Surface Hoar Layers Research Articles

Laboratory set-up for surface hoar layer growth over rounded grain snow

Growth of surface hoar (SH) crystals by deposition of water vapour onto the snow surface and their subsequent burial by snowfall leads to the formation of a persistent weak SH layer in mountain snowpack of Greater Himalayas. The mechanical properties and failure behaviour of SH layers are critical for avalanche hazard assessment as failure of buried SH layers is frequently the primary cause for the release of dry snow slab avalanches. However, extraction of snow samples containing sandwiched SH layer and their transportation to cold laboratory for experimentation is extremely difficult due to the fragile nature of SH layer. To overcome this limitation, we developed a setup that mimics the natural environmental conditions under which the SH crystals grow. The setup consists of a hot (Th = 1oC) and a cold (Tc = − 25oC) environmental chambers which are connected via an air-duct. The warm air from hot chamber is driven over a water bath through the air-duct towards the cold chamber. A sieved snow sample of desired dimension, which act as the base snow layer, is placed just below the air-duct outlet inside the cold chamber. Difference between the temperature and water vapour pressure of the air at duct outlet, and the surface of base snow layer, resulted in the deposition of water vapour and growth of SH crystals on the exposed snow surface. The growth of SH crystals was primarily observed in the direction normal to the snow surface while their typical shapes were predominantly plates, sector plates and dendrite. With the present setup, an SH layer of ∼10 mm thickness over a base snow layer of surface area 0.07 m2 could be grown within a time interval of ∼13 hr. The average growth rate of SH layer is found to be in the range of ∼0.5 − 0.9 mm hr−1 and the accommodation coefficient of water vapour derived from the analysis of growth measurements varied between α = 0.54 − 0.94 under different experimental conditions. Comparison of cumulative mass of deposited water vapour with measured SH layer thickness also yielded an apparent SH density of 42.5 kg m−3. The strength values (0.6 − 2.2 kPa) of snow samples with artificially grown sandwiched SH layer prepared in this study matches closely with weak layer strength reported in literature thereby indicating the usefulness of present experimental setup for investigating the multi-axial failure behaviour of surface hoar under controlled conditions.

Development of artificial surface hoar production system using a circuit wind tunnel and formation of various crystal types

The surface hoar layer is known to be one of the most typical types of weak layers in dry slab avalanches. To investigate the features of snow cover, including the surface hoar layer, we developed apparatuses that make surface hoar artificially. Two growth systems were developed using circuit wind tunnels. The first apparatus supplied wind using a circuit wind tunnel. A few millimeters thick of snow were placed on a cooling panel (0.12 m2) to form the fundamental snow layer. Surface hoar was then grown on the snow surface using the difference between the temperature and relatively high water vapor pressure of the air; and the cooled snow surface. The shapes of the artificially grown surface hoar crystals were simple plate, sector plate, dendrite, needle, column and cup. It was suggested that the crystal shape depended on snow surface temperature and wind condition. Both dendrite and needle shapes required low wind conditions. The trend of the temperature dependence of the crystal shape was often in agreement with the habit of artificial snow crystals and depth hoar crystals. In the second apparatus, we set nine cooling panels and made a surface hoar layer on the fundamental snow cover. It was possible to make surface hoar with an area of 1.08 m2 with one experiment using this system. Three surface temperatures could be selected in a single experiment because the panels were cooled by three different thermostatic baths. These systems are useful for investigating the features of snow and the surface hoar layer efficiently.

Modelling hazardous surface hoar layers across western Canada with a coupled weather and snow cover model

Destructive snow avalanches in western Canada are often caused by failure in buried surface hoar layers. Numerical snow cover models can simulate the formation and evolution of these layers, which could help avalanche forecasters assess the location and timing of avalanches. To investigate this application, we compared modelled surface hoar layers with snow and avalanche observations from the Coast, Columbia, and Rocky Mountains of western Canada. Surface hoar formation and evolution was modelled by forcing the snow cover model SNOWPACK with data from a high-resolution numerical weather prediction model. Surface hoar formation was verified with daily snow surface observations at 88 observation sites over two winters, and the evolution of buried layers was verified with avalanche observations and persistent weak layer assessments from 67 avalanche forecast regions. The frequency of surface hoar formation was over-predicted by 40%, although since modelled crystal sizes were moderately correlated with observed sizes, the more hazardous layers were often distinguished. A structural stability index in SNOWPACK often identified surface hoar layers during storm slab avalanche activity, but identifying layers during persistent slab avalanche activity was more difficult. Model limitations included uncertain meteorological inputs, errors in SNOWPACK's snow surface energy balance, and representing the necessary spatial scales. Despite these limitations, the coupled model resolved differences between major Canadian mountain ranges and could improve avalanche forecasts in data-sparse regions.

