Electrical engineers at Colorado State University have created an imaging system that classifies snowflakes to help predict hazardous events. The system uses a muti-angle snowflake camera that simultaneously captures five high-resolution views of a snowflake in free-fall, along with the flake’s speed, to help reconstruct three-dimensional images. Engineers perform electromagnetic scattering analysis on these reconstructions, and then the simulated data is compared with actual radar data from a local weather radar facility. Knowing the size and fall speed of snowflakes could help weather forecasters accurately predict the amount of snowfall and provide betters warnings about impending storms.
Graduate student Cameron Kleinkort was awarded the Spiros G. Geotis Student Prize at the 37th American Meteorological Society Conference on Radar Meteorology held in Oklahoma in September. He was honored for his paper and presentation titled “3D Shape Reconstruction of Snowflakes from Multiple Images, Meshing, Dielectric Constant Estimation, Scattering Analysis, and Validation by Radar Measurements.” The new camera is more accurate than any other available snowflake shape reconstruction. In his paper, he writes, “The process is almost completely automatized and streamlined from the handling of the MASC images, to the visual hull method and the creation of meshes that adequately represent features of the geometry, to the estimation of the dielectric constant, and finally the scattering analysis.”
Source: Colorado State University | Photo by AMagill 
