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

TL;DR

This paper presents a novel framework combining satellite remote sensing and Doppler LiDAR to observationally evaluate mesoscale flows driven by surface heterogeneity, improving understanding and parameterization in climate models.

Contribution

It introduces a new observational approach using DKE metrics and satellite data to quantify heterogeneity-driven flows, validated through Large Eddy Simulations.

Findings

DKE and DKE ratio effectively indicate heterogeneity-driven flows.

Strong correlation between DKE metrics and surface heterogeneity measures.

Smaller LiDAR networks can reliably capture heterogeneity-driven flow dynamics.

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.