Characterizing Turbulence in Quasi-Stationary Convective Mixed Layers Using Water Vapor Raman Lidar Profiles

The convective mixed layer (CML) is a fundamental aspect of atmospheric sciences, influencing areas from weather forecasting to environmental management. Understanding CMLs complex dynamics is critical for improving weather prediction and climate modeling. This research focuses on quantifying higher-order statistical moments within daytime CMLs by analyzing long-term, high-resolution water vapor measurements collected by the Raman Lidar operated by the Atmospheric Radiation Measurement (ARM) program at the Southern Great Plains (SGP) Central Facility in Lamont, Oklahoma, U.S.A. Using RLD data (collected at 10-second intervals with 60-meter spatial resolution) from 2016 to 2023, we compute higher-order moments like variance and skewness to evaluate the statistical properties of turbulent fluctuations. Vertical profiles of these moments are derived through the autocovariance method proposed by Lenschow et al. [2000]. Initial results provide important insights into turbulence in CMLs, especially regarding how convective processes affect moisture distribution and variability. The skewness profiles show a shift towards positive values near the top of the CML (zi), indicating changes in the asymmetry of moisture fluctuations. Atmospheric variance is lowest near the surface, reaches its peak just below zi, and then decreases, emphasizing that the most significant moisture fluctuations due to turbulent mixing occur within the CML, particularly just below its upper boundary. These results will improve our understanding of turbulence dynamics and offer valuable data for validating fine-scale numerical models, including those used for urban-scale simulations.