The structure and evolution of the atmospheric boundary layer (ABL) under clear-sky fair weather conditions over mountainous terrain is dominated by the diurnal cycle of the surface energy balance and thus strongly depends on surface snow cover. We use data from three passive ground-based infrared spectrometers deployed in the East River Valley in Colorado’s Rocky Mountains to investigate the response of the thermal ABL structure to changes in surface energy balance during the seasonal transition from snow-free to snow-covered ground. Temperature profiles were retrieved from the infrared radiances using the optimal estimation physical retrieval TROPoe. A nocturnal surface inversion formed in the valley during clear-sky days, which was subsequently mixed out during daytime with the development of a convective boundary layer during snow-free periods. When the ground was snow covered, a very shallow convective boundary layer formed, above which the inversion persisted through the daytime hours. We compare these observations to NOAA’s operational High-Resolution-Rapid-Refresh (HRRR) model and find large warm biases on clear-sky days resulting from the model’s inability to form strong nocturnal inversions and to maintain the stable stratification in the valley during daytime when there was snow on the ground. A possible explanation for these model shortcomings is the influence of the model’s relatively coarse horizontal grid spacing (3 km) and its impact on the model’s ability to represent well-developed thermally driven flows, specifically nighttime drainage flows.

The Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors 2019 (CHEESEHEAD19) is an ongoing National Science Foundation project based on an intensive field campaign that occurred from June-October 2019. The purpose of the study is to examine how the atmospheric boundary layer responds to spatial heterogeneity in surface energy fluxes. One of the main objectives is to test whether lack of energy balance closure measured by eddy covariance (EC) towers is related to mesoscale atmospheric processes. Finally, the project evaluates data-driven methods for scaling surface energy fluxes, with the aim to improve model-data comparison and integration. To address these questions, an extensive suite of ground, tower, profiling, and airborne instrumentation was deployed over a 10×10 km domain of a heterogeneous forest ecosystem in the Chequamegon-Nicolet National Forest in northern Wisconsin USA, centered on the existing Park Falls 447-m tower that anchors an Ameriflux/NOAA supersite (US-PFa / WLEF). The project deployed one of the world’s highest-density networks of above-canopy EC measurements of surface energy fluxes. This tower EC network was coupled with spatial measurements of EC fluxes from aircraft, maps of leaf and canopy properties derived from airborne spectroscopy, ground-based measurements of plant productivity, phenology, and physiology, and atmospheric profiles of wind, water vapor, and temperature using radar, sodar, lidar, microwave radiometers, infrared interferometers, and radiosondes. These observations are being used with large eddy simulation and scaling experiments to better understand sub-mesoscale processes and improve formulations of sub-grid scale processes in numerical weather and climate models.