Snow Micro-penetrometer Research Articles

A Poisson shot noise model for micro-penetration of snow

As a common starting point for the interpretation of SnowMicroPenetrometer (SMP) data the penetration force is interpreted as a superposition of spatially uncorrelated ruptures of structural elements which follow an ideal elastic–brittle response. We re-state this idea and describe the fluctuating penetration force as a Poisson shot noise process. This allows us to derive simple analytical expressions for the cumulants and the covariance of the penetration force in terms of the micromechanical, force-displacement parameters of individual elements. Vice versa, the micromechanical parameters can be estimated from the statistics of the penetration force. We test our method with simulated shot noise processes and real snow profiles and reveal potential limitations of the underlying assumptions. Our model unifies different previous approaches to snow classification which are based on snow penetration resistance. ► Re-interpretation of a common penetration model as shot noise. ► Analytical solution for force statistics and parameter estimation. ► Unification of previous, yet unrelated approaches.

Upward-looking ground-penetrating radar for measuring wet-snow properties

Abstract Snow stratigraphy information is among other sources the key data for assessing avalanche danger—not only for dry snow but also for wet-snow conditions. Until now this information is obtained by traditional snow pit observations or more recently by applying more quantitative methods such as the snow micro-penetrometer or dielectric devices. All these methods are destructive and only provide a snap shot in time of snowpack evolution. We used an upward-looking ground-penetrating radar system (upGPR) to monitor snowpack evolution on a daily or, whenever necessary, hourly basis to obtain information on wet-snow properties. We focused on determining the volumetric liquid water content ( θ w ) by calculating the effective permittivity ( e eff ) of the wet snow above the radar antennas, the advance of a wetting front and the wet-snow stratigraphy. e eff was obtained using the signal velocity and snow depth recorded with nearby ultrasonic gauges; θ w was calculated with different mixing model approaches. Results were compared to in-situ measured permittivity, modelled wetting front advance and modelled and measured outflow at the bottom of the snowpack. The upGPR system clearly showed the advance of a wetting front and the arrival time was similar to the one recorded with a nearby lysimeter. Possibly weak wet layers with high liquid water content ( θ w > 6%) were detected within the radar signal by multiple reflections. However, determining the exact amount of liquid water for each layer separately is still a task for future research.

Measuring spatial variations of weak layer and slab properties with regard to snow slope stability

Abstract Spatial variations of weak layer and slab properties are believed to affect snow slope stability. To quantify spatial variability at the slope scale, penetration resistance was measured with a high-resolution snow micro-penetrometer (SMP) in a partly randomized grid pattern. The grid design consisted of 46 SMP measurement locations. In addition, a full snow profile and 20 compression tests as well as a Rutschblock test at the snow profile location were performed within the grid. Fifteen slopes of different aspects were sampled of which 11 could be analysed. Weak layer and slab properties were characterised using non-spatial as well as spatial statistics and results were related to slope stability. The geostatistical analysis revealed that in more than half of the cases a range could be determined. Slab layers tended to have more spatial structure than the weak layer. Though some trends are apparent, firm conclusions on the dependence of slope stability on spatial variations were not possible due to the limited range of snow conditions in the dataset, and the fact that the definition of slope stability remains elusive. Based on our limited data set, we can therefore not specify the conditions when spatial variations of weak layer and slab properties are most relevant for snow slab release.

Accurate inversion of high‐resolution snow penetrometer signals for microstructural and micromechanical properties