Meteorological, elevation, and slope effects on surface hoar formation

Abstract. Failure in layers of buried surface hoar crystals (frost) can cause hazardous snow slab avalanches. Surface hoar crystals form on the snow surface and are sensitive to micro-meteorological conditions. In this study, the role of meteorological and terrain factors was investigated for three layers of surface hoar in the Columbia Mountains of Canada. The distribution of crystals over different elevations and aspects was observed on 20 days of field observations during a period of high pressure. The same layers were modelled over simplified terrain on a 2.5 km horizontal grid by forcing the snow cover model SNOWPACK with forecast weather data from a numerical weather prediction model. Modelled surface hoar growth was associated with warm air temperatures, high humidity, cold surface temperatures, and low wind speeds. Surface hoar was most developed in regions and elevation bands where these conditions existed, although strong winds at high elevations caused some model discrepancies. SNOWPACK simulations on virtual slopes systematically predicted smaller surface hoar on south-facing slopes. In the field, a complex combination of surface hoar and sun crusts were observed, suggesting the simplified model did not adequately resolve the surface energy balance on slopes. Overall, a coupled weather–snow cover model could benefit avalanche forecasters by predicting surface hoar layers on a regional scale over different elevation bands.

Modelling the formation of surface hoar layers and tracking post-burial changes for avalanche forecasting

Predicting the spatial distribution and persistence of buried surface hoar layers is important when evaluating avalanche hazard. This study used weather-based models to predict the formation of surface hoar and investigated how buried layers change over time. Seven years of study plot observations from the Columbia Mountains of British Columbia were used to calibrate models for surface hoar formation. The latent heat flux was modelled with weather station data and forecasted data from the Canadian numerical weather prediction model (GEM15). A linear relationship was found between vapour mass flux and observed surface hoar crystal size (r2 of 0.84 with weather station data and 0.70 with GEM15 data), and was used to predict crystal size over seven winters. Crystal size predictions had root mean square errors of 2.4 and 4.1mm with weather station and GEM15 data, respectively. The model was compared with other empirical weather-based models. Layers of buried surface hoar were tracked with shear frame tests, compression tests (CT) and propagation saw tests (PST). PSTs and fracture character in CTs indicated that the propensity for propagation in layers of surface hoar remained high for up to six weeks. Layers with large crystals were found to weakly indicate low stability. Results from this study could be used to improve the representation of surface hoar layers in snow cover models and make spatial predictions with NWP data.

Forecasting the formation of critical snow layers using a coupled snow cover and weather model

The snow cover model SNOWPACK simulates snow cover formation and evolution based on meteorological data. In the past, these data were measured by automated weather stations. Recently, SNOWPACK was also forced with data from numerical weather prediction models (NWP). In this study we assess the capability of such a model chain to simulate two types of critical layers, i.e. surface hoar and melt–freeze crusts, for virtual north and south-facing slopes as well as a flat study plot. Meteorological key parameters for snow cover formation and evolution, e.g. precipitation, radiation, air temperature and relative humidity, were measured and compared to the forecasted data to evaluate the performance of the NWP model. Systematic errors of the NWP model were corrected and the adjusted data were used to force SNOWPACK. The formation of 80 critical layers – 35 surface hoar layers and 45 melt–freeze crusts – from seven winters between 2005 and 2012 observed in the Columbia Mountains of British Columbia, Canada were investigated. To assess the performance of SNOWPACK at simulating the presence and absence of critical layers on different aspects, monthly manual snow profiles systematically observed on north, south and flat terrains between January and March during the winter of 2010–2011 were compared to the corresponding snow cover simulations. The overall accuracy to predict the formation of melt–freeze crusts was found to be between 65% and 77% depending on the aspect. Surface hoar layers were modeled with accuracy between 89% and 100%. The presence and absence of critical layers within the snow cover – surface hoar and melt–freeze crusts – were investigated during the winter of 2010–2011. Ten out of eleven observed critical layers within the snow cover were simulated, resulting in an accuracy of 91%. However, some surface hoar layers and melt–freeze crusts were simulated but not observed resulting in a low accuracy for the absence of critical layers. The simulated snow height tended to be under-estimated during the early winter season and over-estimated during the late winter season for both slopes and the flat site, especially in March on the south-facing aspect. Nevertheless, the model chain is promising considering the source of the input data. This study showed that such a model chain could become a useful tool for avalanche warning services in the future, especially for data sparse areas.

