Gust Speed Research Articles

Improving the predictive capacity of the windthrow risk model ForestGALES with long-term monitoring data – A statistical calibration approach

Winter storms cause severe damage in German forests. Different modelling approaches have already been used to try and map endangered areas to minimize the risk of wind damage by stand adaption. Prevalent models for Germany include empirical-statistical and hybrid-mechanistic models, such as ForestGALES (FG). As of yet, FG is not extensively used in Germany as its parametrization requires extensive experimental efforts to derive regionally sensitive species-specific parameters. Here, we implement a statistical calibration approach for German forest conditions with observed damage from single tree data, soil types, topography (topex) and gust speed data. We use simulated annealing to generate new species-specific values for the tree species, Norway spruce, European beech, and Douglas fir from within the range of all coniferous (deciduous) species for Norway spruce and Douglas fir (European beech) and an additional 10 % buffer around the default species-specific values for each species. We compare two optimization approaches: First, we aim to maximize the Matthew’s correlation coefficient (MCC), which is calculated from the confusion matrix, applying a fixed classification threshold of 0.5. In comparison to the optimization at a fixed threshold, we optimized the species-specific parameters by maximizing the area-under-curve (AUC) value directly generated from the receiver-operator characteristic (ROC) analysis. We compare our statistical parametrizations for the considered species to those currently implemented in FG and validate the resulting damage probabilities based on confusion matrices and related performance measures. We created separate parametrizations for a single-tree and stand-wide analysis of storm damage risk, which we validated with gust speed data for Germany. Our results show, that for the single-tree method, MCC improved for all species: By 0.26 (0.22) for the calibration (validation) subset for Douglas fir, by 0.22 (0.18) for Norway spruce and by 0.08 (0.05) for European beech. The optimization for the stand-method shows an increase in MCC as well, with results not being considered due to low numbers of observation data. We show that for German forests, FG’s predictive capability can be improved by statistical optimization when no tree-pulling data is available, which could be valuable for creating further regionalizations of FG.

Impact of Meteorological Conditions on Overhead Transmission Line Outages in Lithuania

This study investigates the impact of meteorological conditions on unplanned outages of overhead transmission lines (OHTL) in Lithuania’s 0.4–35 kV power grid from January 2013 to March 2023. Data from the Lithuanian electricity distribution network operator and the Lithuanian Hydrometeorological Service were integrated to attribute outage events with weather conditions. A Bayesian change point analysis identified thresholds for these meteorological factors, indicating points at which the probability of outages increases sharply. The analysis reveals that wind gust speeds, particularly those exceeding 21 m/s, are significant predictors of increased outage rates. Precipitation also plays a critical role, with a 15-fold increase in the relative number of outages observed when 3 h accumulated rainfall exceeds 32 mm, and a more than 50-fold increase for 12 h snowfall exceeding 22 mm. This study underscores the substantial contribution of lightning discharges to the number of outages. In forested areas, the influence of meteorological conditions is more significant. Furthermore, the research emphasizes that combined meteorological factors, such as strong winds accompanied by rain or snow, significantly increase the risk of outages, particularly in these forested regions. These findings emphasize the need for enhanced infrastructure resilience and targeted preventive measures to mitigate the impact of extreme weather events on Lithuania’s power grid.

Meteorological regime of the Elbrus high-mountain zone during the accumulation period

