Extreme Wind Gusts Research Articles

MPPT controller design and comparison of selected site Cox’s Bazar

Wind energy is a popular green source of energy. Like other countries, Bangladesh is also in a process of incorporating wind turbine generator (WTG) into its existing power system. Out of several locations, Cox’s Bazar has the great potential to adopt WTG. The location has every suitability to be selected for offshore WTG farm. From technical perspective, power coefficient plays a significant role in determining the power output. Against variables, maximum power coefficient (Cp) is desired to extract the maximum power from the system. Tip speed ratio (TSR) and pitch angle are the common parameters to be controlled for maximum power point tracking (MPPT). Conventionally, the Proportional - Integral - Derivative (PID) controller is used but they have their limitations against high nonlinearity caused by wind turbine aerodynamics. This paper proposes three types of controllers: Proportional controller (PI), artificial neural network (ANN), and fuzzy logic controllers. The controller is applied to both TSR and pitch angle control. Pitch angle is controlled to avoid the extreme wind gusts. All the simulation works are carried out in MATLAB and Simulink. This paper also analyzes the technical feasibility of the Cox’s Bazar location for offshore WTG.

On the Multiscale Processes Leading to an Extreme Gust Wind Event in East China: Insights From Radar Wind Profiler Mesonet Observations

AbstractIn this study, a record‐breaking surface gust wind event of over 45 m s−1, which occurred in the coastal region of East China during the early evening hours of 30 April 2021, is examined. The dynamical characteristics of this event is explored by using a high‐resolution mesonet comprised of eight radar wind profilers (RWPs), surface observations, radar and satellite data. Observational analyses show the development of several cloud clusters ahead of the axis of a midlevel trough with pronounced baroclinicity, and the subsequent organization into a comma‐shaped squall system with a leading convective line over land and a trailing stratiform region moved offshore. The latter is embedded by a mesovortex with intense northerly rear inflows descending to the surface, accounting for the generation of the gusty winds. Results indicate the different roles of multi‐scale processes in accelerating the surface winds to extreme intensity. Specifically, the large‐scale baroclinic trough provides intense background rear inflows that are enhanced by the formation of the mesovortex, while moist downdrafts in the rear inflows account for the downward transport of horizontal momentum, leading to the generation of intense cold outflows and gusty winds close to the leading convective line. Despite the lack of sufficient observations for quantitative analysis, this study provides a qualitative analysis that offers valuable insights into the dynamics of extreme gusty winds. Moreover, the above results underscore the value of RWP mesonet observations in enhancing our understanding of extreme wind events and in improving the nowcasting and prediction efforts in the future.

Vapor kinetic energy for the detection and understanding of atmospheric rivers

Poleward water vapor transport in the midlatitudes mainly occurs in meandering filaments of intense water vapor transport, spanning thousands of kilometers long and hundreds of kilometers wide and moving eastward. The water vapor filaments are known as atmospheric rivers (ARs). They can cause extreme wind gusts, intense precipitation, and flooding along densely populated coastal regions. Many recent studies about ARs focused on the statistical analyses of ARs, but a process-level understanding of ARs remains elusive. Here we show that ARs are streams of air with enhanced vapor kinetic energy (VKE) and derive a governing equation for Integrated VKE to understand what contributes to the evolution of ARs. We find that ARs grow mainly because of potential energy conversion to kinetic energy, decay largely owing to condensation and turbulence, and the eastward movement is primarily due to horizontal advection of VKE. Our VKE framework complements the integrated vapor transport framework, which is popular for identifying ARs but lacks a prognostic equation for understanding the physical processes.

Convection-permitting climate model representation of severe convective wind gusts and future changes in southeastern Australia

