Quantifying micromechanical and microstructural properties of snow is crucial, as they control bulk thermal, electrical and mechanical properties. Snow density can provide an estimate of mechanical properties, while direct observation of snow microstructure is necessary to determine mechanical properties. We utilized a novel non-contacting laser ultrasound system, providing high-frequency acoustic waveform measurements, to observe mechanical properties at the microscale. We investigated the temporal relationship between p-wave velocity, snow crystal type, p-wave modulus, stiffness and specific surface area (SSA). We created homogeneous snow samples, each composed of a single type of precipitation particle, compacted to a density of 250 kg m-3. We measured wave propagation through these snow samples and observed changes in p-wave speed during equilibrium metamorphism. We also measured SSA with the InfraSnow Sensor, as well as micromechanical properties with the SnowMicroPenetrometer. With these data, we observed changes in mechanical properties, with up to a factor of 2 increase in elastic modulus depending on crystal type during a period of 72 hours. Estimated elastic moduli increased over time, as expected and in agreement with previous work. Needles and columns had faster p-wave velocities, implying larger elastic modulus, compared to plates and dendrites, and had a higher rate of change within the first hour.

We determined the compressive strength of weak layers of faceted crystals and depth hoar using artificially grown samples with a wide range of microstructural morphologies in a cold laboratory setup. Micro-computed tomography (µCT) imaging showed that the microstructures of the artificial samples were comparable to that of natural depth hoar. We performed compression experiments in a displacement controlled testing machine on 92 depth hoar samples with densities ranging from 150 kg m−3 to 350 kg m−3. The compressive strength spanned two orders of magnitude (1–150 kPa) at strain rates of about 10−3 s−1 at $-5^{\circ}\mathrm{C}$ and followed a power law as a function of density. Several microstructural metrics such as the specific surface area, connectivity density and correlation lengths obtained from µCT measurements exhibited a statistically significant relationship with compressive strength. Analysis of the residuals of the power law fit showed that in addition to density, horizontal correlation lengths also correlated with strength. However, in this study, density remained the dominant predictor of the compressive strength of depth hoar.

The relationship between the behavior of water in snow and its microstructure is crucial to improve the prediction of wet snow disasters. X-ray computed tomography (X-ray CT) is frequently used to observe snow microscopically. However, distinguishing between ice and water in the X-ray images is difficult because ice exhibits an X-ray absorption coefficient similar to that of water. In contrast, magnetic resonance imaging (MRI) acquires nuclear magnetic resonance (NMR) signals of protons in a liquid and visualizes the NMR signal intensity, enabling discrimination between water and ice signals. However, snow grains and pore spaces cannot be distinguished in MRI images because they do not generate NMR signals.

To investigate the relationship between the microstructure of snow and the distribution of liquids in snow, we developed a novel method that combines X-ray CT and MRI images to compensate for the disadvantages associated with each method. Using this method, we successfully visualized where liquid (C12H24) occupied pore spaces. We also showed the possibility of using C12H24 instead of water to obtain water retention curve of snow cover, which is a fundamental aspect of hydraulic properties. Although there is room for improvement in the visualization of water in snow, such as shortening the imaging time to escape snow metamorphosis and image superimposition methods, this method is expected to effectively elucidate the behavior of water in snow and clarify the characteristics of wet snow.

We developed a Handheld Integrating Sphere Snow Grain Sizer (HISSGraS) for field use to measure the specific surface area (SSA) of snow. In addition to snow samples, HISSGraS can directly measure snow surfaces and snow pit walls. The basic measurement principle is the same as that of the IceCube SSA instrument. The retrieval algorithm for SSA from reflectance employs two conversion equations formulated using spherical and nonspherical grain shape models. We observed SSAs using HISSGraS, IceCube and the gas adsorption method in a snowfield in Hokkaido, Japan. Intercomparison of the results confirmed that with HISSGraS direct measurement, SSA profile observations can be completed in just ~1/10 the time required for measurement of snow samples. Our results also suggest that HISSGraS and IceCube have similar accuracy when the same snow samples are measured using the same grain shape model. However, SSAs of near-surface snow layers measured using the three techniques exhibited some biases, possibly due to rapid snow metamorphism or melting during measurement and some technical issues with optical techniques. When excluding SSA data for the surface layer, which metamorphosed remarkably during measurement, IceCube- and HISSGraS-derived SSAs correlated strongly with those obtained by gas adsorption and HISSGraS accuracy is 21–34%.