Unique automated meteorological observations were carried out on the southern slope of Elbrus, near Pastukhov Rocks, at 4700 m a.s.l., during the 2021–2022 accumulation season. Data were obtained on air temperature, humidity, wind speed and direction, snowdrift and radiation fluxes with a temporal resolution of 1 minute or less. Analysis of the data series showed that the representative winter air temperature at this altitude on the southern slope of Elbrus is –10 °С, and the minimum is –36.4 °С; the partial pressure of water vapor does not exceed 3.5 hPa. At the same time, the average daily maximum of wind speed amounted 13.1 m s–1 with the absolute maximum of 54.1 m/s. Snowstorms with a snow transport intensity of more 0.1 kg/m2s–1 are quite common phenomenon in winter, while the maximum average value of the transport reaches 0.87 kg/m2s–1. An empirical relationship was established between the average hourly wind speed and the maximum gust speed for the same period, and it was shown that for these conditions the wind gust exceeds the average hourly wind speed by 1.8 times, while the representative value of the standard deviation of wind speed is 5.8 m s–1. This information may be useful not only for the glaciologic problems and modeling, but also for construction and engineering surveys, which are relevant in view of the present-day active development of the mountain ski infrastructure on the southern macro-slope of the Elbrus. In addition, the obtained series of instrumental observations were used to assess the quality of reanalysis data for high mountain regions taking as an example the ERA5. The ERA5 reanalysis was demonstrated to reproduce rather successfully the air temperature, wind speed and humidity in high mountain conditions, but extreme values for all these parameters are underestimated. Thus, the minimum temperature in winter turned out to be overestimated by 2 °C, and the maximum was underestimated by 4 °C, while the wind speed, according to the ERA5 reanalysis, never exceeded 40 m/s during the above observation period. It is also shown that the FlowCapt4 acoustic blizzard gauge (driftometer) can be used to estimate average wind speeds since it is less sensitive to severe high-altitude conditions compared to acoustic and cup anemometers.

Compounded wind gusts and maximum temperature via semiparametric copula in the risk assessments of power blackouts and air conditioning demands for major cities in Canada

A semiparametric copula joint framework was proposed to model wind gust speed (WGS) and maximum temperature (MT) in Canada, using Gaussian kernel density estimation (GKDE) with parametric copulas. Their joint probability estimates allow for a better understanding of the risk of power blackouts and the demand for air conditioning in the community. The bivariate framework used two extreme sample groups to define extreme pairs at different time lags, i.e., 0 to ± 3 days, annual maximum WGS (AMWGS) and corresponding MT and annual highest MT (AHMT) and corresponding WGS. A thorough model performance comparison indicated that GKDE outperformed the parametric models in defining the marginal distribution of selected univariate series. Significant positive correlations were observed among extreme pairs, except for Calgary and Halifax stations, with inconsistent correlation variations based on selected cities and lag time. Various parametric 2-D copulas were selected to model the dependence structure of bivariate pairs at different time lags for selected stations. AMWGS or AHMT events, when considered independently, would be stressful for all stations due to high estimated quantiles with low univariate RPs. The bivariate events exhibited lower AND-joint RPs with moderate to high design quantiles, indicating a higher risk of power blackouts and heightened air-conditioning demands, which varied inconsistently with time lags across the station. The bivariate AMWGS and corresponding MT events would be stressful in Regina, Quebec City, Ottawa, and Edmonton, while AHMT and corresponding WGS events in Toronto, Regina, and Montreal. Conversely, Vancouver poses a lower risk of joint action of pairs AHMT and corresponding WGS events. These hazard statistics can help in better planning for community well-being during extreme weather.

Идентификация порывов ветра при шквале по данным сети ДМРЛ-С и численного моделирования

Experimental methods are considered for identifying squalls in three intensity gradations according to the DMRL-C network data and refining the speed of gusts during a squall using numerical modeling. Particular attention is paid to the identification of severe squalls. The series of severe squalls on July 29, 2023 in the Middle Volga region was analyzed. The results of the verification of squalls using observational data from weather stations and wind gust speeds recorded by automatic weather stations are presented.

Gust-detection from velocity azimuth display Doppler wind lidar profiling at the WiValdi research park.

This study presents a new retrieval for gusts from Doppler wind lidar (DWL) velocity azimuth display (VAD) scans. The scanning mode which is presented allows for retrieval of mean wind speed, vertical wind speed, turbulence kinetic energy and peak gust speed with a single strategy. It is validated against sonic anemometer measurements at 90 m above ground and a reference DWL gust mode with faster scanning speed at the boundary-layer field site Falkenberg of the DWD. The new algorithm is then run on a multi-year dataset to obtain statistics of gusts at the WiValdi research park. It enables analyses not only at hub height, but also up to 500 m aloft with a reasonable availability above 60%. A case study of a convective cold pool shows the potential of the method to detect and analyze the vertical and horizontal structure of gusts with the proposed method. It highlights the importance of measurements throughout the boundary layer to understand the related physical processes to wind ramps and gusts.