Abstract. Previous research has suggested that the frequency and intensity of surface hazards associated with thunderstorms and convection, such as severe convective winds (SCWs), could potentially change in a future climate due to global warming. However, because of the small spatial scales associated with SCWs, they are unresolved in global climate models, and future climate projections are uncertain. Here, we evaluate the representation of SCW events in a convection-permitting climate model (Bureau of Meteorology Atmospheric Regional Projections for Australia, BARPAC-M) run over southeastern Australia for the months of December–February. We also assess changes in SCW event frequency in a projected future climate for the year 2050 and compare this with an approach based on identifying large-scale environments favourable for SCWs from a regional parent model (BARPA-R). This is done for three different types of SCW events that have been identified in this region, based on clustering of the large-scale environment. Results show that BARPAC-M representation of the extreme daily maximum wind gust distribution is improved relative to the gust distribution simulated by the regional parent model. This is due to the high spatial resolution of BARPAC-M output, as well as partly resolving strong and short-lived gusts associated with convection. However, BARPAC-M significantly overestimates the frequency of simulated SCW events, particularly in environments having steep low-level temperature lapse rates. A future decrease in SCW frequency under conditions with steep lapse rates is projected by BARPAC-M, along with less frequent favourable large-scale environments. In contrast, an increase in SCW frequency is projected under conditions of high surface moisture, with more frequent favourable large-scale environments. Therefore, overall changes in SCWs for this region remain uncertain, due to different responses between event types, combined with historical model biases.

Modeling and observations of North Atlantic cyclones: Implications for U.S. Offshore wind energy

To meet the Biden-Harris administration's goal of deploying 30 GW of offshore wind power by 2030 and 110 GW by 2050, expansion of wind energy into U.S. territorial waters prone to tropical cyclones (TCs) and extratropical cyclones (ETCs) is essential. This requires a deeper understanding of cyclone-related risks and the development of robust, resilient offshore wind energy systems. This paper provides a comprehensive review of state-of-the-science measurement and modeling capabilities for studying TCs and ETCs, and their impacts across various spatial and temporal scales. We explore measurement capabilities for environments influenced by TCs and ETCs, including near-surface and vertical profiles of critical variables that characterize these cyclones. The capabilities and limitations of Earth system and mesoscale models are assessed for their effectiveness in capturing atmosphere–ocean–wave interactions that influence TC/ETC-induced risks under a changing climate. Additionally, we discuss microscale modeling capabilities designed to bridge scale gaps from the weather scale (a few kilometers) to the turbine scale (dozens to a few meters). We also review machine learning (ML)-based, data-driven models for simulating TC/ETC events at both weather and wind turbine scales. Special attention is given to extreme metocean conditions like extreme wind gusts, rapid wind direction changes, and high waves, which pose threats to offshore wind energy infrastructure. Finally, the paper outlines the research challenges and future directions needed to enhance the resilience and design of next-generation offshore wind turbines against extreme weather conditions.

Instantaneous 2D extreme wind speed prediction using the novel Wind Gust Prediction Net based on purely convolutional neural mechanism

Accurate prediction of spatial–temporal extreme wind gust is vital for the wind farm dynamic regulation, the floating wind turbine deployment and its early warning. Deep-learning approaches have been applied for wind prediction to alleviate the computational challenges of traditional numerical models. Yet, most previous studies emphasized the prediction accuracy only employing location-specific dataset, such methodologies are site-specific and ignore the importance of spatial–temporal fidelity. Furthermore, the Recurrent Neural Networks (RNN)-based approach previous employed exhibit low efficiency in terms of model convergence and on the aspect of practical engineering purposes. This study firstly proposed the wind gust prediction net (WGPNet), using residual learning with attention modulations to predict the instantaneous spatial–temporal wind gust in the West Pacific region with great potential wind-energy. And a public reanalysis dataset with very high resolution (0.25° x 0.25°) was employed to verify the proposed method under different criteria. The overall RMSE of predicted gust fields obtained by the proposed method dropped to 0.18 m/s. Comprehensive discussions with both temporal and spatial perspective, revealing that the proposed model can offer an accurate 2D wind gust prediction along timeline (the PCC equals to 0.98).

Bias and variance reduction of high return levels for extreme hazard modelling

AbstractMany existing extremal data span only a few decades that result in large uncertainty in the estimated shape parameter of the extreme hazard model. This in turn leads to potentially large bias and variance of extrapolated extreme values at high return levels. This paper illustrates a statistical method that helps obtain hazard models that produce estimates at high return levels with reduced bias and variance. This method makes use of the maximum recorded values of extremal data that are independently recorded from a number of observational sites or generated by heterogeneous mechanisms. The logarithmically transformed average recurrence interval of the maximum recorded value at a site is shown to follow the Gumbel (Type I extreme value) distribution; therefore, multiple, say $$m$$ m , observational sites provide a sample of size $$m$$ m log-transformed average recurrence intervals, each from a distinct site. The sample can be treated as a sample from a Gumbel distribution, irrespective of the underlying hazard-generating mechanisms or the statistical hazard models. Application of the method is illustrated by analysis of the extreme wind gust data from automatic weather stations in South Australia and shown to lead to significant reduction of 2.9 m/s in potential bias of the mean and a reduction of 98.1% in variance of the 500-year gust speed at Adelaide Airport. The results are compared to the specifications in the Australian standard AS/NZS 1170.2:2021 and indicate that the standard may have overestimated the wind gust hazard; hence, the specified design wind speeds may fall on the conservative side for South Australia.

