Multi-resolution Image Regression and Correlation to Track Natural Snowfalls over Extreme Spatial Extent
K. Muller(1), R. Bölsterli(1,2), D.A. Emür(1), M. Lehning(3,4), F. Coletti(1)
1: Institute of Fluid Dynamics, Mechanical and Process Engineering, ETH Zurich, Sonneggstrasse 3, 8092, Zurich, Switzerland
2: Strømningstekniske Laboratorier, Department of Energy and Process Engineering, Faculty of Engineering, NTNU, 229 Gløshaugen, Trondheim, Norway
3: Laboratory of Cryospheric Sciences, EPFL Valais Wallis, Route des Ronquos 86, 1951, Sion, Switzerland
4: Snow and Atmosphere, Snow Processes, WSL Institute for Snow and Avalanche Research (SLF), Flüelastrasse 11, 7260, Davos Dorf, Switzerland
DOI:
We showcase snow particle tracking obtained from two field experiments. First, we reveal large-scale snowfall structures with a wavy streamwise flow in a planar snow tracking experiment, for which we recover the logarithmic law of the wall and signatures of rapid distortion. Secondly, performing volumetric tracking, we match snow particles over a large depth of field and scale up Voronoi analysis over previous advances, revealing a predominance of snow clustering and voids and apparent multi-modality in the snow fall speed.