Machine Learning-Based Forecasting of Metocean Data for Offshore Engineering Applications

The advancement towards utilizing renewable energy sources is crucial for mitigating environmental issues such as air pollution and climate change. Offshore wind turbines, particularly floating offshore wind turbines (FOWTs), are developed to harness the stronger, steadier winds available over deep waters. Accurate metocean data forecasts, encompassing wind speed and wave height, are crucial for offshore wind farms’ optimal placement, operation, and maintenance and contribute significantly to FOWT’s efficiency, safety, and lifespan. This study examines the application of three machine learning (ML) models, including Facebook Prophet, Seasonal Autoregressive Integrated Moving Average with Exogenous Factors (SARIMAX), and long short-term memory (LSTM), to forecast wind speeds and significant wave heights, using data from a buoy situated in the Pacific Ocean. The models are evaluated based on their ability to predict 1-, 3-, and 30-day future wind speed and wave height values, with performances assessed through Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) metrics. Among the models, LSTM displayed superior performance, effectively capturing the complex temporal dependencies in the data. Incorporating exogenous variables, such as atmospheric conditions and gust speed, further refined the predictions.The study’s findings highlight the potential of machine learning (ML) models to enhance the integration and reliability of renewable energy sources through accurate metocean forecasting.

Modeling Climate Change Effects on Genetic Diversity of an Endangered Horse Breed Using Canonical Correlations.

The historical increase in the occurrence of extreme weather events in Spain during the last thirty years makes it a perfect location for the evaluation of climate change. Modeling the effects of climate change on domestic animals' genetic diversity may help to anticipate challenging situations. However, animal populations' short life cycle and patent lack of historical information during extended periods of time drastically compromise the evaluation of climate change effects. Locally adapted breeds' gene pool is the base for their improved resilience and plasticity in response to climate change's extreme climatic conditions. The preservation of these domestic resources offers selection alternatives to breeders who seek such improved adaptability. The Spanish endangered autochthonous Hispano-Arabian horse breed is perfectly adapted to the conditions of the territory where it was created, developed, and widespread worldwide. The possibility to trace genetic diversity in the Hispano-Arabian breed back around seven decades and its global ubiquity make this breed an idoneous reference subject to act as a model for other international populations. Climate change's shaping effects on the genetic diversity of the Hispano-Arabian horse breed's historical population were monitored from 1950 to 2019 and evaluated. Wind speed, gust speed, or barometric pressure have greater repercussions than extreme temperatures on genetic diversity. Extreme climate conditions, rather than average modifications of climate, may push breeders/owners to implement effective strategies in the short to medium term, but the effect will be plausible in the long term due to breed sustainability and enhanced capacity of response to extreme climate events. When extreme climatic conditions occur, breeders opt for mating highly diverse unrelated individuals, avoiding the production of a large number of offspring. People in charge of domestic population conservation act as catalyzers of the regulatory changes occurring during breeds' climate change adaptive process and may identify genes conferring their animals with greater adaptability but still maintaining enhanced performance. This model assists in determining how owners of endangered domestic populations should plan their breeding strategies, seeking the obtention of animals more resilient and adapted to climate-extreme conditions. This efficient alternative is focused on the obtention of increased profitability from this population and in turn ensuring their sustainability.

Wind gust forecasting by post-processing the WRF model outputs using ANN

Strong and highly variable winds and gusts are major hazards to infrastructure, properties, and life. Consequently, accurate prediction and timely detection of wind gust intensity have always been a focus of interest for earth scientists and weather forecasters.In this study, The WRF (Weather Research and Forecasting) post-process diagnostic of wind gusts (WPD method) was utilized to predict non-convective wind gust speeds using the direct outputs of the WRF model. To improve the prediction accuracy of this method, the results were post-processed using an artificial neural network (ANN). Multiple different ANN algorithms were examined to achieve the most accurate predictions possible. The results were evaluated using observational data extracted from 32 synoptic stations across Iran during the time period from 2014 to 2018.The results indicate that employing a multilayer perceptron ANN with a hybrid structure, consisting of one input layer comprising five parameters (10 m wind speed, sea level pressure, temperature, relative humidity, and predicted wind gust speed obtained from the WPD method), one hidden layer with a sigmoid activation function and 12 neurons, one output layer with a linear activation function and using the BR (Bayesian Regularization) training algorithm, significantly improve the accuracy of the WPD wind gust speed prediction method. The RMSE for wind gust speed prediction has decreased from 3.68 m/s (WPD method) to 1.88 m/s for the validation dataset. Additionally, there were considerable improvements of 50 %, 74 %, and 17 % in the MAE, MSE, and R2, respectively.

