Seasonal Extreme Wind Speed and Gust Wind Speed: A Case Study of the China Seas

Zheng, Chunmiao; Liang, Fuyuan; Yao, Jiankang; Dai, Juchuan; Gao, Zhan-sheng; Hou, Tingting; Xiao, Ziniu

doi:10.2112/si99-059.1

Cited by 9 publications

(9 citation statements)

References 9 publications

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“…Note that Eq. 5 is similar to the well-known mean upcrossing rate equation for the probability of exceedance (Gaidai et al, 2020;Gao et al, 2020;Naess and Gaidai, 2009). There is evident convergence with respect to the conditioning parameterk:…”

Section: Methodssupporting

confidence: 65%

“…} for j ≥ k will be independent of j but only dependent on conditioning level k. Thus non-exceedance probability can be approximated as in the Naess-Gaidai method, see (Naess and Gaidai, 2009;Gaidai et al, 2018;Gaidai et al, 2020;Gao et al, 2020) where:…”

Section: Methodsmentioning

confidence: 99%

“…It is generally relatively challenging to calculate realistic environmental system reliability using conventional theoretical reliability methods (Madsen et al, 1986;Thoft-Christensen and Murotsu, 1986;Ditlevsen and Madsen, 1996;Melchers, 1999;Choi et at. 2007;Eryilmaz and Kan, 2020;Kan et al, 2020;Zheng et al, 2020). This is usually due to a large number of degrees of system freedom and random variables governing the responses in a real environmental system.…”

Section: Introductionmentioning

confidence: 99%

“…However, the experimental or computational cost may be unaffordable for many complex dynamic systems in which the long-term statistics of their responses can only be accurately captured using huge data sets. Motivated by the latter argument, the authors have introduced a novel reliability method for environmental systems that uses the average conditional exceedance rate (ACER) method (Naess and Gaidai, 2009;Gaidai et al, 2020;Gao et al, 2020;Sun et al, 2022) for statistical extrapolation to reduce either measurement or computational costs. Extreme value statistics has been a topical matter for some time now, and there are some excellent reviews on the topic, see (Hansen, 2020;Majumdar et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…The weight function ω can be defined as ω(λ) {ln CI + (λ) − ln CI − (λ)} −2 with (CI − (λ), CI + (λ)) being a confidence interval (CI), empirically estimated from the simulated or measured dataset, see (Gaidai et al, 2020;Gao et al, 2020;Naess and Gaidai, 2009). When the parameter c lim k → ∞ c k is equal or close to 1, the distribution approaches to the Gumbel distribution.…”

Section: Model Introductionmentioning

confidence: 99%

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A novel method for prediction of extreme wind speeds across parts of Southern Norway

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Xing

2022

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The paper describes a novel structural reliability method, particularly suitable for multi-dimensional environmental systems, either measured or numerically simulated over a sufficient period, resulting in sufficiently long ergodic time series. This study illustrates the efficiency of the proposed methodology by applying it to predict extreme wind speeds of a group of selected measured sites in Southern Norway in the region near the Landvik wind station. It is well known that wind speeds at different locations are highly non-linear, multi-dimensional and cross-correlated dynamic environmental responses, which can be challenging to analyse accurately. Unlike other environmental reliability methods, the new method does not require restarting the simulation each time the system fails, e.g., in the case of numerical simulation. In the case of measured environmental system response, an accurate prediction of system failure probability is also possible, as illustrated in this study. Moreover, in contrast to classical reliability methods, the proposed method can handle systems with high dimensionality and cross-correlation between the different dimensions.

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Section: Methodssupporting

confidence: 65%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Model Introductionmentioning

confidence: 99%

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A novel method for prediction of extreme wind speeds across parts of Southern Norway

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2022

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Study of the Wind Conditions in the South China Sea and Its Adjacent Sea Area

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Extreme Wind Variability and Wind Map Development in Western Java, Indonesia

Abdillah

Sarli

Firmansyah

et al. 2022

Int J Disaster Risk Sci

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Wind-related disasters are one of the most frequent disasters in Indonesia. It can cause severe damages of residential construction, especially in the world’s most populated island of Java. Understanding the characteristics of extreme winds is crucial for mitigating the disasters and for defining structural design standards. This study investigated the spatiotemporal variations of extreme winds and pioneered a design wind map in Indonesia by focusing on western Java. Based on gust data observed in recent years from 24 stations, the extreme winds exhibit a clear annual cycle where northwestern and southeastern sides of western Java show out-of-phase relationship due to reversal monsoons. Meanwhile, extreme wind occurrences are mostly affected by small-scale weather systems, regardless of seasons and locations. To build the wind map, we used bias-corrected gust from ERA5 and applied the Gumbel method to predict extreme winds with different return periods. The wind map highlights some drawbacks of the current national design standards, which use single wind speed values regardless of location and return period. Beside a fundamental improvement for wind design, this study will benefit disaster risk mapping and other applications that require extreme wind speed distribution.

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Seasonal Extreme Wind Speed and Gust Wind Speed: A Case Study of the China Seas

Cited by 9 publications

References 9 publications

A novel method for prediction of extreme wind speeds across parts of Southern Norway

A novel method for prediction of extreme wind speeds across parts of Southern Norway

Study of the Wind Conditions in the South China Sea and Its Adjacent Sea Area

Extreme Wind Variability and Wind Map Development in Western Java, Indonesia

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