Geometric Versus Anemometric Surface Roughness for a Shallow Accumulating Snowpack

Abstract

When applied to a snow-covered surface, aerodynamic roughness length, z0, is typically considered as a static parameter within energy balance equations. However, field observations show that z0 changes spatially and temporally, and thus z0 incorporated as a dynamic parameter may greatly improve models. To evaluate methods for characterizing snow surface roughness, we compared concurrent estimates of z0 based on (1) terrestrial light detection and ranging derived surface geometry of the snowpack surface (geometric, z0G) and (2) vertical wind profile measurements (anemometric, z0A). The value of z0G was computed from Lettau’s equation and underestimated z0A but compared well when scaled by a factor of 2.34. The Counihan method for computing z0G was found to be unsuitable for estimating z0 on a snow surface. During snowpack accumulation in early winter, z0 varied as a function of the snow-covered area (SCA). Our results show that as the SCA increases, z0 decreases, indicating there is a topographic influence on this relation.

Snow Surface Roughness across Spatio-Temporal Scales

“…Since snowpack surfaces are continuously changing and evolving, snow surface roughness varies spatially (Figures 5a and 6a) [7,8] and temporally (Figures 5b and 6b) [6,8], as it is driven by interactions with meteorological forces. The observed spatio-temporal variability from the boards is partially due to the fine resolution (<1 mm) and small sampling extent (1 m); this scale can identify very local features that are linked to snow depth characteristics [8].…”

“…Numerous surface roughness metrics exist [14], but relatively few are used for snow due to the complexity of snow surface characteristics [11]. Values of z 0 can be computed using surface geometry [2,6,15] by identifying individual surface elements and computing the ratio of the cross-section area perpendicular to the wind to the horizontal area of the elements [13]. This geometric assessment may be performed through photogrammetry or structure from motion [52].…”

“…The geometry of the snowpack surface controls atmosphere-snow heat transfer [2], which influences global climate [3], impacts water resource availability through surface sublimation [4], and dictates blowing snow dynamics and accumulation patterns [5]. Most models consider snow surface roughness to be a constant parameter for snow on the ground [6], yet in reality, snow surface roughness is highly variable [6][7][8][9]. It has been noted that for snow cover on the canopy, roughness length has been presented as a function of the amount of snow cover [10].…”

Application of GIS and Remote Sensing in Determining Trends in Wind Energy Potential and Its Uses for Designing Development Strategies in Ghana

“…Where v is wind speed estimated at desired height, z; v0 is wind speed measured at the reference height, z0; α is the ground surface friction coefficient and calculated by the Counihan equation [33]. In this study, the vertical wind speed was interpolated at 10m of hub heights.…”

Special Issue on Remote Sensing of Snow and Its Applications

“…The SSA is an important snow parameter for the modeling of microwave emission and optical reflectance, and is therefore also important for remote sensing applications [29]. Sanow et al [30] presented terrestrial laser scanner-(TLS) (resolution of +/−5 mm) derived surface geometry and vertical wind profile measurements to compare concurrent aerodynamic roughness length estimates for changing snow surface features of shallow snowpack. The roughness of a snow surface is an important control on air-snow heat transfer and changes in the snow surface can have substantial effects on the energy balance at this interface [30].…”