Once fallen, snow settles due to the combined effects of metamorphism and deformation of the ice matrix under gravity. To understand how these coupled processes affect snow evolution, we performed oedometric compression tests and continuously monitored the snow microstructure with X-ray tomography. Centimetric samples with an initial density between 200 and 300 kg m−3 were followed during an initial sintering phase and under two different loads of 2.1 and 4.7 kPa at $-8^\circ$C for ~1 week. The microstructure captured at a voxel size of 8.5 μm was characterized by density, specific surface area (SSA) and two metrics related to bond network, namely the Euler characteristic and the minimum cut surface. Load-induced creep of the ice matrix was observed only for sufficiently low values of initial density (<290 kg m−3 in our tests), and was shown to be associated to a significant increase of the number of bonds. Application of the load, however, did not affect the individual bond size nor the SSA, which appeared to be mainly controlled by isothermal metamorphism. The uniaxial compression did not induce any creation of anisotropy on the microstructural characteristics. Overall, our results show that, for the considered conditions, the deformation of the ice matrix mainly leads to a reduction of the pore space and an increase of the coordination number, while metamorphism mainly affects the grain and bond sizes.

Casting snow is necessary to prevent metamorphism and deformation prior to X-ray micro-computed tomography (μCT) imaging. Current methods are insufficient for large-scale field sampling of snow due to safety considerations associated with the casting medium and/or lengthy sample preparation times. Here, a casting method using contrast-enhanced diethylphthalate (DEP) for μCT of snow is presented. The X-ray contrast of DEP is enhanced with barium titanate nanoparticles (BaTiO3) and iodine (I2). A partially unsupervised, three-phase segmentation method utilizing traditional Gaussian smoothing followed by a three-step process to address transition voxels is also presented. Synthetic images derived from real snow samples are used to evaluate the segmentation method with various configurations of trapped air bubbles. Real snow samples spanning a range of specific surface areas (SSAs) (8–28 m2 kg−1) and densities (135–463 kg m−3) are used to assess the performance of the segmentation method on real, cast samples. The method yields SSA, density and correlation length errors of less than 10% for synthetic images with air bubble surface areas less than 333 m−1 per sample volume for eight of the nine snow samples. For eight of the nine cast samples, the method yields errors of less than 10% for all three parameters.

Snow appears as a granular material in most engineering applications. We examined the role of grain shape and cohesion in angle of repose experiments, which are a common means for the characterization of granular materials. The role of shape was examined by investigating diverse snow types with discernable shape and spherical ice beads. Two geometrical shape parameters were calculated from X-ray micro-computed-tomography images after a particle segmentation was performed with a watershed algorithm. Cohesion was examined by conducting experiments at six different temperatures between −40 and −2°C, assuming sintering as its cause, which accelerates with increasing temperature. As a cohesionless reference, experiments with glass beads were performed. We found that both shape and cohesion exerted about equally strong influence on the angle of repose. We utilized our results for an empirical model that describes the influence of shape and cohesion as additive corrections of the angle of repose of cohesionless spheres and explains all experiments with a correlation coefficient r2 = 0.95. With temperature and the chosen shape parameter as fitting variables, previous experiments with another snow type could be consistently included. The experiments highlight the relevance of these parameters in granular snow mechanics and can be used for model calibration.

Spectral reflectance of natural snow samples representing various stratigraphies was investigated in a controlled dark laboratory environment. Mean and Std dev. of band specific reflectance values were determined for several satellite sensor bands utilized in remote sensing of snow. The reflectance values for dry, moist, wet and wet and littered snow for different instruments varied between 0.63–0.97 in the visible and near-infrared bands at an incoming light zenith angle of θ = 55°. The results indicate that in MODIS band 4 (545–565 nm), essential to snow mapping, the reflectance of snow drops by 9% when dry snow changes to wet snow and by a further 10% when typical forest litter inclusions exist on the wet snow surface. A separate investigation of individual snow types revealed that they can be grouped either as dry or wet snow based on their spectral behavior. However, some snow types were located between these two distinct groups, such as snow with near-surface melt-freeze crusts, and could not be clearly distinguished. The reflectance statistics collected and analyzed here can be directly used to refine accuracy characterization and parametrization of snow mapping algorithms, such as the SCAmod method, used for the mapping of snow cover area.

