About the Relevance of Snow Microstructure Study in Cryospheric Sciences

2021 ◽  
Keyword(s):  
Snow Microstructure

scholarly journals Effect of snow microstructure variability on Ku-band radar snow water equivalent retrievals

2019 ◽  
Vol 13 (11) ◽  
pp. 3045-3059 ◽  
Author(s):  
Nick Rutter ◽  
Melody J. Sandells ◽  
Chris Derksen ◽  
Joshua King ◽  
Peter Toose ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Layer Thickness ◽  
Snow Water Equivalent ◽  
Estimation Accuracy ◽  
Snow Layer ◽  
Data Set ◽  
Snow Water ◽  
Retrieval Errors ◽  
Snow Microstructure ◽  
The Impact

Abstract. Spatial variability in snowpack properties negatively impacts our capacity to make direct measurements of snow water equivalent (SWE) using satellites. A comprehensive data set of snow microstructure (94 profiles at 36 sites) and snow layer thickness (9000 vertical profiles across nine trenches) collected over two winters at Trail Valley Creek, NWT, Canada, was applied in synthetic radiative transfer experiments. This allowed for robust assessment of the impact of estimation accuracy of unknown snow microstructural characteristics on the viability of SWE retrievals. Depth hoar layer thickness varied over the shortest horizontal distances, controlled by subnivean vegetation and topography, while variability in total snowpack thickness approximated that of wind slab layers. Mean horizontal correlation lengths of layer thickness were less than a metre for all layers. Depth hoar was consistently ∼30 % of total depth, and with increasing total depth the proportion of wind slab increased at the expense of the decreasing surface snow layer. Distinct differences were evident between distributions of layer properties; a single median value represented density and specific surface area (SSA) of each layer well. Spatial variability in microstructure of depth hoar layers dominated SWE retrieval errors. A depth hoar SSA estimate of around 7 % under the median value was needed to accurately retrieve SWE. In shallow snowpacks <0.6 m, depth hoar SSA estimates of ±5 %–10 % around the optimal retrieval SSA allowed SWE retrievals within a tolerance of ±30 mm. Where snowpacks were deeper than ∼30 cm, accurate values of representative SSA for depth hoar became critical as retrieval errors were exceeded if the median depth hoar SSA was applied.

scholarly journals Vapor flux and recrystallization during dry snow metamorphism under a steady temperature gradient as observed by time-lapse micro-tomography

2012 ◽  
Vol 6 (5) ◽  
pp. 1141-1155 ◽  
Author(s):  
B. R. Pinzer ◽  
M. Schneebeli ◽  
T. U. Kaempfer
Keyword(s):  
Temperature Gradient ◽  
Physical Properties ◽  
Time Lapse ◽  
Phase Changes ◽  
External Temperature ◽  
Vapor Flux ◽  
Snow Metamorphism ◽  
Diffusion Enhancement ◽  
Micro Tomography ◽  
Snow Microstructure

Abstract. Dry snow metamorphism under an external temperature gradient is the most common type of recrystallization of snow on the ground. The changes in snow microstructure modify the physical properties of snow, and therefore an understanding of this process is essential for many disciplines, from modeling the effects of snow on climate to assessing avalanche risk. We directly imaged the microstructural changes in snow during temperature gradient metamorphism (TGM) under a constant gradient of 50 K m−1, using in situ time-lapse X-ray micro-tomography. This novel and non-destructive technique directly reveals the amount of ice that sublimates and is deposited during metamorphism, in addition to the exact locations of these phase changes. We calculated the average time that an ice volume stayed in place before it sublimated and found a characteristic residence time of 2–3 days. This means that most of the ice changes its phase from solid to vapor and back many times in a seasonal snowpack where similar temperature conditions can be found. Consistent with such a short timescale, we observed a mass turnover of up to 60% of the total ice mass per day. The concept of hand-to-hand transport for the water vapor flux describes the observed changes very well. However, we did not find evidence for a macroscopic vapor diffusion enhancement. The picture of {temperature gradient metamorphism} produced by directly observing the changing microstructure sheds light on the micro-physical processes and could help to improve models that predict the physical properties of snow.

