A generalized photon-tracking approach to simulate spectral snow albedo and transmissivity using X-ray microtomography and geometric optics
Abstract. A majority of snow radiative transfer models (RTM) treat snow as a collection of idealized grains rather than a semi-organized ice-air matrix. Here we present a generalized multi-layer photon-tracking RTM that simulates light transmissivity and reflectivity through snow based on x-ray microtomography, treating snow as a coherent structure rather than a collection of grains. Notably, the model uses a blended approach to expand ray-tracing techniques applied to sub-1 cm3 snow samples to snowpacks of arbitrary depths. While this framework has many potential applications, this study's effort is focused on simulating light transmissivity through thin snowpacks as this is relevant for surface energy balance applications and sub-nivean hazard detection. We demonstrate that this framework capably reproduces many known optical properties of a snow surface, including the dependence of spectral reflectance on snow grain size and incident zenith angle and the surface bidirectional reflectance distribution function (BRDF). To evaluate how the model simulates transmissivity, we compare it against spectroradiometer measurements collected at a field site in east-central Vermont. In this experiment, painted panels were inserted at various depths beneath the snow to emulate thin snow. The model compares remarkably well against the spectroradiometer measurements. Sensitivity simulations using this model indicate that snow transmissivity is greatest in the visible wavelengths and is limited to the top 5 cm of the snowpack for fine-grained snow, but can penetrate as deep as 8 cm for coarser grain snow. An evaluation of snow optical properties generated from a variety of snow samples suggests that coarse grained low density snow is most transmissive.