Short-term damping estimation for time-varying vibrating structures in nonstationary operating conditions

Various structures exhibit time-varying modal properties driven by short-term variability of environmental and operational conditions, which can cause considerable short-term variations in the modal characteristics, and modal damping, in particular. This study focuses on short-term damping estimation based on operational response measurements, for which a Gaussian process time-dependent auto-regressive moving average time series model is proposed. The model coefficients are represented by basis functions which express dependence on influencing environmental and operational variables, to account for various phenomena driving the time-varying nature of the modal characteristics. The method is verified by estimating short-term damping from the response of a simulated two-mode model. The applicability of the method for more complex cases is demonstrated by estimating the short-term damping of a multi-megawatt wind turbine in operation (and during an extreme wind gust) by means of its simulated edgewise blade response. The results demonstrate that the proposed method offers an enabling approach towards short-term damping and natural frequency estimation based on response measurements.

Canopy gaps and associated losses of biomass – combining UAV imagery and field data in a central Amazon forest

Abstract. Understanding mechanisms of tree mortality and the dynamics of associated canopy gaps is relevant for robust estimates of carbon balance in forests. We combined monthly RGB images acquired from an unoccupied aerial vehicle with field surveys to identify gaps in an 18 ha plot installed in an old-growth central Amazon forest. We measured the size and shape of gaps and analyzed their temporal variation and correlation with rainfall over a period of 28 months. We further described associated modes of tree mortality (i.e., snapping, uprooting and standing dead) and branch fall and quantified associated losses of biomass. In total, we detected 32 gaps either in the images or field ranging in area from 9 to 835 m2. Relatively small gaps (< 39 m2) opened by branch fall were the most frequent (11 gaps). Out of 18 gaps for which both field and image data were available, three could not be detected remotely. Gaps observed in the field but not captured on the imagery were relatively small and mainly formed by the fall of branches from live and standing dead trees. Our data show that ∼ 17 % of the tree-mortality and branch-fall events only affected the lower canopy and the understory of the forest and are likely neglected by top-of-the-canopy assessments. Regardless of the detection method, the size distribution was best described by a lognormal function for gaps starting from the smallest detected size (9 and 10 m2 for field and imagery data, respectively), and the Weibull and Power functions for gaps larger than 25 m2. Properly assessing associated confidence intervals requires larger sample sizes. Repeated field measurements reveal that gap area does not differ significantly among modes of tree mortality or branch fall in central Amazon forests, with the last contributing the least to biomass loss. Predicting mechanisms of gap formation based on associated area and biomass loss remains challenging, which highlights the need for larger datasets. The rate of gap area formation was positively correlated with the frequency of extreme rainfall events, which may be related to a higher frequency of storms propagating extreme rain and wind gusts. While remote sensing has proven to be an accurate and precise method for mapping gaps compared to field data (i.e., ground truth), it is important to note that our sample size was relatively small. Therefore, the extrapolation of these results beyond our study region and landscape shall be made cautiously. Apart from improving landscape assessments of carbon balance, regional information on gap dynamics and associated mechanisms of formation are fundamental to address forest responses to altered disturbance regimes resulting from climate change.

