|Title||Scale invariance of albedo-based wind friction velocity|
|Publication Type||Journal Article|
|Year of Publication||2020|
|Authors||Ziegler N., Webb N, Chappell A., LeGrand S.|
|Journal||Journal of Geophysical Research: Atmospheres|
|ARIS Log Number||370317|
|Keywords||aeolian, aerodynamic roughness, Albedo, dust, friction velocity, satellite remote sensing|
Obtaining reliable estimates of aerodynamic roughness is necessary to interpret and accurately predict aeolian sediment transport dynamics. However, inherent uncertainties in traditional field measurements and models of surface aerodynamic properties continue to impact basic aeolian research, monitoring, and dust modeling. A new relation between aerodynamic shelter and land surface shadow has been established at the wind tunnel scale, enabling the potential for estimates of wind erosion and dust emission to be obtained from albedo data. Here, we compare estimates of wind friction velocity (u*) derived from traditional methods (wind speed profiles) with those derived from the shadow model at two separate scales using bare soil patch (via net radiometers) and landscape (via MODIS 500 m) datasets. Results show that estimates of u* from wind speed profiles are highly variable and therefore uncertain as they change in response to wind speed, direction, turbulence scales, and heterogeneity of the surface roughness. Shadow-based estimates of u* at both scales have small variability because they are integrated over the measurement area and resolve the partition of wind momentum between roughness elements and the soil surface. We demonstrate that the wind tunnel-based relation for predicting wind friction velocities at the soil surface (uS*) is scale invariant. We confirm the need for all wind speed profiles over a rough surface to be corrected for the drag partition if uS* is of interest. In the absence of a reliable drag partition correction in the field, measurements of albedo will reduce uncertainty in field estimates of uS* for wind erosion and dust emission modeling.