Measurements of snow using a high‐resolution micropenetrometer can be used to discriminate between different snow types; in lower‐density snow the signal is sensitive to microstructure, and micromechanical properties can be estimated. Although a physics‐based snow penetration theory was first developed almost a decade ago, since that time the majority of studies using snow micropenetrometers have focused on using direct hardness measurements in statistical relationships. We use Monte‐Carlo simulations to rigorously test the existing physics‐based snow micropenetration theories over a wide range of parameters. These tests revealed four major sources of error in the inversion, which are corrected in this analysis. It is shown that this improved inversion algorithm can recover micromechanical parameters in synthetic data with much greater accuracy over the entire range of micromechanical properties observed in natural snow. Detailed examples of the inversion results are shown for eight different snow types, collected in both Alaskan and alpine snowpacks. The resulting micromechanical properties are distinctly different, indicating that a snow characterization from snow micropenetrometer estimates of micromechanical properties is likely possible. Estimates of the microscale elastic modulus, microscale strength, and structural element length make sense physically when compared to the qualitative descriptions of the different snow types. Microscale strength estimates are used to estimate macroscale strength values, and results from 33 different snow samples, covering a wide range of densities and snow types, are consistent with previously reported values from macroscale tests.

Stability algorithm for snow micro-penetrometer measurements

AbstractInformation on snow-cover stability is important for predicting avalanche danger. Traditionally, stability evaluation is based on manual observations of snow stratigraphy and stability tests, which are time-consuming. The SnowMicroPen (SMP) is a high-resolution, constant-speed penetrometer to measure penetration resistance. We have analysed the resistance signal to derive snow stability. The proposed stability algorithm was developed by comparing 68 SMP force–distance profiles with the corresponding manual profiles, including stability tests. The algorithm identifies a set of four potentially weak layers by taking into account changes in structure and rupture strength of microstructural elements that make up snow layers as derived from the SMP signal. In 90% of the cases, one of the four potentially weak layers proposed by the algorithm coincided with the failure layer observed in the stability test. To select the critical layer from the four potential weaknesses was more difficult. With fully automatic picking of the critical layer, agreement with the failure layer observed in the stability test was reached in 60% of the cases. To derive a stability classification, we analysed weak-layer as well as slab properties. These predictor variables allow the SMP signal to be classified into two stability classes, poor and fair-to-good, with an accuracy of ∼75% when compared with observed stability. The SMP, in combination with the proposed algorithm, shows high potential for providing snow-cover stability information at high resolution in time and space.

Variations in snow surface properties at the snowpack-depth, the slope and the basin scale

AbstractVariations of snow surface and snowpack properties affect avalanche formation. In up to four north-facing slopes above the tree line near Davos, Switzerland, snow surface properties were characterized. Penetration resistance was measured with a snow micro-penetrometer. The sampling scheme was designed to allow a multi-scale approach covering the snowpack depth scale (0.5–5 m), the slope scale (5–100 m) and the basin scale (100–1000 m). Observations and measurements were compared to the data of a nearby automatic weather station (AWS). The AWS data were also used to model snow-cover stratigraphy and its evolution with the numerical snow-cover model SNOWPACK. Comparing the four slopes showed that surface properties observed manually were similar among the three slopes that were sheltered, and often different from the slope that was wind-exposed. However, the penetration resistance of the surface layer was in most cases significantly different among slopes, although most values were <0.1 N, indicating very low hardness. These seemingly contradictory results follow from the different measurement support of the two methods. It is presently unclear which amount of variation at a given scale is relevant for avalanche formation. The geostatistical analysis and an analysis aimed at identifying the causes of variability were not conclusive. No patterns emerged that would allow conclusions regarding the effect on avalanche formation. Finding the causes of variability seems to require high-resolution terrain and weather models that are presently not readily available.

Surface hoar characteristics derived from a snow micropenetrometer using moving window statistical operations

This study aims to improve analytical techniques for studying stratigraphic dimensions and hardness characteristics of thin weak layers in the mountain snowpack, with particular interest to buried surface hoar layers. By determining which structural characteristics of such weak layers are associated with shear strength, we may be better able to monitor and predict stability, which is relevant for avalanche forecasting and management. We utilize moving window statistical operations to analyze SnowMicroPen (SMP) penetrometer hardness profiles of a buried surface hoar layer. Results indicate that significant weak layer thinning and hardening of the interface between the weak layer and its substratum coincided with significant increases in shear strength, as measured using a size-corrected shear strength index derived from concurrent stability tests. With aging, variations in slab thickness appeared to positively affect the hardness and inversely affect the coefficient of variation (CoV) of hardness of weak layer boundaries. These findings support previous research that proposed the strengthening of buried surface hoar layers results from the gradual penetration of the surface hoar crystals into the substratum which allows stronger bonds to form at this critical interface. These analytical techniques allow stratigraphic dimensions and hardness characteristics to be quantified and analyzed, improving our ability to monitor stratigraphic characteristics associated with shear strength and stability of the mountain snowpack.