Return levels of extreme European windstorms, their dependency on the North Atlantic Oscillation, and potential future risks

Abstract. Windstorms are the most damaging natural hazard across western Europe. Risk modellers are limited by the observational data record to only ∼ 60 years of comprehensive reanalysis data that are dominated by considerable inter-annual variability. This makes estimating return periods of rare events difficult and sensitive to the choice of the historical period used. This study proposes a novel statistical method for estimating wind gusts across Europe based on observed windstorm footprints. A good description of extreme wind speeds is obtained by assuming that gust speed peaks over threshold are distributed exponentially, i.e. a generalised Pareto distribution having a zero shape parameter. The threshold and tail scale parameter are estimated at each location and used to calculate estimates of the 10- and 200-year return levels. The North Atlantic Oscillation (NAO) is particularly important for modulating lower return levels and modulating the threshold, with a less detectable influence on rarer extremes and the tail scale parameter. The length of historical data required to have the lowest error in estimating return levels is quantified using both observed and simulated time series of the historical NAO. For reducing errors in estimating 200-year return levels of an independent 10-year period, a data catalogue of at least 20 years is required. For lower return levels the NAO has a stronger influence on estimated return levels, and so there is more variability in estimates. Using theoretical estimates of future NAO states, return levels are largely outside the historical uncertainty, indicating significant increases in risk potential from windstorms in the next 100 years. Our method presents a framework for assessing high-return-period events across a range of hazards without the additional complexities of a full catastrophe model.

Spatio‐temporal distribution of atmospheric chloride deposition over a small island

AbstractEstimating chloride deposition rates is important for several applications. The conservative nature of chloride makes it a widely used natural tracer for hydrological investigations, such as groundwater recharge estimations and catchment salt balances. Additionally, chloride deposition is used to identify the sources and pathways of air pollutants as well as predict the likelihood of corrosion in infrastructures. The variability of atmospheric chloride (Cl−) deposition is a crucial consideration in hydrological investigations, particularly in coastal areas where it can fluctuate greatly. On small islands, spatial variations of chloride deposition have been found to be particularly strong, which may lead to large estimation errors when assuming point measurements are representative of larger areas. This paper aims to improve knowledge of the factors affecting chloride deposition's spatial and temporal variability in small islands using Norfolk Island (South Pacific) as a case study. A monitoring network consisting of 15 open‐site rainwater collectors was installed, from which 275 rainwater samples were collected for the period September 2020–July 2022. Total deposition rates for this period varied from 22.7 to 36.7 mg m−2 d−1 for sites located more than 400 meters from the coast. A higher variation is observed closer to the coast, with values varying from 50.9 to 1022.7 mg m−2 d−1 for sites located between 50 and 200 m from the coast. Statistical analysis shows that distance from the nearest point on the coast and elevation above sea level are significant factors contributing to spatial variability of chloride deposition, whereas period‐total rainfall depth and period‐average maximum daily wind gust speed significantly affect the temporal variability. This study contributes to knowledge on chloride deposition variability as well as the understanding of factors contributing to this variability in small islands. Additionally, a double exponential model for spatial distribution of chloride deposition in small islands is proposed.

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.

Climatology and pre-convection environmental conditions of dry and wet thunderstorm high winds over eastern China

Thunderstorm high wind (THW) is defined as a kind of convective weather phenomenon with a maximum wind gust speed not less than 17.2 m·s−1 over China. It is a sudden, damaging, but common convective weather phenomenon during the warm seasons of China. By adopting a kernel density estimation (KDE) for the THWs during warm seasons (March–September) of 2010–2019 over eastern China and the European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) dataset, the dry and wet THWs usually with different mechanisms are objectively obtained, and climatology and pre-convection environmental conditions investigated. KDE shows the total precipitable water (TPW) of 38 mm can be used as the threshold for distinguishing dry THWs from wet ones. Dry THWs mainly concentrate in North China and Yunnan province, while wet ones mainly concentrate in South China. West of Hebei province has high frequencies for both dry and wet THWs. A comparison of pre-convection environmental conditions shows dry THWs mainly occur under environmental conditions with lower saturation at both the middle-lower and upper layers, while wet ones usually have relatively lower saturation at the middle-lower layers. The instability of wet THWs can be well characterized by the most unstable lifted index (MULI) and most unstable convective available potential energy (MUCAPE), while dry ones can be well depicted by the lower 500 hPa temperature caused higher temperature difference (DT85) or temperature lapse rate (TLR85) between 850 and 500 hPa. The 0–6-km vertical wind shear (SHR6) can distinguish dry THWs from wet ones better than SHR1. The typical difference can be comprehensively revealed by the representative soundings. The results provide objective references for understanding and forecasting THWs under dry and wet environmental conditions.

