Simulating isothermal aging of snow

Vetter, Roman; Sigg, Stephan; Singer, H. M.; Kadau, Dirk; Herrmann, Hans J.; Schneebeli, Martin

doi:10.1209/0295-5075/89/26001

Cited by 24 publications

(35 citation statements)

References 17 publications

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“…The Kelvin effect is seen as the driving force for isothermal snow metamorphism (Bader et al, 1939;Colbeck, 1980). Recent studies indicate that sublimation deposition is the dominant contribution for temperatures close to the melting point, whereas surface diffusion dominates at temperatures far below the melting point (Vetter et al, 2010). Snow has a high permeability, which facilitates diffusion of gases and, under appropriate conditions, airflow (Gjessing, 1977;Colbeck, 1989;Sturm and Johnson, 1991;Waddington et al, 1996).…”

Section: Introductionmentioning

confidence: 99%

Tomography-based monitoring of isothermal snow metamorphism under advective conditions

Ebner

Schneebeli²,

Steinfeld

2015

The Cryosphere

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Abstract. Time-lapse X-ray microtomography was used to investigate the structural dynamics of isothermal snow metamorphism exposed to an advective airflow. The effect of diffusion and advection across the snow pores on the snow microstructure were analysed in controlled laboratory experiments and possible effects on natural snowpacks discussed. The 3-D digital geometry obtained by tomographic scans was used in direct pore-level numerical simulations to determine the effective permeability. The results showed that isothermal advection with saturated air have no influence on the coarsening rate that is typical for isothermal snow metamorphism. Isothermal snow metamorphism is driven by sublimation deposition caused by the Kelvin effect and is the limiting factor independently of the transport regime in the pores.

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Section: Introductionmentioning

confidence: 99%

Tomography-based monitoring of isothermal snow metamorphism under advective conditions

Ebner

Schneebeli²,

Steinfeld

2015

The Cryosphere

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“…3 and 4 in Schleef and Löwe, 2013) which increases A. The ice matrix is squeezed such that the air pores are filled with above situated ice grains which move into the gaps by compaction (Theile et al, 2011;Löwe et al, 2011;Schleef and Löwe, 2013), possibly 25 complemented by rotation of individual fragments of the ice matrix , and possibly also by falling of above situated ice grains into the air-filled gaps (Vetter et al, 2010). In the absence of detailed quantitative work about the anisotropy of this process we start with the simplest assumption of an affine deformation where all length scales of the structure inherit the macroscopically imposed scale change from strain.…”

Section: Gravitational Settlingmentioning

confidence: 99%

Modeling the Evolution of the Structural Anisotropy of Snow

Leinß¹,

Löwe²,

Proksch³

et al. 2019

Preprint

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Abstract. The structural anisotropy of snow that originates from a spatially anisotropic distribution of the ice matrix and the pore space, is a key quantity to understand physical snow properties and to improve their parameterizations. To this end we propose a minimal empirical model to describe the temporal evolution of the structural anisotropy and publish the extensive, calibration dataset consisting of meteorological, radar, and micro computer tomography (CT) data. The dataset was acquired near the town of Sodankylä in Northern Finland. The model is tailored to immediate implementation into common snow pack models driven by meteorological data as its parametrization is solely based on macroscopic, thermodynamic fields. Here we use output data of the physical model SNOWPACK to drive our model. The model implements rate equations for each snow layer and accounts for snow settling and temperature gradient metamorphism, which are taken to be the main drivers of the temporal evolution of the structural anisotropy. The model is calibrated with available time series of anisotropy measurements spanning four different winter seasons. The calibration measurements were obtained from polarimetric radar data which were analyzed with respect to the dielectric anisotropy of snow. From the detailed comparison between simulated anisotropy and radar time series we identify settling as the main mechanism causing horizontal structures in the snow pack. The comparison also confirms temperature gradient metamorphism as the main mechanism for vertical structures. For validation of the model we use full-depth profiles of anisotropy measurements obtained from CT data. The results show that the model can predict the measured CT profiles quite accurately. For depth hoar, differences between modeled anisotropy and the anisotropy derived from exponential correlation lengths are observed and discussed in view of potential limitations.

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“…This material exhibits specific features such as a natural tendency, induced by metamorphism, to minimise its surface energy (e.g. Flin et al, 2003;Vetter et al, 2010). Moreover, these former studies tended to focus on properties linked to volumetric material contents, while less attention was paid to the surface area of the segmented object.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity of snow density and specific surface area measured by microtomography to different image processing algorithms

et al. 2016

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Abstract. Microtomography can measure the X-ray attenuation coefficient in a 3-D volume of snow with a spatial resolution of a few microns. In order to extract quantitative characteristics of the microstructure, such as the specific surface area (SSA), from these data, the greyscale image first needs to be segmented into a binary image of ice and air. Different numerical algorithms can then be used to compute the surface area of the binary image. In this paper, we report on the effect of commonly used segmentation and surface area computation techniques on the evaluation of density and specific surface area. The evaluation is based on a set of 38 X-ray tomographies of different snow samples without impregnation, scanned with an effective voxel size of 10 and 18 µm. We found that different surface area computation methods can induce relative variations up to 5 % in the density and SSA values. Regarding segmentation, similar results were obtained by sequential and energy-based approaches, provided the associated parameters were correctly chosen. The voxel size also appears to affect the values of density and SSA, but because images with the higher resolution also show the higher noise level, it was not possible to draw a definitive conclusion on this effect of resolution.

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Simulating isothermal aging of snow

Cited by 24 publications

References 17 publications

Tomography-based monitoring of isothermal snow metamorphism under advective conditions

Tomography-based monitoring of isothermal snow metamorphism under advective conditions

Modeling the Evolution of the Structural Anisotropy of Snow

Sensitivity of snow density and specific surface area measured by microtomography to different image processing algorithms

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