Modeling the spatial distribution of surface hoar in complex topography

Abstract Buried surface hoar is a well-known weak snowpack layer, often associated with snow avalanches. Knowledge about the spatial distribution of surface hoar is therefore of great importance for avalanche forecasting. We investigate if spatial variations of surface hoar in mountainous terrain can be modeled based on terrain characteristics. Using a detailed radiation balance model, distributed radiation over an ensemble of 1800 simulated topographies, covering a wide range of terrain characteristics, was computed. Light winds and increased relative humidity were assumed to be favorable for surface hoar formation. To describe surface hoar formation, we derived a sky view factor threshold associated with the minimum snow surface cooling necessary for surface hoar formation based on laboratory measurements. To describe surface hoar destruction, as a first approach, we assumed that surface hoar only survives on shaded slopes. Applying two simple thresholds to our spatial radiation modelings, our results show that the spatial distribution of surface hoar is greatly affected by large-scale terrain roughness and sun elevation angle. Spatial correlation ranges for surface hoar, on the order of several hundred meters, were closely related to the typical spacing between mountains. Furthermore, correlation ranges of surface hoar decreased with increasing sun elevation angle. Overall, the modeled spatial patterns of surface hoar were in line with previously published spatial field observations, suggesting that simple terrain parameters can very well be used to describe the predominant surface hoar layer patterns in complex topography.

Spatial patterns of surface hoar properties and incoming radiation on an inclined forest opening

AbstractAvalanche hazard evaluation relies in part on representative snowpack stability observations. Thus, understanding the spatial patterns of snowpack instabilities and their environmental determinants is crucial. This case study integrates intensive field observations with spatial modeling to identify associations between incoming radiation, surface hoar development and its subsequent shear strength across an inclined forest opening. We examined a buried surface hoar layer in southwest Montana, USA, over five sampling days, collecting 824 SnowMicroPen resistance profiles and performing 352 shear frame tests. Spatial models of incoming long- and shortwave radiation were generated for the surface hoar formation period using modeled hemispheric sky visibility, physically based parameters and the Bird Clear Sky Radiation Model in a Geographic Information System. Before burial, the surface hoar persisted despite moderate winds and relatively high air temperatures. The buried surface hoar layer thickness varied between 3 and 21 mm within a distance of 30 m. Modeled incoming radiation explained spatial variations in layer thickness and shear strength. In areas exposed to large amounts of radiation, the surface hoar layer was strong and thin, while areas with limited incoming radiation (due to high sky visibility and shading) possessed a thicker surface hoar layer that sheared more easily. This demonstrates the usefulness of microclimate modeling for slope-scale avalanche hazard evaluation. We also identify that over the 3 week sample period, strengthening occurred without thinning of the surface hoar layer.

Failure of a layer of buried surface hoar

In order to study the formation of the initial failure leading to dry‐snow slab avalanche release, we performed loading experiments in a cold laboratory with natural samples including a layer of buried surface hoar. The experimental setup was such that the layered snow samples were loaded continuously for various tilt (slope) angles; loading rates varied between 1 and 20 Pa s−1. The stress at fracture decreased with increasing loading rate and increasing slope angle, i.e., increasing shear component of the load. The latter result means that the layer was stronger in compression than in shear which is attributed to the particularly anisotropic nature of layers of buried surface hoar. Particle image velocimetry revealed that almost 90% of the sample's global deformation was concentrated in the weak layer. For avalanche release our results imply that the shear component of deformation is of particular importance for failure initiation.

