Unveiling the Dynamics of Snow Settling in Atmospheric Turbulence: A Review of Nearly a Decade of Field Research at EOLOS, MN

This paper provides a comprehensive synthesis of a decade’s advancements in field investigations into snow settling dynamics, carried out at the EOLOS field research station in Rosemount, MN, USA. The research deployed a succession of progressively sophisticated particle characterization and tracking systems, culminating in the development of a 3D particle tracking velocimetry (PTV) system. Our findings show the compelling role of preferential sweeping mechanisms, noting a consistent enhancement in snow particle settling velocities in association with turbulent eddies across multiple deployments. However, the magnitude of this turbulence-induced enhancement displayed variability, potentially modulated by the differences in turbulence and snow conditions inherent to each deployment. Additionally, intriguing patterns in snow clustering were revealed, characterized by an exponent power-law decay of -2 in cluster size distribution. This, coupled with a vertical orientation of clusters indicative of gravitational influences, adds a layer of complexity to our understanding of snow dynamics. Our research further deciphers the interplay between preferential sweeping and the preferential concentration, or clustering, of snow particles. Higher concentrations of snow particles were consistently found on the downward side of vortices, irrespective of vortex type, with an accelerated settling in these regions. Intriguingly, this disparity in average settling velocity was more pronounced in the case of prograde vortices. The advent of 3D particle tracking velocimetry (PTV) has allowed a nuanced examination of 3D settling trajectories and clustering dynamics. By providing insights into the intricate movements of snow particles as they traverse atmospheric turbulence, it uncovers the subtle variations in motion that could be overlooked in a 2D analysis. These findings offer valuable insights into the effects of snow particle morphology and turbulence on settling dynamics, promising to improve the accuracy of snowfall forecasts and ground snow accumulation predictions.