Estimation Of Extreme Wind Speeds Research Articles

Aggregation of data from multiple recording stations for extreme wind analysis and prediction

Accurate, reliable and robust techniques for the probabilistic estimation of extreme wind speeds are essential for the design of structures for wind loading. Aggregating gust wind data from various stations with similar, homogeneous wind climates into a ‘superstation’ for hazard analysis has been employed since the 1980′s to reduce the effects of sampling errors. A concern that has been raised recently is that prediction biases may arise from such aggregation, when the data exhibit non-homogeneity due to inevitable short data lengths or imperfect homogeneity of the wind climates. By Monte Carlo simulation, we show that superstation aggregation is an unbiased technique for high recurrence level estimations when an appropriate fitting method is used, and the apparent biases are dependent on the method used for fitting the hazard model. To ensure homogeneity, we introduce a de–trending technique for minimizing any biases in the aggregated wind data. Four model-fitting methods for superstation analysis are compared, and shown that the introduced de-trending method is effective for eliminating the biases due to sampling errors and non-homogeneity.

Extreme Wind Speed Estimation for Wind-Resistance Design of a Transmission Line Situated in a Typhoon-Prone and Hilly Area

The southeastern coast of China is annually threatened by typhoons originating from the northwestern Pacific Ocean, posing a risk of severe damage to coastal transmission lines. This study employs the random forest-based typhoon full-track simulation method and YM wind field to assess the wind-resistance reliability of transmission lines. The obtained extreme wind speeds of a transmission line site in Wenzhou are 33.7m/s and 35.8m/s under the 50-year and 100-year return periods, respectively. The effects of micro-topography on the extreme typhoon wind speed are further analyzed, and the extreme wind speed distribution maps of Wenzhou City and Yueqing City are plotted, respectively. The results indicate a decreasing trend in typhoon wind speed within inland regions, while micro-topography displays a substantial effect on enhancing the typhoon wind speed.

A wavelet transform based stationary transformation method for estimating the extreme value of the non-stationary wind speeds

The rational estimation of extreme wind speeds is crucial. As reported, in the Chinese area, the measured wind speed variation from different meteorological observatories shows a general non-stationary trend in the past 40 years due to urbanization factors or temperature variation. Thus, the non-stationary extreme value analysis (TNGEV) of the measured wind speed is required. However, TNGEV is relatively complicated. Under such a background, in this paper, a novel simplified wavelet transform-based stationary transformation method (PNGEV) is proposed for the extreme value analysis of the non-stationary wind speeds. This paper firstly derives the proposed time-varying extreme value distribution parameter form, combining the wavelet transform and moving average. Secondly, a case study is provided to illustrate and verify this proposed method (PNGEV), using the 40-year daily maximum wind speeds recorded from 1981 to 2020 in Baoshan District Shanghai, China. The effectiveness of the proposed method (PNGEV) is verified by comparing it with TNGEV in the return period and the risk of failure. By comparison, it can be figured out that the proposed PNGEV is practicable and outperforms TNGEV in the stability of the return period calculation, as well as the time-varying extreme value distribution establishment.

Offshore renewable energy site correlated wind-wave statistics

Offshore engineering often requires estimation of extreme wind speeds and wave heights at offshore in-situ locations. This paper presents practical approach to study extreme wind speed and wave height statistics, based on available in-situ hourly wind speed and wave height maxima. The wind and wave data, studied in this paper, was obtained from numerical hind cast model, locally applied to the offshore the area that covers SEM-REV (European wind and wave energy research site) location, for the time period 2001–2010 years.To obtain accurate local wind and wave data set, accurate wave and wind measurements from in-situ monitoring tools (meteorological stations and buoys) as well as remote satellite sensing data (ENVISAT and TOPEX satellite records) were plugged into atmospheric wind and wave model.The novel bivariate correction technique based on the Average Conditional Exceedance Rate (ACER) method has been presented in brief detail. The bivariate correction method produced quite accurate extreme value predictions, efficiently utilizing available wind speeds and wave heights data set.In some practical situations it would be useful to improve accuracy of some statistical predictions, using information supplied by another synchronous highly correlated random process that has been measured for a longer time than the process of interest. In this paper the novel technique of improving correlated extreme wind speed and wave height predictions has been presented.