Convective-gust nowcasting based on radar reflectivity and a deep learning algorithm

Abstract. Convective wind gusts (CGs) are usually related to thunderstorms, and they may cause great structural damage and serious hazards, such as train derailment, service interruption, and building collapse. Due to the small-scale and nonstationary nature of CGs, reliable CG nowcasting with high spatial and temporal resolutions has remained unattainable. In this study, a novel nowcasting model based on deep learning – namely, CGsNet – is developed for 0–2 h lead times of quantitative CG nowcasting, achieving minute–kilometer-level forecasts. CGsNet is a physics-constrained model established by training on large corpora of average surface wind speed (ASWS) and radar observations; it can produce realistic and spatiotemporally consistent ASWS predictions in CG events. By combining the gust factor (1.77, the ratio of the observed peak wind gust speed (PWGS) to the ASWS) with the ASWS predictions, the PWGS forecasts are estimated with a spatial resolution of 0.01∘ × 0.01∘ and a 6 min temporal resolution. CGsNet is shown to be effective, and it has an essential advantage in learning the spatiotemporal features of CGs. In addition, quantitative evaluation experiments indicate that CGsNet exhibits higher generalization performance for CGs than the traditional nowcasting method based on numerical weather prediction models. CG-nowcasting technology can be applied to provide real-time quantitative CG forecasts.

Impact of ASOS Real-Time Quality Control on Convective Gust Extremes in the USA

Most damage to buildings across the contiguous United States, in terms of number and total cost, is caused by gusts in convective events associated with thunderstorms. Their assessment relies on the integrity of meteorological observations. This study examines the impact on risk due to valid gust observations culled erroneously by the real-time quality control algorithm of the US Automated Surface Observation System (ASOS) after 2013. ASOS data before 2014 are used to simulate the effect of this algorithm at 450 well-exposed stations distributed across the contiguous USA. The peak gust is culled in around 10% of these events causing significant underestimates of extreme gusts. The full ASOS record, 2000–2021, is used to estimate and map the 50-year mean recurrence interval (MRI) gust speeds, the conventional metric for structural design. It is concluded that recovery of erroneously culled observations is not possible, so the only practical option to eliminate underestimation is to ensure that the 50-year MRI gust speed at any given station is not less than the mean for nearby surrounding stations. This also affects stations where values are legitimately lower than their neighbors, which represents the price that must be paid to eliminate unacceptable risk.

Strong surface winds in Storm Eunice. Part 1: storm overview and indications of sting jet activity from observations and model data

Strong surface winds in Storm <i>Eunice</i>. Part 1: storm overview and indications of sting jet activity from observations and model data

Winter storm risk assessment in forests with high resolution gust speed data

Winter storms pose a major threat to forest management in Central Europe. They affect forests at a large spatial scale and produce large losses in standing and merchantable timber within few hours. The assessment of winter storm vulnerability by statistical modelling serves as an important tool to tackle uncertainities about the damage risk and to inform management decision processes. This study made use of an extensive forest inventory data set from South-West Germany before and after winter storm Lothar in 1999, one of the most severe storm events in Germany over the last decades. Hierarchical logistic models were fitted to relate storm damage probability on individual tree level to features of dendrometry, site, orography, and storm-specific high resolution data of maximum gust speed. We developed two different approaches to implement gust speed as a predictor and compared them to a baseline model with a structured spatial effect function with no gust speed information. Regional and local variability which could not be described by the predictors was modelled by multi-level group effects. Generalisation performance was tested with a spatially and temporally independent data set on storm separation between explicit spatial gust speeds and unknown variability achieved with the parametric multi-level approach led to a higher degree of transparency and utilisability.