Micrometeorological and morphological observations of surface hoar dynamics on a mountain snow cover

The formation, growth, and destruction of surface hoar crystals is an important feature of mountain snow covers as buried surface hoar layers are a frequent weak layer leading to unstable snowpacks. The energy and mass exchange associated with surface hoar dynamics is further an important part of land‐atmosphere interaction over snow. A quantitative prediction of surface hoar evolution based on local environmental conditions is, however, difficult. We carried out measurements of crystal hoar size and total surface mass changes in the period between January and March 2007 on the Weissfluhjoch study plot of the WSL Institute for Snow and Avalanche Research SLF, located above Davos, Switzerland, at 2540 m above sea level. For the first time, a direct comparison between eddy correlation measurements of latent heat flux and lysimeter‐like measurements of surface mass change has been made. Results show that the growth of surface hoar crystals is very well correlated with deposition of water vapor during clear‐sky nights as measured by two eddy correlation systems placed close to the ground. By analyzing local meteorological data, we confirm that low to moderate wind speed, humid air, and clear‐sky nights are the necessary ingredients for the occurrence of significant vapor fluxes toward the surface and thus for the growth of surface hoar. We also confirm that surface hoar crystals tend to preserve during daytime, when strong sublimation occurs, although their size significantly reduces. Despite the complexities associated with mountain terrain and snow surfaces, such as nonequilibrium boundary layers and stratification effects, the hoar formation could be predicted by the snow cover model SNOWPACK, which uses a bulk Monin‐Obukhov (MO) parameterization for the turbulent heat fluxes. On the basis of the comparison between direct observations and model predictions, we suggest that neutral stability conditions in the MO formulation provide the most stable and least flawed prediction for surface hoar formation.

An attempt to describe the mechanism of surface hoar growth from valley clouds

In the Columbia Mountains of western Canada, avalanches release from buried surface hoar layers which form when stratus clouds extend across valleys on consecutive nights. Water vapor diffusion over distances of even a meter is much too weak to account for the growth, so non-diffusive movement in some form is necessary. One likely mechanism for the formation of surface hoar is rapid deposition of vapor as the cloud thickens and moves up and/or along the snow slope. Field evidence shows cloud movement both by rocking and lateral flow of the entire cloud. Since the time over which deposition could take place on any given night is short, successive nights are needed to produce a substantial deposit of hoar. More direct field observations of surface-hoar growth are needed to be certain that we have identified the dominant mechanisms.

Field observations on spatial variability of surface hoar at the basin scale

Surface hoar deposited on the snow surface represents, once buried by subsequent snowfall, one of the principal weak layers on which dry snow slab avalanches release. To predict instabilities caused by a buried surface hoar layer, its spatial extent needs to be known. Avalanche forecasting relies, among other things, on meteorological data from automatic stations. In principle, surface hoar formation can be predicted from these data. In order to study the spatial variation in surface hoar formation and destruction, daily observations were made during one winter at 23 locations of different aspect, slope inclination, and wind exposure within an area of about 3 km2. Four automatic weather stations were located within the study area: one on level terrain and three across a ridge. Despite the good instrumentation the correlation between surface hoar growth and calculated sublimation rate was poor. Distinct spatial patterns of surface hoar growth were found. Surface hoar crystals were frequently larger at the ridge site than in the surroundings of the automatic weather station on level terrain. The variation in surface hoar formation was mainly due to different prevailing wind regimes during the formation periods. The surroundings of the automatic weather station on level terrain were under the influence of local katabatic winds that dried up the air so that growth conditions were locally less favorable. Our observations suggest that predicting surface hoar formation for complex alpine terrain on the basis of data from an automatic weather station, the standard procedure in avalanche forecasting, seems nearly impossible unless at least the local wind regime is known at high resolution (≤10 m). For both surface hoar formation and surface hoar destruction observations suggest wind conditions to be most crucial for spatial variation.

