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[AAS10-01] Incorporating spatial uncertainty functions for inference on rooftop wind fields' heterogeneity
Keywords:urban meteorology, wind observations, micrometeorology, spatial wind distribution, sparse meteorological network, Mahalanobis distance
The spatial wind field is a major factor in the transport and dispersion of air pollution in urban regions. This field is frequently characterized by an inherent spatial heterogeneity. This heterogeneity may be manifested by noticeable differences between rooftop level observations in adjacent locations. Quite often the degree of heterogeneity changes throughout the day.
While a dense weather stations’ network can provide real-time information on this heterogeneity, the prolonged maintenance of such network is expansive and technically demanding. This leads to networks that are too sparse to provide an accurate description on this characteristic. In situations where information regarding a specific urban region is insufficient, it is possible to conduct a two-phase scheme. First, deploy a dense weather stations’ network for a limited time. Then leave a sparse network to continuously monitor the region. Such a scheme requires the use of statistical methods in order to characterize the true wind field from the sparse network’s observations.
This study presents a method for inferring the wind field's heterogeneity at a given urban region. This is expressed by the range of wind vectors values that incorporate a given percent of the possible wind observations. This inference is based on the current properties of the wind field, which stem from a sparse network. These are added to an empirical Mahalanobis distance function, which quantifies the degree of spatial uncertainty. Such a function is based on past dense network’s observations. The method was applied to rooftop wind observations collected in the Tel-Aviv metropolitan area which consisted a network of more than 20 weather stations. Based on these stations, empirical Mahalanobis distance functions were calculated for each season. These functions were found to fit well the logistic distribution. The inference produced by incorporating these functions to statistical parameters calculated for a small network of merely four stations, exhibited a very well representation of the original spatial wind distribution.
While a dense weather stations’ network can provide real-time information on this heterogeneity, the prolonged maintenance of such network is expansive and technically demanding. This leads to networks that are too sparse to provide an accurate description on this characteristic. In situations where information regarding a specific urban region is insufficient, it is possible to conduct a two-phase scheme. First, deploy a dense weather stations’ network for a limited time. Then leave a sparse network to continuously monitor the region. Such a scheme requires the use of statistical methods in order to characterize the true wind field from the sparse network’s observations.
This study presents a method for inferring the wind field's heterogeneity at a given urban region. This is expressed by the range of wind vectors values that incorporate a given percent of the possible wind observations. This inference is based on the current properties of the wind field, which stem from a sparse network. These are added to an empirical Mahalanobis distance function, which quantifies the degree of spatial uncertainty. Such a function is based on past dense network’s observations. The method was applied to rooftop wind observations collected in the Tel-Aviv metropolitan area which consisted a network of more than 20 weather stations. Based on these stations, empirical Mahalanobis distance functions were calculated for each season. These functions were found to fit well the logistic distribution. The inference produced by incorporating these functions to statistical parameters calculated for a small network of merely four stations, exhibited a very well representation of the original spatial wind distribution.