Hindcasts of Sea Surface Wind around the Korean Peninsula Using the WRF Model: Added Value Evaluation and Estimation of Extreme Wind Speeds

Sea surface wind plays an essential role in the simulating and predicting ocean phenomena. However, it is difficult to obtain accurate data with uniform spatiotemporal scale. A high-resolution (10 km) sea surface wind hindcast around the Korean Peninsula (KP) is presented using the weather research and forecasting model focusing on wind speed. The hindcast data for 39 years (1979–2017) are obtained by performing a three-dimensional variational analysis data assimilation, using ERA-Interim as initial and boundary conditions. To evaluate the added value of the hindcasts, the ASCAT-L2 satellite-based gridded data (DASCAT) is employed and regarded as “True” during 2008–2017. Hindcast and DASCAT data are verified using buoy observations from 1997–2017. The added value of the hindcast compared to ERA-Interim is evaluated using a modified Brier skill score method and analyzed for seasonality and wind intensity. Hindcast data primarily adds value to the coastal areas of the KP, particularly over the Yellow Sea in the summer, the East Sea in the winter, and the Korean Strait in all seasons. In case of strong winds (10–25 m·s−1), the hindcast performed better in the East Sea area. The estimation of extreme wind speeds is performed based on the added value and 50-year and 100-year return periods are estimated using a Weibull distribution. The results of this study can provide a reference dataset for climate perspective storm surge and wave simulation studies.

Data-Based Windstorm Type Identification Algorithm and Extreme Wind Speed Prediction

AbstractThe extreme wind speed estimation method, which is critical for designing wind load calculation for building structures, should consider windstorm climate types for mixed climates. However,...

Estimation of Extreme Wind Speeds in the Western North Pacific Using Reanalysis Data Synthesized with Empirical Typhoon Vortex Model

Estimation of Extreme Wind Speeds in the Western North Pacific Using Reanalysis Data Synthesized with Empirical Typhoon Vortex Model

Analysis of Extreme Wind Speed Estimates in the Northern South China Sea

AbstractAn increasing number of coastal and offshore structures have been built for coastal protection and marine development in recent years, and these marine structures need to be reasonably designed on the basis of wind speed. In this paper, extreme wind speed estimates are studied in detail by using the best-track datasets of northwestern Pacific Ocean tropical cyclones and ERA5 wind field data. The extreme wind speed fits by five distributions are compared using a blended sample of the wind fields from the ERA5 dataset and parametric wind data. The blend of wind fields improved the data accuracy and extreme value estimation reliability. In addition, the effects of the distribution model, data, threshold, and parameter estimation methods on the calculated results are discussed. The results show that the data had the greatest influences on probability prediction, followed by the distribution model and the parameter estimation method, with the threshold presenting the least influence. In this study, the reliability of the estimates was improved and the uncertainty of the results was analyzed, and the findings provide a wind speed design reference for the northern South China Sea.

Improving extreme wind speed prediction based on a short data sample, using a highly correlated long data sample

This paper focuses on the problem of improving extreme wind speed estimates for locations with only a short time series of measured values available, given the opportunity to use a highly correlated long time series of wind speed data. For this purpose, an efficient transfer of information is necessary between two highly correlated stochastic processes. To illustrate the efficiency of the proposed technique, two sets of two highly correlated time series of wind speed data are used. The first set of data consists of two synthetic wind speed data sets. The second set of data was obtained from two Norwegian wind speed measurement stations. This paper intends to stimulate further research and fill a gap in the current literature on this important topic.

Accurate estimation of T year extreme wind speeds by considering different model selection criterions and different parameter estimation methods

Accurate estimation of extreme wind speeds for different return periods is necessary to avoid extensive costs or large damages. To achieve this aim, the probability distribution of the wind speed data should be well defined and its parameters should be more precisely estimated. In this study, the commonly used probability distributions, including Gamma, Generalized Extreme Value, Logistic, Lognormal, Normal and Weibull, are fitted to annual maximum wind speed data in Turkey. Parameters of the fitted distributions are estimated using method of moments (MOM), method of maximum likelihood (MLM) and method of probability weighted moments (PWMs). Based on various model selection criterions (Akaike Information Criterion, Bayesian Information criterion, Anderson-Darling, Cramér-von-Mises, and Kolmogorov–Smirnov tests), the Generalized Extreme Value and Logistic, which provided the best fit for 40% and 30% of the series, respectively, were mostly found to be the most suitable distributions. Additionally, the Lognormal, Normal and Gamma distributions showed the best fit for 15%, 10% and 5% of the series, respectively. Moreover, the MLM and PWMs provided better parameter estimations for 57% and 30% the best fitted distributions, respectively. Furthermore, wind speed quantiles with the standard errors in various return periods were estimated using the best fitted distributions.

Estimation of extreme wind speeds and wave heights along the regional waters of India

A new Polynomial approximation method has been used for the estimation of the extreme significant wave heights and wind speeds from 33 years of hindcast data for six locations along the Indian coast. These return value estimates are compared with the values obtained from the well-known Generalised extreme value distribution and Generalised Pareto distribution methods. Further, the entire data is subdivided into three decadal time blocks to assess the time variability in model outputs of extreme values of significant wave heights and wind speeds. Although the comparison, in general, is found good, a closer examination of the results reveals that the present method based on polynomial approximation could be preferred for practical applications. The detailed analysis of the Polynomial approximation method, salient features and its fulfilment of consistency condition which overcomes the shortcomings of the other standard extreme value estimation methods are presented and discussed in this paper.

