Abstract

The Canopy Horizontal Array Turbulence Study (CHATS) took place in Spring 2007 and is the third in the series of Horizontal Array Turbulence Study (HATS) experiments. The HATS experiments have been instrumental in testing and developing sub-filter-scale (SFS) models for large-eddy simulations (LE... Show moreThe Canopy Horizontal Array Turbulence Study (CHATS) took place in Spring 2007 and is the third in the series of Horizontal Array Turbulence Study (HATS) experiments. The HATS experiments have been instrumental in testing and developing sub-filter-scale (SFS) models for large-eddy simulations (LES) of planetary boundary layer (PBL) turbulence. The CHATS campaign took place in a deciduous walnut orchard near Dixon, California and was designed to examine the impacts of tall vegetation on SFS turbulence. Measurements were collected both prior to and following leaf-out in order to capture the impact of the leaves on the turbulence, stratification and scalar source/sink distribution. CHATS utilized crosswind arrays of sonic anemometers (measuring velocity components and virtual temperature), fast water vapor and carbon dioxide sensors to investigate the impact of the canopy-imposed distribution of momentum extraction and scalar sources on SFS transport of momentum, energy and three scalars. To directly test and link with PBL parameterizations of canopy-modified turbulent exchange, CHATS also included a 30m profile tower instrumented with assorted turbulence instrumentation, fast and slow chemistry measurements, aerosol samplers, and radiation instrumentation. A high-resolution scanning backscatter LIDAR characterized the turbulence structure above and within the canopy; a scanning Doppler LIDAR, mini SODAR/RASS system, and a new helicopter observing platform provided details of the PBL-scale flow. Ultimately, the CHATS data set aims to improve parameterizations responsible for transporting energy and chemicals to and from vegetation which is a critical component of global and regional land, atmosphere, and chemical models. Show less