Sensitivity of snow grain size retrievals to dust content, ice particle asphericity, and solar zenith angle

Abstract:

The effective grain size of snow is a critical factor in the determination of spectral albedo and snowpack evolution. Consequently, Nolin and Dozier, (2000) developed a snow grain size retrieval algorithm using hyperspectral radiance data from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). This algorithm generates a lookup table based on a normalized band area at the ice absorption feature centered at 1.03 μm. A suite of studies has since demonstrated the effectiveness of the technique though imaging and contact spectroscopy. However, the original study noted that a dusty snowpack may lead to biased retrievals, and there has yet to be a quantitative analysis of this effect. In this study, we used 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 Saharan dust, San Juan dust, Greenland dust, and black carbon. We performed additional sensitivity analyses for changes in solar zenith angle (θ₀) and scattering asymmetry parameter (g) to mimic the influence of different ice particle shapes. Our results show that a change in these variables may produce large grain size errors, especially when r_eff ≥ 500 μm. Dust contents of 1000 ppm induce errors in grain size retrievals of up to 829 μm, with the highest biases seen at small particle size distributions (0.05-0.5 μm). Aspherical particles (g = 0.75) and perturbed solar zenith angles (Δθ₀ = 15°) result in maximum biases of 580 μm and 408 μm, respectively. Modeled spectra indicate a masking of the ice absorption feature in extreme cases, leading to the smaller grain size retrievals. The biases shown here indicate that knowledge of the snowpack state is important when determining snow grain size.