Relating optical and microwave grain metrics of snow: the relevance of grain shape

Abstract

Abstract. Grain shape is commonly understood as a morphological characteristic of snow that is independent of the optical diameter (or specific surface area) influencing its physical properties. In this study we use tomography images to investigate two objectively defined metrics of grain shape that naturally extend the characterization of snow in terms of the optical diameter. One is the curvature length λ 2 , related to the third-order term in the expansion of the two-point correlation function, and the other is the second moment µ 2 of the chord length distributions. We show that the exponential correlation length, widely used for microwave modeling, can be related to the optical diameter and λ 2 . Likewise, we show that the absorption enhancement parameter B and the asymmetry factor g G , required for optical modeling, can be related to the optical diameter and µ 2 . We establish various statistical relations between all size metrics obtained from the two-point correlation function and the chord length distribution. Overall our results suggest that the characterization of grain shape via λ 2 or µ 2 is virtually equivalent since both capture similar aspects of size dispersity. Our results provide a common ground for the different grain metrics required for optical and microwave modeling of snow.

Comparison of different methods to retrieve optical-equivalent snow grain size in central Antarctica

“…Snow albedo models are employed to calculate the spectral albedo and to invert the measurements to retrieve the optical snow grain size (e.g., Wiscombe and Warren, 1980). These albedo models mostly assume spherical grains, which is unrealistic because the grain shape is usually far from being spherical (e.g., Kokhanovsky and Zege, 2004;Libois et al, 2013;Leppä-nen et al, 2015;Krol and Löwe, 2016). Picard et al (2009) estimated an uncertainty of ±20 % when determining SSA from albedo measurements in case of an unknown snow grain shape.…”

SMRT: an active–passive microwave radiative transfer model for snow with multiple microstructure and scattering formulations (v1.0)

“…However, more recently Montpetit et al (2013) performed an optimization of the simulations with MEMLS on a large set of observations on the Arctic snowpack and found a different coefficient of 1.3. While the origin of this large discrepancy can be understood from the effect of shape (or equivalently size dispersity) of the 3-D microstructure (Krol and Löwe, 2016) it remains a practical problem, similar to the freedom of choosing an appropriate stickiness value. To this end we explore the connection between the Debye scaling factor and stickiness, or in other words, the equivalence between the exponential ACF with scaled correlation length and SHS.…”

“…As a remedy, more and more studies include predictions from different models (e.g., Wójcik et al, 2008;Rees et al, 2010;Roy et al, 2013;Kwon et al, 2015;Sandells et al, 2017) to draw more general conclusions. Other studies directly focused on the intercomparison of different models (Tedesco and Kim, 2006;Tse et al, 2007;Tian et al, 2010;Xiong and Shi, 2013;Pan et al, 2016;Löwe and Picard, 2015;Sandells et al, 2017;Royer et al, 2017) to quantify the differences.…”

Grand Challenges in Cryospheric Sciences: Toward Better Predictability of Glaciers, Snow and Sea Ice

“…While it is quite clear that such a theory will include-next to density-the specific surface area of the snow grains and curvature terms characterizing the snow-pore interface, it remains to be shown that a predictive microstructure model based on density, surface area and mean and/or Gaussian curvature is able to replace current more empirical microstructure models (Lehning et al, 2002). This step is currently being addressed in the snow microstructure community (e.g., Krol and Löwe, 2016) and will lead to the development of new snow models, which are able to describe mechanical and electromagnetic properties of snow in a more physical way.…”