Turbulence statistics measurements in a northern hardwood forest

TitleTurbulence statistics measurements in a northern hardwood forest
Publication TypeJournal Article
Year of Publication2003
AuthorsVillani M.G, Schmid HPeter, Su H-B, Hutton J.L, Vogel CS
JournalBoundary-Layer Meteorology
Volume108
Pagination343-364
KeywordsTURBULENCE
Abstract

Tower-based turbulence measurements were collected in and over a mixed hardwood forest at the University of Michigan Biological Station (UMBS) UMBS~flux site in the northern summer of 2000. Velocity and temperature fluctuations were measured at five levels within the canopy (up to the canopy height, H = 21.4m), 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 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 for implementation 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.

DOI10.1023/A:1024118808670