scholarly journals A general treatment of snow microstructure exemplified by an improved relation for the thermal conductivity

2012 ◽  
Vol 6 (6) ◽  
pp. 4673-4693 ◽  
Author(s):  
H. Löwe ◽  
F. Riche ◽  
M. Schneebeli
Keyword(s):  
Thermal Conductivity ◽  
Correlation Function ◽  
Pore Space ◽  
Anisotropy Parameter ◽  
Micro Computed Tomography ◽  
Data Set ◽  
Microstructural Parameters ◽  
Point Correlation ◽  
Point Correlation Function ◽  
Snow Microstructure

Abstract. Finding relevant microstructural parameters beyond the density is a longstanding problem which hinders the formulation of accurate parametrizations of physical properties of snow. Towards a remedy we address the effective thermal conductivity tensor of snow via known anisotropic, second-order bounds. The bound provides an explicit expression for the thermal conductivity and predicts the relevance of a microstructural anisotropy parameter Q which is given by an integral over the two-point correlation function and unambiguously defined for arbitrary snow structures. For validation we compiled a comprehensive data set of 167 snow samples. The set comprises individual samples of various snow types and entire time series of metamorphism experiments under isothermal and temperature gradient conditions. All samples were digitally reconstructed by micro-computed tomography to perform microstructure-based simulations of heat transport. The incorporation of anisotropy via Q considerably reduces the root mean square error over the usual density-based parametrization. The systematic quantification of anisotropy via the two-point correlation function suggests a generalizable route to incorporate microstructure into snowpack models. We indicate the inter-relation of the conductivity to other properties and outline a potential impact of Q on dielectric constant, permeability and adsorption rate of diffusing species in the pore space.

scholarly journals Analysis of QualitySpec Trek Reflectance from Vertical Profiles of Taiga Snowpack

Geosciences ◽  
2018 ◽  
Vol 8 (11) ◽  
pp. 404 ◽  
Author(s):  
Leena Leppänen ◽  
Anna Kontu
Keyword(s):  
Grain Size ◽  
Surface Area ◽  
Near Infrared ◽  
Sample Surface ◽  
Short Wave ◽  
Optical Remote Sensing ◽  
Mass Ratio ◽  
Data Set ◽  
Hemispherical Reflectance ◽  
Snow Microstructure

Snow microstructure is an important factor for microwave and optical remote sensing of snow. One parameter used to describe it is the specific surface area (SSA), which is defined as the surface-area-to-mass ratio of snow grains. Reflectance at near infrared (NIR) and short-wave infrared (SWIR) wavelengths is sensitive to grain size and therefore also to SSA through the theoretical relationship between SSA and optical equivalent grain size. To observe SSA, the IceCube measures the hemispherical reflectance of a 1310 nm laser diode from the snow sample surface. The recently developed hand-held QualitySpec Trek (QST) instrument measures the almost bidirectional spectral reflectance in the range of 350–2500 nm with direct contact to the object. The geometry is similar to the Contact Probe, which was previously used successfully for snow measurements. The collected data set includes five snow pit measurements made using both IceCube and QST in a taiga snowpack in spring 2017 in Sodankylä, Finland. In this study, the correlation between SSA and a ratio of 1260 nm reflectance to differentiate between 1260 nm and 1160 nm reflectances is researched. The correlation coefficient varied between 0.85 and 0.98, which demonstrates an empirical linear relationship between SSA and reflectance observations of QST.

