Representativeness of Near-Surface Winds: Effects of Temporal Averaging, Spatial Separation, and Atmospheric Conditions in a Dense Tower Network

The representativeness of point measurements in the atmospheric boundary layer is a fundamental challenge for interpreting observations and evaluating numerical models. In this study, we quantify the representativeness of near-surface wind measurements using a dense network of 13 meteorological towers from the Army Research Laboratory’s Meteorological Sensor Array. These towers are distributed over an approximately 3 × 3 km domain at the U.S. Department of Agriculture Jornada Experimental Range in southern New Mexico. The analyzed domain consists of relatively flat terrain within a broader region of more complex topography. Representativeness is assessed using pairwise differences between towers and deviations from the array mean. Spatial variability decreases with temporal averaging, with the largest reductions occurring between 1 and 10 min and diminishing improvements beyond 10–30 min. Wind measurements become progressively less similar with increasing separation distance, particularly at separations approaching 1 km. Representativeness errors are larger under unstable conditions due to enhanced turbulence and spatial variability, while stronger winds increase wind speed variability but enhance directional coherence. Deviations from domain-averaged conditions are comparable among towers, indicating that no single location is uniquely representative. These results quantify the extent to which temporal averaging, spatial separation, and atmospheric conditions influence representativeness, providing practical estimates of the associated spatial scales and residual errors. The results are useful for interpreting observations, evaluating models, and designing sampling strategies using fixed and mobile platforms, including Uncrewed Aircraft Systems.