. At 11 of 15 sites the MSGF model showed skill (improvement over climatology) in predicting peak gusts out to projections of 72 h. This has important implications for operational wind forecasting because the method can be utilized at any location for which the meteorologically stratified gust factors have been determined. During particularly windy conditions the MSGF model exhibited skill in predicting peak gusts at forecast projections ranging from 6 to 72 h at roughly half of the sites analyzed. Site characteristics and local wind climatologies were shown to exert impacts on gust factor model performance. The MSGF method represents a viable option for the operational prediction of peak wind gusts, although model performance will be sensitive to the quality of the necessary wind speed and direction forecasts. [ABSTRACT FROM AUTHOR]

Wind energy is an important renewable source with a stochastic nature. To capture this energy, variable speed generators are installed in wind towers and different maximum power point (MPP) tracking (MPPT) strategies have been developed in literature to maximise the output power. However, in gusty weather, wind turbines fail to track the optimum operating point due to their relatively high inertia. This paper proposes an innovative MPPT strategy to capture the energy of wind gusts. The developed controller consists of a conventional TSR control together with a bidirectional controller, which takes over the control of the rectifier under varying wind condition. The simulation results show an overall efficiency improvement and more stability for the proposed strategy.

Wind gusts are a major cause of damage to property and the natural environment and a source of noise in seismic networks such as the USArray Transportable Array. Wind gusts cause ground motion through shear stresses, pressure fluctuations and vegetation flexing. Herein, we demonstrate the presence of a seismic response signature to wind gusts at sites across the contiguous USA and explore important geophysical factors that determine the precise nature of wind gust–seismic response relationships. There is a consistent seismic response to wind gusts that is typically manifest at relatively low frequency (0.05–0.1 Hz). However, there is also a marked seasonality in the seismic frequency of peak response, possibly due to seasonal differences in atmospheric conditions and/or vegetation and soil mediation of the atmosphere–ground interaction. The gust–seismic response functions also exhibit a clear dependence on (1) distance from the coast, (2) land cover, (3) topographic complexity and (4) lithology. We propose a generalized methodology to extract wind gust magnitude distributions from seismic networks. Although initial results from this model overestimate the spatial variability in wind gusts as measured by meteorological networks, the analyses described here highlight the potential for new methods to remove wind gust noise from seismic time series and potentially to derive quantitative wind gust estimates from seismic observations. [ABSTRACT FROM AUTHOR]

Wind energy is an important renewable source with a stochastic nature. To capture this energy, variable speed generators are installed in wind towers and different maximum power point (MPP) tracking (MPPT) strategies have been developed in literature to maximise the output power. However, in gusty weather, wind turbines fail to track the optimum operating point due to their relatively high inertia. This paper proposes an innovative MPPT strategy to capture the energy of wind gusts. The developed controller consists of a conventional TSR control together with a bidirectional controller, which takes over the control of the rectifier under varying wind condition. The simulation results show an overall efficiency improvement and more stability for the proposed strategy. [ABSTRACT FROM AUTHOR]

(a) Comparison of wind gust data collected at a DWD station in Dresden and data obtained from ERA interim and COSMO reanalysis data. (b) Electroluminescence image of a photovoltaic module damaged by mechanical loads. Black areas comprise electrically weakly connected areas which cannot contribute to power production anymore and result from the damage caused by the load. (c) Mechanical load set up at the ZAE Bayern, which can be used for simulating wind and snow loads with simultaneous detection of the generation and evolution of solar cell cracks in-situ by means of electroluminescence. • The method allows forecasting of PV module degradation for virtually any site in the world. • Reanalysis is applied for PV degradation forecasting for the first time. • The method can be transferred to other degradation factors. • Site assessment and site-specific module design will benefit from this method. • Excellent agreement between simulated and measured data is achieved. This work demonstrates the capability of meteorological reanalysis data for estimating mechanical stresses due to wind gusts on photovoltaic modules. Besides the immediate power loss caused by the inactive solar cell areas within the module, the influence of cracks induced by such mechanical loads on the ageing and degradation behavior of PV modules is in the focus of current PV reliability research. To choose and/or design the optimum module for a given deployment site at the most economical cost, the mechanical loads the module will be exposed to during its service life need to be estimated. Hence, this paper introduces a new methodology for assessing and deducing site-specific mechanical load scenarios which are expected to be imposed on photovoltaic modules by wind gusts for a given location. The approach presented here is based on the linear calibration of meteorological reanalysis data with publicly available and standardized high-quality surface weather station data of local weather services. In this fashion, site-specific mechanical load scenarios can be deduced for virtually any site covered by the respective reanalysis data set. These scenarios can be used for developing site-specific module qualification tests, thereby providing a methodology enabling climate-specific module designs or specifications. This methodology can also be transferred to virtually any location and other climate and weather parameters such as snow loads, UV dose, etc. Hence a complete assessment of all weather-related degradation scenarios could be deduced eventually. [ABSTRACT FROM AUTHOR]

