Estimating wind velocity standard deviation values in the inertial sublayer from observations in the roughness sublayer

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• 作者：Simona Falabino ; Silvia Trini Castelli
• 刊名：Meteorology and Atmospheric Physics
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：129
• 期：1
• 页码：83-98
• 全文大小：
• 刊物类别：Earth and Environmental Science
• 刊物主题：Atmospheric Sciences; Meteorology; Math. Appl. in Environmental Science; Terrestrial Pollution; Waste Water Technology / Water Pollution Control / Water Management / Aquatic Pollution;
• 出版者：Springer Vienna
• ISSN：1436-5065
• 卷排序：129

文摘

In air quality practice, observed data are often input to air pollution models to simulate the pollutants dispersion and to estimate their concentration. When the area of interest includes urban sites, observed data collected at urban or suburban stations can be available, and it can happen to use them for estimating surface layer parameters given in input to the models. In such case, roughness sublayer quantities may enter the parameterizations of the turbulence variables as if they were representative of the inertial sublayer, possibly leading to a not appropriate application of the Monin–Obukhov similarity theory. We investigate whether it is possible to derive suitable values of the wind velocity standard deviations for the inertial sublayer using the friction velocity and stability parameter observed in the roughness sublayer, inside a similarity-like analytical function. For this purpose, an analysis of sonic anemometer data sets collected in suburban and urban sites is proposed. The values derived through this approach are compared to actual observations in the inertial sublayer. The transferability of the empirical coefficients estimated for the similarity functions between different sites, characterized by similar or different morphologies, is also addressed. The derived functions proved to be a reasonable approximation of the actual data. This method was found to be feasible and generally reliable, and can be a reference to keep using, in air pollution models, the similarity theory parameterizations when measurements are available only in the roughness sublayer.