Abstract

This study surveys the existing conceptual views of the influence of surface heterogeneity on spatial variability of fluxes into the atmosphere, and constructs an approach for using low-level aircraft to measure spatial variations of the surface moisture flux. This approach is applied to Canadian... Show moreThis study surveys the existing conceptual views of the influence of surface heterogeneity on spatial variability of fluxes into the atmosphere, and constructs an approach for using low-level aircraft to measure spatial variations of the surface moisture flux. This approach is applied to Canadian Twin Otter aircraft data collected during the Southern Great Plains Experiment. The response of turbulent moisture fluxes to surface heterogeneity is reduced by horizontal mixing by the turbulent eddies. During the evolution of the daytime convective boundary layer, the eddy size increases and the spatial variation of the moisture flux into the atmosphere is confined to larger horizontal scales. The influence of the surface heterogeneity on moisture fluxes into the atmosphere is framed here in terms of a horizontal blending scale, which can be used as guidance for partitioning the aircraft track. Problems with the estimation of the spatial variability of surface moisture fluxes from aircraft eddy correlation data are examined. Obtaining an adequate sample size of the moisture fluxes over heterogeneous surfaces is considerably more difficult than over homogeneous surfaces. Sampling requirements determine the maximum spatial resolution of the measured surface fluxes. The time-dependence of the spatial variation of the surface moisture flux is even more difficult to estimate since simple compositing of different aircraft passes would eliminate the time-dependence. A new scheme for estimating the time–space dependence of surface fluxes is developed. As an application example, this method is used to show that the evaporative fraction varies only slowly from morning to afternoon for the surface types examined here. Show less