A Quantitative Method for Estimating Spatial Uncertainty of Urban Rooftop Winds

The wind field in urban areas is characterized by an inherent spatial variability, which is also termed spatial uncertainty. This may be manifested as a noticeable difference between rooftop-level measurements in adjacent locations, the degree of which changes throughout the day. In meteorological and environmental contexts, such uncertainty is often described as a probability distribution. Usually, studies deal with the uncertainty of each wind vector component separately, i.e., wind speed and direction. The uncertainty is assumed to be distributed symmetrically around the mean and represented by a single characteristic value. Such representation neglects the correlation between the two wind vector components together. This, in turn, may result in wind vector component combinations that are physically inconsistent with realistic wind regimes. This study proposes a method that quantifies the spatial uncertainty of the urban rooftop wind. It is based on a covariance matrix that quantifies the relationship between the rooftop spatial wind components alongside the seasonal Mahalanobis distance functions. It draws on a representative sample of weather stations and previously calculated seasonal log-logistic Mahalanobis distance functions. Thus, an elliptic-shaped tolerance region is calculated to quantitatively estimate a given proportion of the possible values of the wind vectors at a given time. The model was demonstrated on the metropolitan area of Tel Aviv. The results show that the spatial wind distribution can be very well represented by a small sample of merely four stations. The model’s results were found to be well within the confidence interval, leading to the conclusion that the model is fully capable of providing an accurate description of the current state of the urban wind field.