Extreme Wind Speed Map for Mainland China Considering the Directional Effect

Abstract

This study proposes a framework for mapping the extreme wind speed for mainland China considering the directional effect. To this end, long-term observations of the daily maximum surface wind speed and associated wind direction from 188 meteorological stations across mainland China are collected. First, the marginal probability distribution function (PDF) of the daily maximum wind speed and the wind direction is modeled by fitting the observed data to several candidate probability distributions and selecting the best-fit model using the Akaike Information Criterion (AIC). The results indicate that at most meteorological stations, the Gumbel distribution is the best-fit model for the marginal PDF of the daily maximum wind speed, and the third-order Von Mises distribution is the best-fit model for the wind direction. Second, the joint probability distribution function (JPDF) for the daily maximum wind speed and wind direction is modeled by considering several candidate correlation models, including the traditional Angular-Linear (AL) model and four Archimedean copula function models. The AIC analysis of the estimated JPDFs shows that the Frank copula function performs the best among the candidate models. Third, the wind speeds associated with a 50-year return period in 16 wind directions are estimated based on the best-fit marginal PDF and JPDF of daily maximum wind speed and wind direction, and the extreme wind speed map is further derived by using the Kriging method. Comparing the extreme wind speeds considering the directional effect to those estimated by the data in all directions indicates that neglecting the directional effect generally results in inaccurate extreme wind speed.