This study documents atmospheric conditions, development, and evolution of a severe weather outbreak that occurred on 11 August 2017 in Poland. The emphasis is on analyzing system morphology and highlighting the importance of a mesovortex in producing the most significant wind damages. A derecho-producing mesoscale convective system (MCS) had a remarkable intensity and was one of the most impactful convective storms in the history of Poland. It destroyed and partially damaged 79 700 ha of forest (9.8 million m3 of wood), 6 people lost their lives, and 58 were injured. The MCS developed in an environment of high 0–3-km wind shear (20–25 m s−1), strong 0–3-km storm relative helicity (200–600 m2 s−2), moderate most-unstable convective available potential energy (1000–2500 J kg−1), and high precipitable water (40–46 mm). Within the support of a midtropospheric jet, the MCS moved northeast with a simultaneous northeastward inflow of warm and very moist air, which contributed to strong downdrafts. A mesocyclone embedded in the convective line induced the rear inflow jet (RIJ) to descend and develop a bow echo. In the mature stage, a supercell evolved into a bookend vortex and later into a mesoscale convective vortex. Swaths of the most significant wind damage followed the aforementioned vortex features. A high-resolution simulation performed with initial conditions derived from GFS and ECMWF global models predicted the possibility of a linear MCS with widespread damaging wind gusts and embedded supercells. Simulations highlighted the importance of cloud cover in the preconvective environment, which influenced the placement and propagation of the resulting MCS. [ABSTRACT FROM AUTHOR]

The predictability of high impact weather events on multiple time scales is a crucial issue both in scientific and socio-economic terms. In this study, a statistical-dynamical downscaling (SDD) approach is applied to an ensemble of decadal hindcasts obtained with the Max-Planck-Institute Earth System Model (MPI-ESM) to estimate the decadal predictability of peak wind speeds (as a proxy for gusts) over Europe. Yearly initialized decadal ensemble simulations with ten members are investigated for the period 1979–2005. The SDD approach is trained with COSMO-CLM regional climate model simulations and ERA-Interim reanalysis data and applied to the MPI-ESM hindcasts. The simulations for the period 1990–1993, which was characterized by several windstorm clusters, are analyzed in detail. The anomalies of the 95 % peak wind quantile of the MPI-ESM hindcasts are in line with the positive anomalies in reanalysis data for this period. To evaluate both the skill of the decadal predictability system and the added value of the downscaling approach, quantile verification skill scores are calculated for both the MPI-ESM large-scale wind speeds and the SDD simulated regional peak winds. Skill scores are predominantly positive for the decadal predictability system, with the highest values for short lead times and for (peak) wind speeds equal or above the 75 % quantile. This provides evidence that the analyzed hindcasts and the downscaling technique are suitable for estimating wind and peak wind speeds over Central Europe on decadal time scales. The skill scores for SDD simulated peak winds are slightly lower than those for large-scale wind speeds. This behavior can be largely attributed to the fact that peak winds are a proxy for gusts, and thus have a higher variability than wind speeds. The introduced cost-efficient downscaling technique has the advantage of estimating not only wind speeds but also estimates peak winds (a proxy for gusts) and can be easily applied to large ensemble datasets like operational decadal prediction systems.

The paper describes the development of predictive equations of windthrow for five tree species based on remote sensing of wind-affected stands in southwestern New Brunswick (NB). The data characterises forest conditions before, during and after the passing of extratropical cyclone Arthur, July 4–5, 2014. The five-variable logistic function developed for balsam fir (bF) was validated against remote-sensing-acquired windthrow data for bF-stands affected by the Christmas Mountains windthrow event of November 7, 1994. In general, the prediction of windthrow in the area agreed fairly well with the windthrow sites identified by photogrammetry. The occurrence of windthrow in the Christmas Mountains was prominent in areas with shallow soils and prone to localised accelerations in mean and turbulent airflow. The windthrow function for bF was subsequently used to examine the future impact of windthrow under two climate scenarios (RCP’s 4.5 and 8.5) and species response to local changes anticipated with global climate change, particularly with respect to growing degree-days and soil moisture. Under climate change, future windthrow in bF stands (2006–2100) is projected to be modified as the species withdraws from the high-elevation areas and NB as a whole, as the climate progressively warms and precipitation increases, causing the growing environment of bF to deteriorate.