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Turbulence Statistics Measurements in a Northern Hardwood Forest

By M. G. Villani, Hans Peter Schmid, H. -B. Su, J. L. Hutton and Christoph S. Vogel


Tower-based turbulence measurements were collected in and over a mixed hardwood forest at the University of Michigan BiologicalStation (UMBS) UMBS∼flux site in the northern summerof 2000. Velocity and temperature fluctuations were measured at five levels within the canopy (up to the canopy height, H = 21.4 m), using one- and three-dimensional sonic anemometers and fine-wire thermocouples. Six additional thermocouples were distributed over the canopy-layer depth. Three-dimensional velocities and sonic temperatures were also measured above the canopy at 1.6H and at 2.15H on the AmeriFlux tower located at the UMBS∼flux site. Vertical profiles of buoyancy flux, mean horizontal velocity, Reynolds stress, and standard deviation and skewness of velocity components were calculated. The analysis of these measurements aims at a multi-layer parameterization framework of turbulence statistics forimplementation in Lagrangian stochastic models. Turbulence profiles and power spectra above the canopy were analyzed in the context of Monin-Obukhov similarity theory (MOST) and Kolmogorov theory, as determined by stability at the top level (2.15H), to assess the extent to which surface scaling is valid as the canopy top is approached. Velocity spectra were computed to explore the potential of estimating the viscous dissipation rate, and results show that the high frequency range of the spectra above the canopy exhibits the roll-off predicted by Kolmogorov theory. Similarly, velocity standard deviations above the canopy converge to MOST predicted values toward the top level, and spectral peaks shift with stability, as expected. Within the canopy, both turbulence statistics profiles and spectral distributions follow the general known characteristics inside forests

Publisher: Kluwer Academic Publishers; Springer Science+Business Media
Year: 2003
DOI identifier: 10.1023/A:1024118808670
OAI identifier:
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