A novel parameterization of snow albedo based on a two-layer snow model with a mixture of grain habits A novel parameterization of snow albedo based on a two-layer snow model with a mixture of grain habits Academic Article uri icon


  • © 2019 American Meteorological Society. Snow albedo plays a critical role in the surface energy budget in snow-covered regions and is subject to large uncertainty due to variable physical and optical characteristics of snow. We develop an optically and microphysically consistent snow grain habit mixture (SGHM) model, aiming at an improved representation of bulk snow properties in conjunction with considering the particle size distribution, particle shape, and internally mixed black carbon (BC). Spectral snow albedos computed with two snow layers with the SGHM model implemented in an adding-doubling radiative transfer model agree with observations. Top-snow-layer optical properties essentially determine spectral snow albedo when the top-layer snow water equivalent (SWE) is large. When the top-layer SWE is less than 1 mm, the second-snow-layer optical properties have nonnegligible impacts on the albedo of the snow surface. Snow albedo enhancement with increasing solar zenith angle (SZA) largely depends on snow particle effective radius and also internally mixed BC. Based on the SGHM model and various sensitivity studies, single- and two-layer snow albedos are parameterized for six spectral bands used in NASA Langley Research Center's modified Fu-Liou broadband radiative transfer model. Parameterized albedo is expressed as a function of snow particle effective radii of the two layers, SWE in the top layer, internally mixed BC mass fraction in both layers, and SZA. Both single-layer and two-layer parameterizations provide band-mean snow albedo consistent with rigorous calculations, achieving correlation coefficients close to 0.99 for all bands.

altmetric score

  • 2.5

author list (cited authors)

  • Saito, M., Yang, P., Loeb, N. G., & Kato, S.

citation count

  • 8

publication date

  • May 2019