Model for effective thermal conductivity of a dry snow cover composed of uniform ice spheres

Adams, Edward; Sato, Atsushi

doi:10.1017/s026030550001168x

Cited by 35 publications

(23 citation statements)

References 5 publications

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“…Traditionally, estimates of snow thermal properties are based on readily-measurable characteristics such as density and grain size, though it is well known that the geometric forms of the microstructure are a first-order effect. Adams and Sato (1993) were the first to try to derive a physical model for snow conductivity considering snow microstructure (packed spheres) and distinguishing between ice conduction, conduction in the pore space and vapor transport. This microstructure-based conductivity model has been successfully implemented in SNOWPACK (Lehning et al, 2002a).…”

Section: Thermal Conductivitymentioning

confidence: 99%

Snow physics as relevant to snow photochemistry

et al. 2008

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Abstract. Snow on the ground is a complex multiphase photochemical reactor that dramatically modifies the chemical composition of the overlying atmosphere. A quantitative description of the emissions of reactive gases by snow requires knowledge of snow physical properties. This overview details our current understanding of how those physical properties relevant to snow photochemistry vary during snow metamorphism. Properties discussed are density, specific surface area, thermal conductivity, permeability, gas diffusivity and optical properties. Inasmuch as possible, equations to parameterize these properties as functions of climatic variables are proposed, based on field measurements, laboratory experiments and theory. The potential of remote sensing methods to obtain information on some snow physical variables such as grain size, liquid water content and snow depth are discussed. The possibilities for and difficulties of building a snow photochemistry model by adapting current snow physics models are explored. Elaborate snow physics models already exist, and including variables of particular interest to snow photochemistry such as light fluxes and specific surface area appears possible. On the other hand, understanding the nature and location of reactive molecules in snow seems to be the greatest difficulty modelers will have to face for lack of experimental data, and progress on this aspect will require the detailed study of natural snow samples.

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Section: Thermal Conductivitymentioning

confidence: 99%

Snow physics as relevant to snow photochemistry

et al. 2008

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“…In addition, parametrization of snow physical properties such as permeability (Shimizu, 1970;Calonne et al, 2012;Zermatten et al, 2014) and thermal conductivity (Adams and Sato, 1993;Sturm et al, 1997;Calonne et al, 2011) are linked to density. Snow models like SNTHERM (Jordan, 1991), CROCUS (Brun et al, 1989), and SNOWPACK (Lehning et al, 2002) adopted density for the parametrizations of such properties, and models describing ventilation and air flow (Albert, 1996), isotopic content in polar snow (Neumann and Waddington, 2004;Town et al, 2008), or drifting snow (Lenaerts et al, 2012) also require density.…”

Section: Introductionmentioning

confidence: 99%

Intercomparison of snow density measurements: bias, precision, and vertical resolution

Proksch

Rutter

Fierz³

et al. 2016

The Cryosphere

107

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Abstract. Density is a fundamental property of porous media such as snow. A wide range of snow properties and physical processes are linked to density, but few studies have addressed the uncertainty in snow density measurements. No study has yet quantitatively considered the recent advances in snow measurement methods such as micro-computed tomography (µCT) in alpine snow. During the MicroSnow Davos 2014 workshop, different approaches to measure snow density were applied in a controlled laboratory environment and in the field. Overall, the agreement between µCT and gravimetric methods (density cutters) was 5 to 9 %, with a bias of −5 to 2 %, expressed as percentage of the mean µCT density. In the field, density cutters overestimate (1 to 6 %) densities below and underestimate (1 to 6 %) densities above a threshold between 296 to 350 kg m −3 , dependent on cutter type. Using the mean density per layer of all measurement methods applied in the field (µCT, box, wedge, and cylinder cutters) and ignoring ice layers, the variation between the methods was 2 to 5 % with a bias of −1 to 1 %. In general, our result suggests that snow densities measured by different methods agree within 9 %. However, the density profiles resolved by the measurement methods differed considerably. In particular, the millimeter-scale density variations revealed by the high-resolution µCT contrasted the thick layers with sharp boundaries introduced by the observer. In this respect, the unresolved variation, i.e., the density variation within a layer which is lost by lower resolution sampling or layer aggregation, is critical when snow density measurements are used in numerical simulations.

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“…Based on Adams et al (1993), the equivalent thermal conductivity for saturated moist air kg can be written as:…”

Section: Snow Component Propertiesmentioning

confidence: 99%

Modeling and simulation of melting process in a snow sleeve on overhead conductors /

Zhang¹

2011

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The general objective of this PhD study is to develop models that simulate the snow melting process on overhead conductors and predict snow shedding under various meteorological and current transmission conditions. In an attempt to validate this new model, a number of experimental tests were carried out in the CIGELE cooling chamber and wind tunnel, and the results obtained from these tests were then compared with those from numerical simulations.Firstly, two-dimensional Reynolds-Average Navier-Stokes (RANS) simulations were implemented in FLUENT software to predict both the local heat-transfer coefficient distribution along the snow sleeve surface in a cross flow of air, and the overall heattransfer rate. These investigations reveal the characteristics of forced convection around a snow sleeve, especially the effects resulting from the roughness of the snow surface and the non-circular shape of the sleeve. The study shows that roughness has a significant effect on the heat transfer rate, although the effect of the non-circular shape is negligible in most cases. The computational results show a satisfactory concordance with the theoretical analyses as well as with the experimental data derived from the literature in the field.Secondly, a microstructure model was developed to estimate the equivalent thermal conductivity of dry snow. This study describes the relationship between the equivalent thermal conductivity and the microstructure of dry snow under different temperatures regimes. These results were compared with those obtained in prior research, and showed good agreement. A set of experiments was carried out at the CIGELE laboratories and the results were compared with those produced by this particular model. Furthermore, the relationship between the snow conductivity model and the weather is introduced here. Elles montrent aussi l'effet significatif de la rugosité de surface sur le taux de transfert de la chaleur.iv Deuxièmement, un modèle microstructural a été développé pour estimer la conductivité thermique équivalente de la neige sèche, qui établit la relation entre la conductivité thermique équivalente et la microstructure de la neige sèche dans divers régimes de température. Ces résultats ont été comparés avec ceux de recherches antérieures, montrant un bon accord. De plus, une série d'expériences a été réalisée dans les laboratoires de la CIGELE et leurs résultats ont été comparés avec ceux du modèle. Finalement, une relation entre le modèle de conductivité de la neige et la température a été proposée.Troisièmement, un modèle numérique 2-D en fonction du temps, de la percolation de l'eau dans un manchon de neige fondante a été établi sur la base de la méthode de Galerkin. L'influence de la vitesse du vent, de la température de l'air, de l'effet Joule, de la rugosité de la surface de la neige et de la dimension des grains de neige a été étudiée. Les résultats du modèle montrent que l'effet Joule et la rugosité de la surface de neige ont des effets notables sur la percolation de l'eau. Le temps ...

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Model for effective thermal conductivity of a dry snow cover composed of uniform ice spheres

Cited by 35 publications

References 5 publications

Snow physics as relevant to snow photochemistry

Snow physics as relevant to snow photochemistry

Intercomparison of snow density measurements: bias, precision, and vertical resolution

Modeling and simulation of melting process in a snow sleeve on overhead conductors /

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