Snow properties relevant to the fracture processes involved in dry-snow slab avalanche release include weak layer specific fracture energy, slab elastic modulus and density. Various techniques exist to determine these snow mechanical properties, but it is presently unclear how values determined with different methods compare. In the laboratory, the 3-D microstructure of cm-sized snow samples is reconstructed by micro-computed tomography (μCT) so that density and elastic modulus can be computed. In the field, fracture energy and modulus are estimated based on particle tracking velocimetry (PTV) of the displacement field observed during propagation saw tests. Snow stratigraphy is measured with the snow micro-penetrometer (SMP) in either, field or laboratory. We compared SMP-derived properties to corresponding μCT- and PTV-derived values. Values of snow density related well to μCT results and so were SMP-derived elastic moduli related to PTV-derived values. By taking into account snow anisotropy a good relation between SMP- and μCT-derived moduli resulted suggesting the SMP-derived modulus characterizes the components of the modulus perpendicular to the axis of penetration. SMP- and PTV-derived values of fracture energy were correlated. The SMP can provide a bridge between scales and techniques, yet further improvements in signal interpretation are still needed.

Snow metamorphism and settlement change the microstructure of a snowpack simultaneously. Past experiments investigated snow deformation under isothermal conditions. In nature, temperature gradient metamorphism and settlement often occur together. We investigated snow settlement in the first days after the onset of temperature-gradient metamorphism in laboratory experiments by means of in-situ time-lapse micro-computed tomography. We imposed temperature gradients of up to 95 K m−1 on samples of rounded snow with a density of ~230 kg m−3 and induced settlement by applying 1.7 kPa stress with a passive load on the samples simultaneously. We found that snow settled about half as fast when a temperature gradient was present, compared with isothermal conditions. The change in specific surface area after 4 days caused by temperature-gradient metamorphism was only a few percent. The viscosity evolution correlated with the amount of the temperature gradient. Finite element simulations of the snow samples revealed that stress-bearing chains had developed in the snow structure, causing the large increase in viscosity. We could show that a small change in microstructure caused a large change in the mechanical properties. This explains the difficulty of predicting snow mechanical properties in applications such as firn compaction or snow avalanche formation.

The microstructure and stratigraphy of a snowpack determine its physical behaviour. Weak layers or weak interfaces buried under a slab are prerequisites for the formation of dry-snow slab avalanches, and a precise characterization of weak layers or interfaces is essential to assess stability. Yet their exact geometry and micromechanical properties are poorly known. We cast weak layers and their adjacent layers in the field during two winters and reconstructed their three-dimensional microstructure using X-ray microcomputer tomography. The high resolution of 10–20 μm allowed us to study snow stratigraphy at the microstructural scale. We quantified the microstructural variability for 32 centimetre-sized layered samples and we calculated Young’s modulus and Poisson’s ratio by tomography-based finite-element simulations. Layers in a sample could therefore be differentiated not only by a change in morphology or microstructure, but also by a change in mechanical properties. We found a logarithmic correlation of Young’s modulus with density for two different density ranges, consistent with previous studies. By calculating the relative microstructural changes within our samples, we showed that a large change could indicate a potential weak layer, but only when the weak layer and both adjacent layers, i.e. the sandwich, were considered.

The instrumented sample holder Snowbreeder 5 is used to investigate the simultaneous influence of settlement on temperature-gradient snow metamorphism in time-lapse micro-computed tomography experiments. So far, experiments have only been done on temperature-gradient snow metamorphism without settlement or settlement under isothermal conditions. With the new device we can impose a constant temperature gradient on a snow sample and induce settlement by placing a passive load on top of the snow sample. The weight of the load can be varied, simulating various snow heights on top of the snow sample. Snow-temperature measurements on the passive load are possible due to wireless data transfer via Bluetooth. The temperature gradient is set by controlling the air temperature inside the computer tomograph and by a Peltier element at the bottom of the snow sample. First experiments under isothermal conditions and a constant temperature gradient of 43 K m−1 showed that the settlement was reduced to almost half as soon as a temperature gradient was applied under otherwise almost equal snow conditions. The compactive viscosity in the isothermal experiment was in the range of literature values.

Microwave radar amplitude within a snowpack can be strongly influenced by spatial variability of internal layer boundaries. We quantify the impact of spatial averaging of snow stratigraphy and physical snowpack properties on surface scattering from near-nadir frequency-modulated continuous-wave radar at 12–18 GHz. Relative permittivity, density, grain size and stratigraphic boundaries were measured in-situ at high resolution along the length of a 9 m snow trench. An optimal range of horizontal averaging (4–6 m) was identified to attribute variations in surface scattering at layer boundaries to dielectric contrasts estimated from centimetre-scale measurements of snowpack stratigraphy and bulk layer properties. Single vertical profiles of snowpack properties seldom captured the complex local variability influencing near-nadir radar surface scattering. We discuss implications of scaling in-situ measurements for snow radiative transfer modelling and evaluation of airborne microwave remote sensing of snow.