A Nonstationary Wind Speed Frequency Model over South Korea: In the Context of Bayesian Mixture Distribution Model

Abstract

It has been well recognized that extreme wind speed processes often feature nonstationary behavior, which may not be effectively modeled within a stationary frequency modeling framework. Furthermore, univariate Gumbel distribution has been commonly used for wind speed frequency analysis in Korea. However, the distributional changes in extreme wind speed have been globally observed, including Korea. More specifically, the univariate Gumbel distribution based wind speed frequency analysis often failed to describe multimodal behaviors which are mainly influenced by distinct climate conditions. In this perspective, this study explores a mixture distribution based nonstationary frequency (MDNF) model in a changing climate within a Bayesian framework. It was found that the MDNF model can effectively account for the time-varying moments (i.e., mean and variance) in a two-component mixture distribution. The MDNF model showed more robust results for describing the upper tail of the distribution, which plays a crucial role in estimating the design wind speed.