• Wind and Structures
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• v.26 no.3
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• pp.129-146
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• 2018

Extreme wind speed analysis has been carried out conventionally by assuming the extreme series data is stationary. However, time-varying trends of the extreme wind speed series could be detected at many surface meteorological stations in China. Two main reasons, exposure change and climate change, were provided to explain the temporal trends of daily maximum wind speed and annual maximum wind speed series data, recorded at Hangzhou (China) meteorological station. After making a correction on wind speed series for time varying exposure, it is necessary to perform non-stationary statistical modeling on the corrected extreme wind speed data series in addition to the classical extreme value analysis. The generalized extreme value (GEV) distribution with time-dependent location and scale parameters was selected as a non-stationary model to describe the corrected extreme wind speed series. The obtained non-stationary extreme value models were then used to estimate the non-stationary extreme wind speed quantiles with various mean recurrence intervals (MRIs) considering changing climate, and compared to the corresponding stationary ones with various MRIs for the Hangzhou area in China. The results indicate that the non-stationary property or dependence of extreme wind speed data should be carefully evaluated and reflected in the determination of design wind speeds.

• Journal of Industrial Technology
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• v.35
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• pp.95-102
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• 2015

Two extreme wind speed prediction models, the EWM(Extreme wind speed model) in IEC61400-1 and the Gumbel method were compared in this study. The two models were used to predict extreme wind speeds of six different sites in Korea and the results were compared with long term wind data. The NCAR reanalysis data were used for inputs to two models. Various periods of input wind data were tried from 1 year to 50 years and the results were compared with the 50 year maximum wind speed of NCAR wind data. It was found that the EWM model underpredicted the extreme wind speed more than 5 % for two sites. Predictions from Gumbel method overpredicted the extreme wind speed or underpredicted it less than 5 % for all cases when the period of the input data is longer than 10 years. The period of the input wind data less than 3 years resulted in large prediction errors for Gumbel method. Predictions from the EWM model were not, however, much affected by the period of the input wind data.

• Atmosphere
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• v.18 no.4
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• pp.397-416
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• 2008

This study investigates a long-term variation of the annual extreme value for the instantaneous wind speed and the daily precipitation during 56 years (1951-2006) in Korea. Results show that there is a uptrend for both wind and precipitation extreme records, although regional trends are different from overall pattern in some places, particularly for wind speed. The estimated linear trends are 230 mm/56 yr in the daily precipitation and $15ms^{-1}$/56 yr in the maximum instantaneous wind speed. For precipitation, other indexes such as total annual precipitation, the number of extreme precipitation event, and precipitation intensity have dramatically increased as well, while there has been a clear downtrend for the number of strong wind events (> $14ms^{-1}$). It is found that the minimum surface pressure recorded during typhoon attacks in Korea tends to be decreasing, about 10 hPa/56 yr. This partly explains why the extreme values in the precipitation are increasing in Korea.

• Wind and Structures
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• v.19 no.3
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• pp.271-294
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• 2014

The impact of artefacts in archived wind observations on the design wind speed obtained by extreme value analysis is demonstrated using case studies. A signpost protocol for detecting candidate artefacts is described and its performance assessed by comparing results against previously validated data. The protocol targets artefacts by exploiting the serial correlation between observations. Additional "sieve" algorithms are proposed to identify types of correctable artefact from their "signature" in the data. In extreme value analysis, artefacts displace valid observations only when they are larger, hence always increase the design wind speed. Care must be taken not identify large valid values as artefacts, since their removal will tend to underestimate the design wind speed.

• Wind and Structures
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• v.33 no.6
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• pp.423-435
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• 2021

Although existing algorithms can predict wind speed using historical observation data, for engineering feasibility, most use moment methods and probability density functions to estimate fitted parameters. However, extreme wind speed prediction accuracy for long-term return periods is not always dependent on how the optimized frequency distribution curves are obtained; long-term return periods emphasize general distribution effects rather than marginal distributions, which are closely related to potential extreme values. Moreover, there are different wind speed parent sample types; how to theoretically select the proper extreme value distribution is uncertain. The influence of different sampling time intervals has not been evaluated in the fitting process. To overcome these shortcomings, updated steps are introduced, involving parameter sensitivity analysis for different sampling time intervals. The extreme value prediction accuracy of unknown parent samples is also discussed. Probability analysis of mean wind is combined with estimation of the probability plot correlation coefficient and the maximum likelihood method; an iterative estimation algorithm is proposed. With the updated steps and comparison using a Monte Carlo simulation, a fitting policy suitable for different parent distributions is proposed; its feasibility is demonstrated in extreme wind speed evaluations at Longhua and Chuansha meteorological stations in Shanghai, China.