Snow stratigraphy measurements with high-frequency FMCW radar: Comparison with snow micro-penetrometer

The stratigraphy of an alpine snowpack is very important for avalanche danger assessment, as well as interpretation of remote sensing measurements for hydrological purposes. Since spatial variability is often widespread, due mainly to wind, micro-climatic and topographic effects, extrapolating point measurements can be difficult. Tools which can quickly characterize snowpack stratigraphy, such as high frequency radar and mechanical probes, will be required for a complete understanding of the effects of spatial variability, however interpretation of these kinds of measurements still remains challenging. We compare measurements from a portable 8–18 GHz Frequency Modulated Continuous Wave (FMCW) radar with SnowMicroPenetrometer (SMP) and standard snowpit measurements. Although significant variability existed at the sub-meter scale, major stratigraphic horizons could be followed along radar profiles and identified in SMP measurements. A very thin hard crust (0.2–0.4 mm) that was continuous caused strong signals that were identifiable in both the SMP and the radar measurements at five different sites along a 10 m traverse. Two other more subtle transitions in the SMP signal were highly correlated with the locations of radar reflections. This work suggests that combining FMCW radar measurements, to characterize snowpack geometry, with SMP measurements, to characterize mechanical properties of layers, may be a useful technique for quantifying the spatial variability of the snowpack.

Spatial variability of micropenetration resistance in snow layers on a small slope

AbstractThe mechanisms leading to dry-snow slab release are influenced by the three-dimensional variability of the snow cover. We measured 113 profiles of penetration resistance with a snow micropenetrometer on an alpine snow slope. Seven distinct layers were visually identified in all snow micropenetrometer profiles. The penetration resistance of adjacent layers did not change abruptly, but gradually across layer boundaries that were typically 2 mm thick. In two layers, penetration resistance varied around 200% over the grid, possibly due to wind effects during or after layer deposition. Penetration resistance varied around 25%in five layers. Statistically significant slope-scale linear trends were found for all layers. The semivariogram was used to describe the spatial variation. Penetration resistance was autocorrelated, but the scale of variation was layer-specific. A buried layer of surface hoar was the most critical weak layer. It had little spatial variation. The layers in the slab above had higher spatial variation. The penetration resistance of each snow layer had distinct geostatistical properties, caused by the depositional processes.

Snow texture: a comparison of empirical versus simulated texture index for Alpine snow

AbstractThe texture of snow has a great impact on the mechanical and thermal properties of a snowpack. The texture index (TI) is a new concept to quantify the texture of snow at the micro- and mesoscale, a scale at which the properties of snow layers are responsible for the stability of a snowpack. It is calculated by taking the ratio of mean grain-size (mm) of snow to density (kg m−3) of snow in a given layer. It combines a microstructural with a mesostructural parameter. An empirically derived TI is compared with a simulated TI for laboratory experimental snow and natural snow profiles. TheTI was calculated throughout the development of three temperature-gradient snow experiments from laboratory measurements of grain-size and density. In a one-dimensional finite-element snowpack model (SNOWPACK), the development of the mean grain-size and density of these snow samples was simulated. It can be shown that the measured TI can be predicted by the modelled TI. Furthermore, three natural snow profiles from the Weissfluhjoch (Switzerland) test-field are compared with their simulations in terms of TI. This comparison reveals shortcomings in the measurement of fundamental parameters. It also shows the dependency of a good physical model on quantitative process descriptions at the microscale TheTI can be estimated using the force signal of the SnowMicroPen, a new high-resolution snow micropenetrometer. It is suggested that more accurate classical measurements combined with penetrometer measurements in the immediate vicinity of the snow profile will allow better verification of SNOWPACK simulations.