A Satellite Remote Sensing and Doppler LiDAR-based Framework for Evaluating Mesoscale Flows Driven by Surface Heterogeneity

TL;DR

This paper develops an observational framework that links surface heterogeneity to mesoscale boundary-layer flows by fusing GOES land surface temperature with Doppler LiDAR measurements. It introduces Dispersive Kinetic Energy (DKE) and its ratio to Mean Kinetic Energy (MKE) as indicators of heterogeneity-driven motions, and demonstrates their relationship to heterogeneity metrics under filtered atmospheric conditions. Large Eddy Simulations contextualize the results and reveal how LiDAR network configuration affects DKE estimation, showing that small networks can capture the essential signal. The approach provides a scalable, observationally grounded path to improve land-atmosphere interaction understanding and to inform parameterizations in climate and weather models.

Abstract

Surface heterogeneity, particularly complex patterns of surface heating, significantly influences mesoscale atmospheric flows, yet observational constraints and modeling limitations have hindered comprehensive understanding and model parameterization. This study introduces a framework combining satellite remote sensing and Doppler LiDAR to observationally evaluate heterogeneity-driven mesoscale flows in the atmospheric boundary layer. We quantify surface heterogeneity using metrics derived from GOES land surface temperature fields, and assess atmospheric impact through the Dispersive Kinetic Energy (DKE) calculated from a network of Doppler LiDAR profiles at the Southern Great Plains (SGP) Atmospheric Radiation Measurement (ARM) site. Results demonstrate that DKE and its ratio to the Mean Kinetic Energy (MKE) serve as effective indicators of heterogeneity driven flows, including breezes and circulations. The DKE and DKE ratio are correlated with metrics for surface heterogeneity, including the spatial correlation lengthscale, the spatial standard deviation, and the orientation of the surface heating gradient relative to the wind. The correlation becomes stronger when other flows that would affect DKE, including deep convection, low level jets, and storm fronts, are accounted for. Large Eddy Simulations contextualize the findings and validate the metric's behavior, showing general agreement with expectations from prior literature. Simulations also illustrate the sensitivity to configuration of LiDAR networks using virtual LiDAR sites, indicating that even smaller networks can be used effectively. This approach offers a scalable, observationally grounded method to explore heterogeneity-driven flows, advancing understanding of land-atmosphere interactions as well as efforts to parameterize these dynamics in climate and weather prediction models.

Figures (10)

• Figure 1: Vertical profiles for three example days (2018-05-21, 2018-07-19 and 2018-06-29) of one-hour averaged horizontal velocity at noon local time (a) and DKE from those profiles (b). Profiles of velocity are colored according to the site in the LiDAR network they come from. Also shown are the GOES LST surfaces (c) from each day with the location of the LiDAR measurements shown. Mean wind direction is also plotted as an arrow from the center site. For the surfaces, the lengthscale of heterogeneity and standard deviation of land surface temperature are also shown.
• Figure 2: LES profiles of DKE (top) and DKE/MKE (middle) through time in a large eddy simulation from 2016-06-25 as well as a horizontal cross section of the horizontal velocity and the surface temperature (bottom). The profiles on the left are from a homogeneous surface LES simulation where heterogeneity driven flows cannot be developed, and the profiles on the right are from a heterogeneous surface LES simulation.
• Figure 3: LiDAR profiles through time of the horizontal component of DKE (a,b) and the vertical component of DKE (c,d) averaged across a group of more homogeneous days (a,c) and more heterogeneous days (b,d). More heterogeneous days are all LiDAR days for which $\sigma_{T_s}>0.75$ and less heterogeneous days are those with $\sigma_{T_s}<0.75$
• Figure 4: LiDAR profiles through time of DKE/MKE averaged across a group of more homogeneous days (a) and more heterogeneous days (b). More heterogeneous days are all days for which $\sigma_{T_s}>0.75$ and less heterogeneous days are those with $\sigma_{T_s}<0.75$
• Figure 5: Scatterplots illustrating the relationship between the heterogeneity parameter $\lambda_{T_s}\sigma_{T_s}/\overline{T}_s$ and DKE/MKE (top), 1/MKE (middle) and DKE (bottom). The relationship is shown for all LiDAR days (left) and for a selection after a strong filter is applied (right). Spearman rank correlation coeficient is also shown. Scatter is colored according to the wind velocity at $1$ km ($u_g$). The strong filter restricts the analysis to days with at least 3 sites reporting, $u_g<15\ ms^{-1}$, $\alpha\geq70^{\circ}$, no daytime precipitation, and weak large-scale forcing $|\zeta|<2.5\times10^{-4}$