• Wind and Structures
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• v.34 no.6
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• pp.469-482
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• 2022

The generalized extreme value distribution (GEVD) is frequently used to fit the block maximum of environmental parameters such as the annual maximum wind speed. There are several methods for estimating the parameters of the GEV distribution, including the least-squares method (LSM). However, the application of the LSM with the expected order statistics has not been reported. This study fills this gap by proposing a fitting method based on the expected order statistics. The study also proposes a plotting position to approximate the expected order statistics; the proposed plotting position depends on the distribution shape parameter. The use of this approximation for distribution fitting is carried out. Simulation analysis results indicate that the developed fitting procedure based on the expected order statistics or its approximation for GEVD is effective for estimating the distribution parameters and quantiles. The values of the probability plotting correlation coefficient that may be used to test the distributional hypothesis are calculated and presented. The developed fitting method is applied to extreme thunderstorm and non-thunderstorm winds for several major cities in Canada. Also, the implication of using the GEVD and Gumbel distribution to model the extreme wind speed on the structural reliability is presented and elaborated.

• Wind and Structures
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• v.19 no.2
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• pp.169-184
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• 2014

This paper presents a method of estimation of extreme wind. Assuming the extreme wind follows the Gumbel distribution, it is modeled through fitting an exponential function to the numbers of storms over different thresholds. The comparison between the estimated results with the Improved Method of Independent Storms (IMIS) shows that the proposed method gives reliable estimation of extreme wind. The proposed method also shows its advantage on the insensitiveness of estimated results to the precision of the data. The volume of extreme storms used in the estimation leads to more than 5% differences in the estimated wind speed with 50-year return period. The annual rate of independent storms is not a significant factor to the estimation.

• Nuclear Engineering and Technology
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• v.47 no.1
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• pp.59-65
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• 2015

Background: After the Fukushima accident, the importance of hazard analysis for extreme external events was raised. Methods: To analyze typhoon-induced hazards, which are one of the significant disasters of East Asian countries, a statistical analysis using the extreme value theory, which is a method for estimating the annual exceedance frequency of a rare event, was conducted for an estimation of the occurrence intervals or hazard levels. For the four meteorological variables, maximum wind speed, instantaneous wind speed, hourly precipitation, and daily precipitation, the parameters of the predictive extreme value theory models were estimated. Results: The 100-year return levels for each variable were predicted using the developed models and compared with previously reported values. It was also found that there exist significant long-term climate changes of wind speed and precipitation. Conclusion: A fragility analysis should be conducted to ensure the safety levels of a nuclear power plant for high levels of wind speed and precipitation, which exceed the results of a previous analysis.

• Wind and Structures
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• v.30 no.2
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• pp.141-153
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• 2020

Probabilistic information regarding directional extreme wind speeds is important for the precise estimation of the design wind loads on structures. A joint probability distribution model of directional extreme typhoon wind speeds is established using Monte Carlo simulation and empirical copula function to fully consider the correlations of extreme typhoon wind speeds among the different directions. With this model, a procedure for estimating directional extreme wind speeds for given return periods, which ensures that the overall risk is distributed uniformly by direction, is established. Taking 5 typhoon-prone cities in China as examples, the directional extreme typhoon wind speeds for given return periods estimated by the present method are compared with those estimated by the method proposed by Cook and Miller (1999). Two types of directional factors are obtained based on Cook and Miller (1999) and the UK standard's drafting committee (Standard B, 1997), and the directional risks for the given overall risks are discussed. The influences of the extreme wind speed correlations in the different directions and the simulated typhoon wind speed sample sizes on the estimated extreme wind speeds for a given return period are also discussed.

• Wind and Structures
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• v.37 no.2
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• pp.153-161
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• 2023

Wind speed data from Nepal and adjoining countries have been analyzed to estimate an extreme wind speed climatology for the region. Previously wind speed information for Nepal was adopted from the Indian National Standard and applied to two orographically different regions: above and below 3000 m elevation respectively. Comparisons of the results of this analysis are made with relevant codes and standards. The study confirms that the assigned basic wind speed of 47 m/s for the plains and hills of Nepal (below 3000 m) is appropriate, however, data to substantiate a basic wind speed of 55 m/s above 3000 m is unavailable. Using a composite analysis of 15 geographically similar stations, the study also generated 435 years of annual maxima wind data and fitted them to Type I and Type III extreme value distributions. The results suggest that Type III distribution may better represent the data. The findings are also consistent with predictions made by Holmes and Weller (2002) and to a certain extent those of Sarkar et al. (2014), but lower than the analysis undertaken by Lakshmanan et al. (2009) for northern India. The study also highlights that the use of a load factor of 1.5 on wind load implies lower strength design MRI's of around 260 years compared to the 700 years of ASCE 7-22.