Extreme value statistical analysis of meteorological parameters observed at Rawatbhata Rajasthan site

Abstract Knowledge of extreme meteorological parameters plays an important role, for the selection of any civil structure at nuclear site or to design important nuclear structures to withstand design basis values of meteorological extremes that are likely to occur during the lifetime of these structures. The method used for the evaluation of extreme parameters is least square fit method. In this study, statistical analyses of extreme values of meteorological parameters at Rawatbhata Rajasthan site (RR site) are presented for the period 1980 to 2022 and for rainfall, it is for the period 1964 to 2022. Meteorological parameters such as extreme wind speed and wind gust (5 min) at 10 m and 30 m heights, extreme surface air temperature, extreme atmospheric pressure and extreme rainfall (yearly, monthly, and daily) are evaluated and discussed. From the extreme value analysis, it is observed that the variables for monthly maximum rainfall, annual maximum rainfall, maximum air temperature, maximum atmospheric pressure, maximum hourly wind speed at 10 m and 30 m heights and wind gust (5 min.) at 10 m height obey Fisher-Tippette Type I distribution, whereas daily maximum rainfall, annual minimum rainfall, minimum air temperature, minimum relative humidity, minimum atmospheric pressure and wind gust (5 min.) at 30 m height obey Fisher-Tippette Type II distribution. Distribution function parameters, i.e., location, scale, and shape parameter for each variable, have been determined and design basis values corresponding to return periods of 50 years and 100 years are derived and presented. The derived extreme values are particularly useful for arriving at suitable design values to ensure the safety of civil structures at RR site area with respect to stresses due to weather conditions. The values are also compared with corresponding values at other nuclear reactor sites of Tarapur, Narora, and Kalpakkam.

Turbulence regimes in the nocturnal roughness sublayer: Interaction with deep convection and tree mortality in the Amazon

We investigated the influence of seasonality and proximity to the forest canopy on nocturnal turbulence regimes in the roughness sublayer of a Central Amazon forest. Since convective systems of different scales are common in this region, we also analyzed the effect of extreme wind gusts (propagated from convective downdrafts) on the organization of the turbulence regimes, and their potential to cause the mortality of canopy trees. Our data include high-frequency winds, temperature and ozone concentration at different heights during the dry and wet seasons of 2014. In addition, we used critical wind-speed data derived from a tree-winching experiment and a modeling study conducted in the same study site. Two different turbulence regimes were identified at three heights above the canopy: a weakly stable (WS) and a very stable regime (VS). The threshold wind speeds that mark the transition between turbulence regimes were larger during the dry season and increased as a function of the height above the canopy. The turbulent fluxes of sensible heat and momentum during the WS accounted for 88% of the entire nighttime flux. Downdrafts occurred only in the WS and favored a fully coupled state of wind flow along the canopy profile. The destructive potential of winds was four times higher than on nights without downdrafts.

Analysis and characterisation of extreme wind gust hazards in New South Wales, Australia

Extreme wind gusts cause major socioeconomic damage, and the rarity and localised nature of those events make their analysis challenging by either modelling or empirical approaches. A 23-year long data record from 29 automatic weather stations located in New South Wales (eastern Australia) is used to study the distribution, frequency and average recurrence intervals (ARIs) of extreme gusts via a peaks-over-threshold approach. We distinguish between gust events generated by synoptic phenomena (e.g. cyclones and frontal systems), hereafter called “synoptic events”, and convective phenomena (i.e. thunderstorms), hereafter called “convective events”, using the wind time series. For synoptic events the frequency of gusts >25 m/s decreases systematically inland from the coast, in contrast to convective gusts which are more uniformly distributed geographically and occur more often than synoptic gusts at nearly all inland locations. At inland locations the most extreme wind gusts are likewise dominated by convective events, whereas at coastal stations both gust types have similar intensities at low ARIs but convective events again dominate at the highest ARIs. Extreme gust directions were found to be predominantly westerly at inland locations and southerly at coastal ones, with more variable direction for convective than synoptic events. This study confirms the dominant role of thunderstorms in producing the most extreme gusts in the region, and shows that wind risk varies strongly with distance from the coast.

Hillslope roughness reveals forest sensitivity to extreme winds

Windthrow, or the uprooting of trees by extreme wind gusts, is a natural forest disturbance that creates microhabitats, turns over soil, alters hydrology, and removes carbon from the above-ground carbon stock. Long recurrence intervals between extreme wind events, however, make direct observations of windthrow rare, challenging our understanding of this important disturbance process. To overcome this difficulty, we present an approach that uses the geomorphic record of hillslope topographic roughness as a proxy for the occurrence of windthrow. The approach produces a probability function of the number of annual windthrow events for a maximum wind speed, allowing us to explore how windthrow or tree strengths may change due to shifting wind climates. Slight changes to extreme wind speeds may drive comparatively large changes in windthrow production rates or force trees to respond and change the distribution. We also highlight that topographic roughness has the potential to serve as an important archive of extreme wind speeds.