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

2018 ◽  
Vol 11 (7) ◽  
pp. 2763-2788 ◽  
Author(s):  
Ghislain Picard ◽  
Melody Sandells ◽  
Henning Löwe
Keyword(s):  
Radiative Transfer ◽  
Thermal Emission ◽  
Electromagnetic Properties ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Hard Sphere Model ◽  
Snow Layer ◽  
Mathematical Basis ◽  
Snow Properties ◽  
Snow Microstructure

Abstract. The Snow Microwave Radiative Transfer (SMRT) thermal emission and backscatter model was developed to determine uncertainties in forward modeling through intercomparison of different model ingredients. The model differs from established models by the high degree of flexibility in switching between different electromagnetic theories, representations of snow microstructure, and other modules involved in various calculation steps. SMRT v1.0 includes the dense media radiative transfer theory (DMRT), the improved Born approximation (IBA), and independent Rayleigh scatterers to compute the intrinsic electromagnetic properties of a snow layer. In the case of IBA, five different formulations of the autocorrelation function to describe the snow microstructure characteristics are available, including the sticky hard sphere model, for which close equivalence between the IBA and DMRT theories has been shown here. Validation is demonstrated against established theories and models. SMRT was used to identify that several former studies conducting simulations with in situ measured snow properties are now comparable and moreover appear to be quantitatively nearly equivalent. This study also proves that a third parameter is needed in addition to density and specific surface area to characterize the microstructure. The paper provides a comprehensive description of the mathematical basis of SMRT and its numerical implementation in Python. Modularity supports model extensions foreseen in future versions comprising other media (e.g., sea ice, frozen lakes), different scattering theories, rough surface models, or new microstructure models.

scholarly journals Tomography-based determination of permeability and Dupuit–Forchheimer coefficient of characteristic snow samples

2011 ◽  
Vol 57 (205) ◽  
pp. 811-816 ◽  
Author(s):  
Emilie Zermatten ◽  
Sophia Haussener ◽  
Martin Schneebeli ◽  
Aldo Steinfeld
Keyword(s):  
Mass Transport ◽  
Effective Properties ◽  
Three Dimensional ◽  
Reynolds Numbers ◽  
Momentum Conservation ◽  
Micro Computed Tomography ◽  
Wide Range ◽  
Snow Microstructure ◽  
Semi Empirical

AbstractA tomography-based methodology for the mass transport characterization of snow is presented. Five samples, characteristic for a wide range of seasonal snow, are considered. Their three-dimensional (3-D) geometrical representations are obtained by micro-computed tomography and used in direct pore-level simulations to numerically solve the governing mass and momentum conservation equations, allowing for the determination of their effective permeability and Dupuit–Forchheimer coefficient. The extension to the Dupuit–coefficient is useful near the snow surface, where Reynolds numbers higher than unity can appear. Simplified semi-empirical models of porous media are also examined. The methodology presented allows for the determination of snow’s effective mass transport properties, which are strongly dependent on the snow microstructure and morphology. These effective properties can, in turn, readily be used in snowpack volume-averaged (continuum) models such as strongly layered samples with macroscopically anisotropic properties.

scholarly journals Comparison of traditional and optical grain-size field measurements with SNOWPACK simulations in a taiga snowpack

2015 ◽  
Vol 61 (225) ◽  
pp. 151-162 ◽  
Author(s):  
L. Leppänen ◽  
A. Kontu ◽  
J. Vehviläinen ◽  
J. Lemmetyinen ◽  
J. Pulliainen
Keyword(s):  
Grain Size ◽  
Snow Cover ◽  
Electromagnetic Waves ◽  
Field Measurements ◽  
Scaling Factor ◽  
Field Conditions ◽  
Meteorological Forcing ◽  
Sensing Applications ◽  
Variable Nature ◽  
Snow Microstructure

AbstractKnowledge of snow microstructure is relevant for modelling the physical properties of snow cover and for simulating the propagation of electromagnetic waves in remote-sensing applications. Characterization of the microstructure in field conditions is, however, a challenging task due to the complex, sintered and variable nature of natural snow cover. A traditional measure applied as a proxy of snow microstructure, which can also be determined in field conditions, is the visually estimated snow grain size. Developing techniques also allow measurement, for example, of the specific surface area (SSA) of snow, from which the optical-equivalent grain size can be derived. The physical snow model SNOWPACK simulates evolution of snow parameters from meteorological forcing data. In this study we compare an extensive experimental dataset of measurements of traditional grain size and SSA-derived optical grain size with SNOWPACK simulations of grain-size parameters. On average, a scaling factor of 1.2 is required to match traditional grain-size observations with the corresponding SNOWPACK simulation; a scaling factor of 2.1 was required for the optical equivalent grain size. Standard deviations of scaling factors for the winters of 2011/12 and 2012/13 were 0.36 and 0.42, respectively. The largest scaling factor was needed in early winter and under melting conditions.