Estimation of Extreme Wind Speeds in Korean Peninsula using Typhoon Monte Carlo Simulation

국내 서해대교, 인천대교와 같은 장대교량은 대부분 빈번하게 태풍에 의해 영향을 받는 해안에 위치하였으며, 교량의 길이가 긴 만큼 풍하중에 의한 영향이 다른 하중에 비해 상대적으로 크기 때문에 내풍 안정성을 확보하기 위해 정확한 설계풍속을 산정하는 것이 매우 중요하다. 본 연구에서는 태풍의 기후학적 특성 인자로 중심기압깊이, 태풍이동속도, 태풍이동방향, 최단접근거리를 결정하였으며, 태풍의 기후학적 특성들의 확률 분포를 추정하고, 바람장 모형과 중심기압상승 모형을 적용하여 몬테카를로 시뮬레이션을 실시하였다. 분석결과, 대체적으로 제주도와 남해안 지역의 재현기간 풍속이 크게 나오며 고위도로 갈수록 작아지는 특징을 나타냈다. 이와 같은 특징이 나타난 가장 큰 원인은 고위도 분석지점 표본 태풍의 중심기압이 저위도 분석지점 표본 태풍의 중심기압보다 높기 때문으로 판단되며, 또한 우리나라에 해상에서 육지로 이동하면서 쇠퇴기를 겪어 점차 약해지기 때문인 것으로 분석되었다. 또한, 시뮬레이션 결과를 도로교 설계기준 100년 재현기간 풍속(10분 평균, 지상 10m, 지표조도 II)과 비교한 결과, 태풍시뮬레이션의 결과가 낮게 나타났으며, 이러한 점을 볼 때 도로교 설계기준의 기본 풍속이 높게 산정되어 있다고 판단되며, 기상자료 분석과 같은 추가적인 연구를 통해 기본풍속 조정에 대한 연구가 수행 되어야 할 것으로 사료된다. The long-span bridges such as Incheon Bridge and Seohae Grand Bridge are located on the coastal region effected frequently by strong wind of typhoons. In order to ensure the wind-resistant performance of the structure, estimation of the proper design wind speed is very important. In this study, stochastic estimation of design wind speed incurred by typhoons is carried out. For this purpose, we first established probability distribution of climatological parameters such as central pressure depth, distance of closest approach, translation speed and heading to build statistical model of typhoons, which are employed in Monte Carlo simulation for hypothetical typhoons. Once a typhoon is generated with statistically justified parameters, wind speeds are estimated along its path using wind field model. Thousands of typhoons are generated and their peak wind speeds are utilized to establish the extreme wind speeds for different return period. The results are compared with design basic wind speeds in Korean Highway Bridge Design Code, showing that the present results agree well with similar studies while the existing code suggests higher design wind speed.

Analysis of Washington, DC, Wind and Temperature Extremes with Examination of Climate Change for Engineering Applications

Climate projections suggest the frequency and intensity of some environmental extremes will be affected in the future due to a changing climate. These projections raise questions regarding the treatment of future extreme environmental loading for the design of buildings and other structures. One of the more uncertain questions is possible changes in the properties of extreme wind. For this paper, extreme wind events for nearly 70 years from the Washington, DC, area are analyzed from the three major airports [(1) International Airport at Dulles; (2) Washington, District of Columbia, Reagan Airport; and (3) Baltimore/Washington International Airport]. Uncertainties in estimation of extreme wind speeds without considering climate change are identified. Analysis disregarding climate change revealed that thunderstorms control design wind speeds for Washington, DC. As thunderstorms are then important, climate projections with respect to thunderstorms are also introduced. Possible strategies for long-term decision making are outlined such as understanding observed wind speed magnitudes and their relationship to environmental conditions, developing probability-based prediction techniques, and modifying design codes and standards. Extreme heat events occurring in Washington, DC, are discussed in a similar manner. Research needs in linking climate science and engineering design in the long-term are outlaid.

Estimation of Extreme Wind Speeds by Using Mixed Distributions

Structures are designed with the intention of safely withstanding ordinary and extreme wind loads over the entire intended economic lifetime. Due to the fact that extreme wind speeds are essentially random, appropriate statistical procedures needed to be developed in order design more accurately wind-sensitive structures. Five mixed extreme value distributions, with Gumbel, reverse Weibull and General Extreme Value components along with the Two Component Extreme Value distribution were used to model extreme wind speeds. The general procedure to estimate their parameters based on the maximum likelihood method is presented in the paper. A total of 45 sets, ranging from 9-year to 56-year, of largest annual wind speeds gathered from stations located in The Netherlands were fitted to mixed distributions. The best model was selected based on a goodness-of-fit test. The return levels were estimated and compared with those obtained by assuming the data arise from a single distribution. 87% of analyzed samples were better fitted with a mixed distribution. The best mixed models were the mixed reverse Weibull distribution and the mixture Gumbel-Reverse Weibull. Results suggest that it is very important to consider the mixed distributions as an additional mathematical tool when analyzing extreme wind speeds.

