A Lidar-based Investigation of Entrainment Zone Processes over a Semiarid Region

Date

2023-08

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Abstract

Within this work, we examined the entrainment zone (EZ) processes between the atmospheric boundary layer (ABL) and free atmosphere (FA) over a semiarid region of West Texas. Semiarid areas are of interest due to their strong surface heat fluxes and intense turbulent mixing that often results in the formation of deep ABLs. Despite their importance to ABL growth, observations of EZ processes remain scarce in the existing literature. Due to the distinct aerosol characteristics of the ABL and FA, a layer of heightened aerosol variability often exists near the top of the ABL (zi). We use the newly developed Full-Width Half-Maxima method to determine the depth over which the EZ processes occur (i.e., EZ thickness; EZT) based on the aerosol variability near zi. Exploring newfound relationships among the aerosol variability and turbulent kinematics within the ABL and EZ is essential for developing new comprehensive frameworks for parameterizing the ABL evolution. We used Doppler lidar measurements of aerosol backscatter coefficients (β) and vertical velocity (w) at Reese Center, located west of Lubbock, TX, for three specific cases (17 Jul 2022, 02 Sep 2022, and 08 Sep 2022). The relationship between the variability in β (𝜎𝛽,𝑧𝑖) and turbulent mixing observed at zi (𝜎𝑤,𝑧𝑖) was deduced as it related to the EZ thickness (Δℎ/𝑧𝑖). Overall, our results demonstrated a strong correlation between the Δℎ/𝑧𝑖 and 𝜎𝛽,𝑧𝑖 was observed for each case (r ≈ 0.86, 0.89, and 0.93), while a weak-to-moderate correlation was seen between 𝜎𝛽,𝑧𝑖 and 𝜎𝑤,𝑧𝑖 (r ≈ 0.33, 0.77, and 0.62). These results suggest an inherent relationship between the turbulent kinematics and the associated aerosol distribution within the EZ. More specifically, we observed that as the EZ turbulence intensified, the aerosol variability at zi increased, suggesting enhanced exchange between the ABL and overlying FA.


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Keywords

Atmospheric Boundary Layer, Aerosol, Entrainment, Lidar, Turbulence

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