Identifying the most influential statistics on low-occurring wind speed at pedestrian height for a semi-idealized urban building environment

• Comparison of LES and wind tunnel data for peak wind events at pedestrian height in semi-idealized European city layouts. • Determined the accuracy of Weibull and Gram-Charlier series models for predicting low-occurring wind speed, also highlighting limitations in complex urban wind patterns. • High correlation of peak factor with skewness and kurtosis. • Identification of discrepancies in skewness and kurtosis between LES and experiments in courtyard areas. • Identification of bimodal distribution for wind components linked to urban layout. Evaluating the pedestrian-level wind environment (PLWE) during urban design and planning is important due to its impact on air quality, human comfort, and safety. This study uses datasets from large eddy simulation (LES) and wind tunnel experiment (WTE) to predict low-occurring wind speed (LOWS) events in a complex European urban layout known as the “Michelstadt” case. The study first compares LES results of high-order statistics with those of the WTE and then investigates the relationship of wind statistics with gust factor (GF) and peak factor (PF). Positive skewness values are found at all measured locations for wind speed, while negative skewness values are reported for the wind velocity components on the street canyons perpendicular to the incoming wind direction at pedestrian height. The skewness of both speed and velocity components showed the highest correlation with PF for all exceedance probability values compared to the coefficient of variation, standard deviation, and kurtosis. The integral timescale showed a marginal correlation with the PF and GF. The relationships of statistics with GF and PF are then compared with the theoretical curves based on the Weibull and Gram-Charlier series (GCS) for estimating low-occurring events of wind speed and velocity components. The findings indicate that the Weibull distribution accurately represents the wind speed and related PF and GF but shows limitations for velocity components where GCS provides better results. The study emphasizes the need for improved statistical models to predict wind velocity components and calls for more experimental studies to compare higher-order statistics with LES.