Improved extreme wind speed estimation for wind engineering applications

The description of the extreme wind climate is an important step in the process of determining wind loads on structures. Factors that affect the estimation of extreme wind speeds include spatiotemporal data resolution, micrometeorological characterization, and extreme value statistics. Accounting for these factors inadequately can result in estimation differences that can exceed 50%. Issues associated with these factors are explained in detail and improvements in current estimation practice are outlined. Research presented in this paper shows that recent and ongoing progress makes it possible to produce significantly improved extreme wind speed estimates.

Automated extraction and classification of thunderstorm and non-thunderstorm wind data for extreme-value analysis

Design wind loads are partly based on extreme value analyses of historical wind data, and limitations on the quantity and spatial resolution of wind data pose a significant challenge in such analyses. A promising source of recent wind speed and direction data is the automated surface observing system (ASOS), a network of about 1000 standardized US weather stations. To facilitate the use of ASOS data for structural engineering purposes, procedures and software are presented for (a) extraction of peak gust wind data and thunderstorm observations from archived ASOS reports, (b) classification of wind data as thunderstorm or non-thunderstorm to enable separate analyses, and (c) construction of data sets separated by specified minimum time intervals to ensure statistical independence. The procedures are illustrated using approximately 20-year datasets from three ASOS stations near New York City. It is shown that for these stations thunderstorm wind speeds dominate the extreme wind climate at long return periods. Also presented are estimates based on commingled data sets (i.e., sets containing, indiscriminately, both non-thunderstorm and thunderstorm wind speeds), which until now have been used almost exclusively for extreme wind speed estimates in the US. Analyses at additional stations will be needed to check whether these results are typical for locations with both thunderstorm and non-thunderstorm winds.

Reply to discussion of “A comparison of methods of extreme wind speed estimation” [ J. Wind Eng. Ind. Aerodyn. 93 (2005) 535–545

Reply to discussion of “A comparison of methods of extreme wind speed estimation” [ J. Wind Eng. Ind. Aerodyn. 93 (2005) 535–545

Discussion of “A comparison of methods of extreme wind speed estimation” by Ying An and M.D. Pandey: [J. Wind Eng. Ind. Aerodyn. 93 (2005) 535–545

Discussion of “A comparison of methods of extreme wind speed estimation” by Ying An and M.D. Pandey: [J. Wind Eng. Ind. Aerodyn. 93 (2005) 535–545

Bivariate estimation of extreme wind speeds

When wind speeds data exists but not with the length of record required to provide accurate parameter estimates, the error of the estimated return level can be very large and inefficient for design purposes. A way to reduce this error is by applying a joint estimation model, where information from nearby sites in the region may be combined with the record of inadequate length to increase information and to provide a regional at-site estimate of wind speed frequency. In order to achieve this goal, the Logistic model for bivariate extreme value distribution is proposed. The general procedure to estimate its parameters based on the maximum likelihood method is presented. Seven bivariate options were obtained by combining the marginal distributions: Gumbel, Reverse Weibull and General Extreme Value. A total of 45 sets, ranging from 9-year to 56-year, of largest annual wind speeds gathered of stations located in The Netherlands were fitted to univariate and bivariate distributions. At-site and regional at-site return levels were estimated and compared with those obtained in a previous study, which used the conditional Weibull distribution. A significant improvement occurs, measured through the use of a goodness-of-fit test, when parameters are estimated using the bivariate distribution instead of its univariate counterpart. Results suggest that it is very important to consider the bivariate joint estimation option when analyzing extreme wind speeds, especially for short samples.

The r largest order statistics model for extreme wind speed estimation

The paper presents the statistical estimation of extreme wind speed using annually r largest order statistics ( r-LOS) extracted from the time series of wind data. The method is based on a joint generalized extreme value distribution of r-LOS derived from the theory of Poisson process. The parameter estimation is based on the method of maximum likelihood. The hourly wind speed data collected at 30 stations in Ontario, Canada, are analyzed in the paper. The results of r-LOS method are compared with those obtained from the method of independent storms (MIS) and specifications of the Canadian National Building Code (CNBC-1995). The CNBC estimates are apparently conservative upper bound due to large sampling error associated with annual maxima analysis. Using the r-LOS method, the paper shows that the wind pressure data can be suitably modelled by the Gumbel distribution.