Offshore wind farm layouts designer software's

Offshore wind energy can be considered one of the renewable energy sources with high force potential installed in marine areas. Consequently, the best wind farm layouts identified for constructing combined offshore renewable energy farms are crucial. To this aim, offshore wind potential analysis is essential to highlight the best offshore wind layouts for farm installation and development. Furthermore, the offshore wind farm layouts must be designed and developed based on the offshore wind accurate assessment to identify previously untapped marine regions. In this case, the wind speed distribution and correlation, wind direction, gust speed and gust direction for three sites have been analyzed, and then two offshore wind farm layout scenarios have been designed and analyzed based on two offshore wind turbine types in the Northwest Persian Gulf. In this case, offshore wind farm layouts software and tools have been reviewed as ubiquitous software tools. The results show Beacon M28 and Sea Island buoys location that the highest correlation between wind and gust speeds is between 87% and 98% in Beacon M28 and Sea Island Buoy, respectively. Considerably, the correlation between wind direction and wind speed is negligible. The Maximum likelihood algorithm, the WAsP algorithm, and the Least Squares algorithm have been used to analyze the wind energy potential in offshore buoy locations of the Northwest Persian Gulf. In addition, the wind energy generation potential has been evaluated in different case studies. For example, the Umm Al-Maradim buoy area has excellent potential for offshore wind energy generation based on the Maximum likelihood algorithm, WAsP algorithm, and Least Squares algorithm.

Implementation and Evaluation of a Low Speed and Self-Regulating Small Wind Turbine for Urban Areas in South Africa

A low-cost small 500W wind generator was used as a basis for the prototype development. The research was primarily focused on the determination of the type of aerofoil for improved rotor blades and pitch angle, and for adapting the number of blades in order to optimize the power output from the prototype, for low wind-speed inland conditions in Soweto. NACA-4412 type aerofoil was chosen as a departure point for the blade design, and a variation of the maximum pitch angle of 6°, 10°, and 12° at an optimum angle of attack of 5°, 7°, and 9° were implemented respectively for Designs 1, 2 and 3. With the Soweto area having an average wind speed of 2.3m/s (8.28km/h), 3-, 5-, and 7-blade sets were subsequently developed, implemented, and tested. Prototype 1 produced a maximum output power of 8.2W at 4.2km/h wind speed. Prototype 2 yielded a maximum output power of 12.5W at 4.2km/h, and Prototype 3, generated a very useful power output of 39.5W during testing. The maximum power output was achieved at an average wind speed of 1.17m/s (4.2km/h). Moreover, the developed prototype designs were also tested for self-regulation in case of high-speed gust conditions. Prototype 3, with a 12° maximum pitch angle during operation in high gust conditions, had its blades control high speed. A drawback pressure occurred on the back side of the blades and tangent drag was developed normally to the blade rotation direction, consequently limiting the maximum speed of the rotor and acting as a self-regulation mechanism with regard to maximum achievable speed. The other two designs suffered from over-speeding tendencies in high gust speed conditions, also causing noise and turbulence.

An Overview of Extreme Storm Trends in Java Island using Storm Severity Index (SSI)

Extreme winds can lead to extreme storms, which are among the most frequent forms of extreme weather in Indonesia. With climate change predicted to increase the adverse impact of extreme weather, it is crucial to keep track of and gauge the severity of storm events. Storm Severity Index (SSI) can be used to measure the severity of storms, which is defined by their intensity, duration, and extent. SSI has been used in continental areas. However, the application of SSI in the maritime continent is still limited. This study explores the application of SSI to measure extreme storms in Indonesia based on wind gust speed. This study uses wind gust data in 1959-2021 from ERA5 reanalysis data and calculates the SSI based on the 98th percentile and the 50-year return value of the wind data. This study also explores the meteorological events that coincide with high SSI values. This study found the SSI able to capture high wind gust events in Java Island. This study finds high SSI value coincides with tropical cyclone events in the Indian Ocean. Topography affects the area on Java Island that has a high SSI value. Further study should evaluate the impact of the coastal and non-coastal areas to reduce the significant effect of sea-land breeze or mountain-valley breeze. In addition, future research also needs to address the significance level of the duration of events in a severe storm to enhance the information on severity.