Light-scattering sensor for monitoring properties of snow

Abstract

Real-time estimation of snow types and depth on a runway with stand-off sensing is essential for safe and efficient aircraft operations. Comprehensively identifying grain size, liquid water content (LWC), density and thickness is necessary for estimating the snow types and depth. However, off-the-shelf snow observation sensors are generally only optimized for one property of snow. In this study, we develop a light-scattering sensor consisting of lasers and image sensors, to obtain light-scattering images for comprehensively measuring the properties of snow. As a proof of concept demonstration, we measure snow samples with different grain size distribution, volumetric LWC, density and thickness, and obtain the relationship between the characteristics of snow and the optical scattering properties. For example, scattering intensity of the obtained image decreases as the grain size or volumetric LWC increases, and scattering area increases as the snow thickness increases. Additionally, given that multiple parameters can be extracted from a two-dimensional scattering image as well as with a different wavelength, by utilizing our sensor, we can simultaneously classify two properties of snow (e.g., grain size distribution and volumetric liquid water content, or density and thickness) on a scatter plot by extracting two indices from the obtained images. Our stand-off sensing technique shows great promise for improving safety and efficiency in aircraft operations during winter.