Reply on RC2

Abstract

Snow grain size is an important metric to determine snow age and metamorphism, but it is difficult to measure. The effective grain size can be derived from spaceborne and airborne radiance measurements due to strong attenuation of near-infrared energy by ice. Consequently, a snow grain size inversion technique that uses hyperspectral radiances and exploits variations in the 1.03 μm ice absorption feature was previously developed for use with airborne imaging spectroscopy. Previous studies have since demonstrated the effectiveness of the technique, though there has yet to be a quantitative assessment of the retrieval sensitivity to snowpack impurities, ice particle shape, or solar geometry. In this study, we use the Snow, Ice, and Aerosol Radiative (SNICAR) model and a Monte Carlo photon tracking model to examine the sensitivity of snow grain size retrievals to changes in dust and black carbon content, anisotropic reflectance, changes in solar illumination angle (θ0), and scattering asymmetry parameter (g) associated with different particle shapes. Our results show that changes in these variables can produce large grain size errors, especially when the effective grain size exceeds 500 μm. Dust content of 1000 ppm induces errors exceeding 800 μm, with the highest biases associated with small particles. Aspherical ice particles and perturbed solar zenith angles produce maximum biases of ∼540 μm and ∼400 μm respectively, when spherical snow grains and θ0 = 60° are assumed in the generation of the retrieval calibration curve. Retrievals become highly sensitive to viewing angle when reflectance is anisotropic, with biases exceeding 1000 μm in extreme cases. Overall, we show that a more detailed understanding of snowpack state and solar geometry improves the precision when determining snow grain size through hyperspectral remote sensing.