Analysis of extreme wind gusts using a high-resolution Australian Regional Reanalysis

The characterisation of extreme wind gust speeds has historically relied on data from observations, while numerically modelling these events is limited due to their rarity and localised nature. However, the ongoing increase in computational power now allows extreme events to be characterised in fine-scale numerical data. Extreme gust events are examined using 24 years of 1.5 km horizontal grid reanalysis data for eastern Australia, and compared to those based on 1-minute data from station observations. We estimate return values over long periods using the generalised Pareto distribution (GPD) peaks-over-threshold approach. An automated algorithm that selects an optimal threshold at each grid point is outlined and gives similar results to an approach based on pre-fixing the shape factor value similar to what is applied in Australian building design standards. We also present a decision tree model that skillfully distinguishes between convective (i.e., thunderstorm) and synoptic (e.g., frontal system and cyclone) gust events using only hourly reanalysis data. The reanalysis shows high synoptic gust intensities and frequencies along the coast, consistent with station observations, and that synoptic-type extreme gusts are enhanced by topography (higher occurrence frequencies in mountain areas) while convective-type events are less affected. The reanalysis systematically overestimates parameterised gust activity, while compensating for this at many locations by underestimating the resolved wind speed, thus often producing gusts close to observed. With the proviso that these issues are considered, we find that the information on wind gusts provided by reanalysis data may be quite useful especially in regions with low observational network coverage.

Global-local analysis of cost-optimal onshore wind turbine configurations considering wind classes and hub heights

This study aims to contribute to the field of energy systems modelling with high-resolution cost-optimised onshore wind turbine configurations and an openly available hourly data of wind electricity yield on a global-local scale. It introduces a more rigorous and novel methodology to estimate the wind electricity yield for lowest cost electricity generation by considering different wind classes and hub height related capital expenditures along with limitations induced by extreme wind gusts. Based on up-to-date financial and technical assumptions, including the latest power curves for ENERCON wind turbines, the results of this study show that there exists a certain hub height that enables wind turbines to deliver the lowest cost electricity and growing beyond that height does not pay-off the rise in capital expenditures needed for stronger foundations and taller and sturdier towers. Class III turbines provide higher full load hours and more stable hourly generation profiles, but in some areas higher cost does not pay-off or wind gusts become a limiting factor. The application of this novel multi-turbine multi-hub height high resolution optimisation results to energy system modelling would significantly increase the quality of modelling by improved estimation of the wind generation cost at different locations.

Simulation of buckling-driven progressive damage in composite wind turbine blade under extreme wind loads

Wind turbine blades are continuously upscaled in size to meet the increasing demand of low cost renewable energy. The larger and slender blades with reduced mass are susceptible to large-deflection bending and instability during extreme wind gusts. The loading can induce interactive damage modes such as adhesive bond failure and composite skin damage progressively degrading the blade structural performance. The structural deformation and instability as well as progressive damage in composite blade is analyzed by developing finite element (FE) models using ANSYS software. First, a geometric nonlinear FE analysis of the 3D blade is performed to predict its deflection and regions of high stresses. The results demonstrated that high compressive stresses in the blade suction side trigger local skin buckling; thus initiating debonding of weak adhesive interface joint between skin and spar combined with skin damage. Subsequently, the interactive buckling-driven skin-spar debonding and composite skin damage in the identified region are analyzed through a submodel using cohesive zone model (CZM) as well as continuum damage mechanics (CDM) based progressive failure analysis, respectively, through a user-defined material subroutine. The results indicated that buckling-driven debonding of adhesive interface between skin and spar was primary damage mode leading to progressive failure of the blade composite skin. Consequently, the blade ultimate load carrying-ability was governed by coupled adhesive debonding and progressive skin damage phenomena, which is in good agreement with published experimental results. The developed simulation approach is capable to efficiently analyze the interactive buckling-induced interface damage as well as progressive failure of composite blade structure. In this work, modelling of interaction between interface debonding and skin damage, based on the CZM and CDM scheme, is a novel methodology for progressive damage analysis of blade under extreme loading.

