A four-Doppler Lidar study to quantify spatio-temporal heterogeneity of wind statistics over a deciduous forest during the LEAFF campaign

One fundamental assumption of surface layer flow theory is homogeneity over a horizontal plane, a hypothesis systematically challenged for many atmospheric flows. For example, variability in terrain elevation, presence of heterogeneous roughness sub-layers and mesoscale motions can alter the spatio-temporal flow evolution even over small distances (≈1 km). In this scenario, the experimental investigation of air-land interactions requires simultaneous data acquisitions at multiple sites, against which the hypothesis of flow homogeneity can be assessed. The Appalachian Mountains (in the Southeastern United States) represent a compelling environment to resolve complex flows over small distances due to their irregular terrain (800-1500 m elevation above sea level) and presence of moderately tall deciduous forests (~20 m) and open fields constituting an uneven roughness sub-layer. In this work, three nearby instrument sites (within 2 km of each another) are investigated as part of the Lidar Experiments for Assessing Flow over Forests (LEAFF) campaign located in and around a deciduous forest in mountainous Virginia (U.S.). Ten months of wind statistics are resolved both within the canopy by a well instrumented flux tower, and above it via four remote sensing Doppler Lidar (up to 300 m above ground, i.e. ≈15 times the forest height), thereby resolving the turbulent flow developing over a roughness sublayer with high statistical accuracy. The goal of the present analysis is twofold. First, to quantify the monthly variability of wind statistics induced by the annual cycles of leaf senescence and synoptic winds. Second, to quantify the heterogeneity of the wind statistics between different but closely spaced sites across different months. A year’s worth of data showed that the wind statistics are predominantly affected by synoptic forcing, while the leaf senescence cycle plays a marginal role in shaping mean wind and turbulence within the surface . Additionally, site-to-site heterogeneity is found to change following a monthly time scale, a result emphasizing the importance of selecting a sufficiently long observational period to correctly address site heterogeneity under different background flow conditions. The present study provides a compelling observational dataset to validate  numerical weather prediction tools accounting for the presence of a forest sub-layer, as well as improving our understanding of the physical mechanisms inducing flow heterogeneities over complex terrains.