Correlations among high-order statistics and low-occurrence wind speeds within a simplified urban canopy based on particle image velocimetry datasets

Predicting infrequent and extreme wind speeds in a built environment is essential for ensuring comfortable and safe pedestrian spaces. Recent studies have employed statistical methods that assume a distribution function for wind speeds at pedestrian levels. Fundamental studies on the relationship between canopy flow and statistics are required to further develop statistical models. Therefore, this study aimed to understand the characteristics of strong and weak wind events within a simplified urban canopy and to scrutinize the relationship between high-order moments and extreme wind events. Particle image velocimetry (PIV) was employed to capture the velocities within a canopy consisting of cubes arranged in a staggered layout with a packing density of 25 %. The probability density functions (PDFs) of the velocity components classified by the mean flow patterns revealed that the PDF shapes were altered by the reverse and spanwise flows. In addition, strong correlations were verified between the gust or peak factor (PF) and high-order moments such as skewness, kurtosis, fifth-order, and sixth-order moments. Accordingly, the PF of the velocity components and wind speed were compared with the predictions by statistical methods based on the Weibull and Gram–Charlier series (GCS). These observations validate the previous statistical methods based on Weibull or GCS distributions. Although the physical interpretation of these statistics is ambiguous, the present analyses indicate that PF can be predicted by high-order moments, especially in particular, by skewness and kurtosis.