Залежність виникнення поривів вітру від аномалій тиску та температури повітря на території Північно-Західного Причорнорномор’я

In order to reduce the devastating consequences of extreme wind gusts, a significant amount of research is being carried out in the world to study the conditions for the occurrence of this phenomenon, the dynamics of its spatio-temporal distribution, and a possible potential change in the wind regime against the background of modern climate change. Nowadays, it appears that not much research has been carried out to determine the possible impact of climate change on future wind gusts on the territory of Ukraine and, in particular, in the Northern Black Sea region. Global warming scenarios are expected to change the severity and frequency of future wind gusts by the end of this century. Considering this, a study was made of the spatio-temporal features of the formation of wind gusts in the North-Western Black Sea region for the period 2005-2020, and the dependence of their occurrence on air temperature and atmospheric pressure anomalies, which can be used to analyze possible changes of future wind regimes as a result of climate change. To analyze the occurrence of wind gusts in the North-Western Black Sea region, data from three stations located in different parts of the region were selected. The data of METAR telegrams on wind gusts at the Odessa-AMSC, Mykolaiv, Kherson stations and surface weather observations data for the period from January 2005 to December 2020 were used as source material in the work. For statistical analysis of daily air temperature and pressure anomalies, NCEP/NCAR Reanalysis archive data for the same period were used. The obtained results show that in the territory of the North-Western Black Sea region over the past 15 years, wind gusts in the gradation of 13-15 (m.s-1) prevailed, and the speed of wind gusts in most cases rised with an increase of the daily temperature anomaly (from 0.0 to 5.0 °С) and a decrease of the daily pressure anomaly (from 0 to -7.0 hPa).

Estimating the Risk of Extreme Wind Gusts in Tropical Cyclones Using Idealized Large-Eddy Simulations and a Statistical–Dynamical Model

Abstract 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.

Tropical-cyclone-wind-induced flutter failure analysis of long-span bridges

Continuously growing number and span length of long-span bridges in coastal region of China require a robust aeroelastic design to against the tropical-cyclone (TC) -induced flutter instability. Instead of using the deterministic approach, this study presents a Monte-Carlo-technique-based framework to analyze the flutter reliability of a long-span suspension bridge subjected to TC winds. A limit state function formulated as the difference between the flutter capacity and the extreme gust wind, termed as the product of a random gust factor and the TC mean wind hazard is employed. The flutter fragility curve in terms of the critical wind speed is derived using 2D and 3D flutter analysis models accounting for the structural modal and damping randomness as well as experiment-induced errors of aeroelastic flutter derivatives. The TC wind hazard curves at the height of the bridge deck described as the probability of exceedance at any given years of interest versus the extreme 10-min mean wind speed are estimated through generating a large quantity of synthetic tracks around the bridge site. The probabilistic solutions of the gust factor associated with any gust duration of interest are determined utilizing the statistics of observed TCs coupled with a theoretical model. The TC-induced flutter failure probabilities of present bridge are then predicted in different combinations of gust duration, surface roughness length and flutter analysis models, which enables the assessment of flutter risk subjected to TC winds as compared to code-suggested target reliability indices. The present reliability analysis provides some basic information to assist and enhance the flutter-resistant design of long-span bridges due to TC winds during the preliminary stage.

Extreme windstorms and sting jets in convection-permitting climate simulations over Europe

Extra-tropical windstorms are one of the costliest natural hazards affecting Europe, and windstorms that develop a sting jet are extremely damaging. A sting jet is a mesoscale core of very high wind speeds that occurs in Shapiro–Keyser type cyclones, and high-resolution models are required to adequately model sting jets. Here, we develop a low-cost methodology to automatically detect sting jets, using the characteristic warm seclusion of Shapiro–Keyser cyclones and the slantwise descent of high wind speeds, within pan-European 2.2 km convection-permitting climate model (CPM) simulations. The representation of wind gusts is improved with respect to ERA-Interim reanalysis data compared to observations; this is linked to better representation of cold conveyor belts and sting jets in the CPM. Our analysis indicates that Shapiro–Keyser cyclones, and those that develop sting jets, are the most damaging windstorms in present and future climates. The frequency of extreme windstorms is projected to increase by 2100 and a large contribution comes from sting jet storms. Furthermore, extreme wind speeds and their future changes are underestimated in the global climate model (GCM) compared to the CPM. We conclude that the CPM adds value in the representation of extreme winds and surface wind gusts and can provide improved input for impact models compared to coarser resolution models.