scholarly journals A review of finite-element modelling in snow mechanics

2013 ◽  
Vol 59 (218) ◽  
pp. 1189-1201 ◽  
Author(s):  
E.A. Podolskiy ◽  
G. Chambon ◽  
M. Naaim ◽  
J. Gaume
Keyword(s):  
Finite Element ◽  
Approximate Solutions ◽  
Mechanics Of Solids ◽  
Model Parameters ◽  
The Finite Element Method ◽  
Element Analysis ◽  
Essential Information ◽  
Snow Microstructure ◽  
Snow Mechanics

The finite-element method (FEM) is one of the main numerical analysis methods in continuum mechanics and mechanics of solids (Huebner and others, 2001). Through mesh discretization of a given continuous domain into a finite number of sub-domains, or elements, the method finds approximate solutions to sets of simultaneous partial differential equations, which express the behavior of the elements and the entire system. For decades this methodology has played an accelerated role in mechanical engineering, structural analysis and, in particular, snow mechanics. To the best of our knowledge, the application of finite-element analysis in snow mechanics has never been summarized. Therefore, in this correspondence we provide a table with a detailed review of the main FEM studies on snow mechanics performed from 1971 to 2012 (40 papers), for facilitating comparison between different mechanical approaches, outlining numerical recipes and for future reference. We believe that this kind of compact review in a tabulated form will produce a snapshot of the state of the art, and thus become an appropriate, timely and beneficial reference for any relevant follow-up research, including, for example, not only snow avalanche questions, but also modeling of snow microstructure and tire–snow interaction. To that end, this correspondence is organized according to the following structure. Table 1 includes all essential information about previously published FEM studies originally developed to investigate stresses in snow with all corresponding mechanical and numerical parameters. Columns in Table 1 provide references to particular studies, placed in chronological order. Rows correspond to the main model parameters and other details of each considered case.

scholarly journals The thermal conductivity of seasonal snow

1997 ◽  
Vol 43 (143) ◽  
pp. 26-41 ◽  
Author(s):  
Matthew Sturm ◽  
Jon Holmgren ◽  
Max König ◽  
Kim Morris
Keyword(s):  
Thermal Conductivity ◽  
Quadratic Equation ◽  
Mean Value ◽  
Data Sets ◽  
Data Set ◽  
Seasonal Snow ◽  
Kinetic Growth ◽  
Image Position ◽  
Snow Microstructure ◽  
Independent Behavior

AbstractTwenty-seven studies on the thermal conductivity of snow (Keff) have been published since 1886. Combined, they comprise 354 values ofKeff, and have been used to derive over 13 regression equation and predictingKeffvs density. Due to large (and largely undocumented) differences in measurement methods and accuracy, sample temperature and snow type, it is not possible to know what part of the variability in this data set is the result of snow microstructure. We present a new data set containing 488 measurements for which the temperature, type and measurement accuracy are known. A quadratic equation,whereρis in g cm−3, andKeffis in W m−1K−1, can be fit to the new data (R2= 0.79). A logarithmic expression,can also be used. The first regression is better when estimating values beyond the limits of the data; the second when estimating values for low-density snow. Within the data set, snow types resulting from kinetic growth show density-independent behavior. Rounded-grain and wind-blown snow show strong density dependence. The new data set has a higher mean value of density but a lower mean value of thermal conductivity than the old set. This shift is attributed to differences in snow types and sample temperatures in the sets. Using both data sets, we show that there are well-defined limits to the geometric configurations that natural seasonal snow can take.

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