Semi-analytical Footprint Model Compliant with Arbitrary Atmospheric Stratification: Application to Monin–Obukhov Profiles

被引:0
|
作者
J.-C. Krapez
G. A. Ky
机构
[1] ONERA The French Aerospace Lab,
[2] DOTA,undefined
[3] Air Traffic Management Research Institute,undefined
来源
Boundary-Layer Meteorology | 2023年 / 187卷
关键词
Advection–diffusion equation; Flux footprint; Scalar footprint; Fetch; Boundary layer stability; Surface layer; Monin–Obukhov theory;
D O I
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中图分类号
学科分类号
摘要
A new model is proposed for the so-called scalar footprint and flux footprint in the atmospheric boundary layer. The underlying semi-analytical model allows computing the scalar concentration and flux fields related to turbulent diffusion of heat, water-vapor or to the dispersion of any scalar (e.g. passive pollutant) in the framework of K-theory. It offers improved capabilities regarding the representation of the gradual stratification in the boundary layer. In this model, the boundary layer is split in a series of sublayers in which the aerodynamic inertivity (a compound parameter aggregating wind-speed and eddy-diffusivity) is approximated by a sum of two power-law functions of a new vertical scale corresponding to the height-dependent downwind extension of the plume. This multilayer approach allows fitting with vanishing error any boundary-layer stratification, in particular those described by the Monin–Obukhov similarity theory (MOST) in the surface layer, while keeping the computation time of the footprint to low values. As a complement, a fully analytical surrogate model is presented for practical applications. For MOST profiles, the flux (resp. concentration) footprint is, to a RMS difference less than 1% (resp. 1.2%), equal (resp. equal to a constant multiplicative factor) to the inverse Gamma distribution. The optimal parameters of this distribution were evaluated for a broad range of atmospheric conditions and height. Regression formulas were also provided to compute the crosswind-integrated flux footprint distribution easily and with less than 1.6% RMS residual error. A comparison with the well-known footprint approximate model by Kormann and Meixner and the one by Hsieh, Katul and Chi has allowed quantifying their performances and limitations.
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页码:743 / 791
页数:48
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