A Flux Footprint Approach for Estimating Effective Roughness Lengths to Augment Mesonet Station Metadata

Directionally varying surface roughness length estimates (z₀) can provide additional metadata for surface observation stations. Because z₀ quantifies the frictional and turbulent impacts of the local land cover on the flow, it is an essential component in the determination of the representativeness of a wind observation as well as in the standardization of wind observations in varying terrain. This is especially true for mesonet stations, which may not be sited with the same specifications as an ASOS site and thus may require significant ‘exposure' correction. In addition, roughness estimates that are determined independent of the wind (e.g., land cover) may serve as a QC mechanism by providing a measure of the expected wind speed variability that, in turn, can be compared directly with the observed gustiness. One such tool developed by the US EPA (AERSURFACE), uses 30 m resolution USGS National Land Cover Database classifications and assigns a seasonally varying z0 to each pixel. An inverse-distance weighting algorithm is then used to arrive at an ‘effective' z₀. However, studies performed on wind flow over transitions in surface roughness indicate the development of internal boundary layers (IBLs), where the modification to the wind profile due to the change in roughness first occurs at the roughness element height and builds vertically downwind via turbulent diffusion. Hence, IBLs generated from roughness transitions that occur immediately adjacent to and upwind of a 10 m anemometer may not be sufficiently vertically developed to impact the observation, i.e., these nearby roughness transitions will not be “seen” by the anemometer. Therefore, an alternative “flux footprint” weighting scheme is proposed in which the maximum impact of the roughness elements is shifted upstream of the observation location. Using various statistical measures, observed gust factors are then used to optimize the footprint dimensions to generate directionally varying z₀s that best explain the observed variability. A comparison between both weighting methods and the mesonet observations quantify the effectiveness of this approach.