Non-stationary statistical modeling of extreme wind speed series with exposure correction

Mingfeng Huang, Qiang Li, Haiwei Xu, Wenjuan Lou, Ning Lin

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)129-146
Number of pages18
JournalWind and Structures, An International Journal
Volume26
Issue number3
DOIs
StatePublished - Mar 2018

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Building and Construction
  • Modeling and Simulation

Keywords

  • Exposure adjustment
  • Extreme wind speed
  • Generalized maximum likelihood approach
  • Non-stationary
  • Statistical modeling

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