Snow cover spatial variability at multiple scales: Characteristics of a layer of buried surface hoar

Abstract With the aim of a multi-scale analysis, we simultaneously observed snowpack stratigraphy and stability at three scales: the slope scale, the regional scale and the mountain range scale. The minimum spacing between measurements was 0.5 m and the maximum extent was about 17 km. Field measurements were made during five periods near Davos in the eastern Swiss Alps and focused on a layer of surface hoar. The layer was initially observed on the snow surface where it showed a high degree of spatial continuity. After burial the layer was less spatially continuous. Snow stability and its variation depended on the existence and size of the surface hoar layer. At the slope scale, measurements were made with a micro-penetrometer at a high spatial resolution and showed that the layer of buried surface hoar was spatially continuous at the slope scale. At the regional and mountain range scale various patterns in surface hoar size and presence were observed. A spherical semivariogram model fitted to the regional data gave a range of 500–1500 m. At the mountain range scale the distance of autocorrelation increased to around 10 km. The different lengths of autocorrelation indicate that the observed variability was the result of several physical processes with different typical scales.

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.

Temporal changes in the slope–scale spatial variability of the shear strength of buried surface hoar layers

Determining whether the snowpack is becoming more spatially variable or uniform is important for accurate avalanche forecasts. Greater variability increases uncertainty in extrapolation and prediction. Our results offer a look at the evolution of the spatial variability of shear strength of two buried surface hoar layers in southwestern Montana, USA, over time. We studied the layers from shortly after burial until they were no longer the weakest layer in the snowpack. We selected study sites with planar slopes, uniform ground cover, and wind-sheltered locations. This simplified the comparison of the plots by minimizing initial spatial differences so we could focus on temporal change. Within each site, we sampled four 14 m × 14 m plots with more than 70 shear frame tests in a layout optimized for spatial analysis. At both sites, the layers gained strength at a rate that slowed as the layers aged. Although there was little change in the relative variability, absolute variability increased through time. Temporal change was more pronounced when the layers were younger and were gaining strength more rapidly. Additional tests at one plot suggested a correlation length, or the distance at which test results are related, for shear strength of just a few meters. At the other plot, the surface hoar layer collapsed during the initial sample. An initial dramatic decrease in shear strength occurred after this collapse followed by strengthening during that day and into the following day. Though we measured increasing absolute variability through time, uncovering changes in our other measures of spatial variability proved elusive. Developing methods and techniques for adequately characterizing variability, and temporal changes in that variability, will continue to be challenging.

Refinements of empirical models to forecast the shear strength of persistent weak snow layers

Buried layers of surface hoar often release skier-triggered avalanches in the Columbia Mountains of Canada and their shear strength can be used to assess the stability of a slab overlaying these layers. In 2001 Chalmers introduced an Interval Model to calculate the shear strength of layers of surface hoar based on manual snowprofile observations. We refined his model by adjusting the measured shear strength for the normal load and included only data points where the weak layer depth did not exceed 100 cm to better account for skier triggering. Further, we used average and daily loading rates as well as a regression analysis to determine the best estimate of the shear strength change. Our final Forecasting Model used a multivariate regression to calculate the shear strength on days with snowprofile observations and as well as average and daily loading rates to forecast the shear strength on days without manual snowprofile observations. The performance of the model ( r 2 ) was 0.71 and 0.63 using average and daily loading rates, respectively. A companion paper, Part A, develops a forecasting model for weak layers of faceted crystals.

Refinements of empirical models to forecast the shear strength of persistent weak snow layers PART A: Layers of faceted crystals

Buried layers of surface hoar often release skier-triggered avalanches in the Columbia Mountains of Canada and their shear strength can be used to assess the stability of a slab overlaying these layers. In 2001 Chalmers introduced an Interval Model to calculate the shear strength of layers of surface hoar based on manual snowprofile observations. We refined his model by adjusting the measured shear strength for the normal load and included only data points where the weak layer depth did not exceed 100 cm to better account for skier triggering. Further, we used average and daily loading rates as well as a regression analysis to determine the best estimate of the shear strength change. Our final Forecasting Model used a multivariate regression to calculate the shear strength on days with snowprofile observations and as well as average and daily loading rates to forecast the shear strength on days without manual snowprofile observations. The performance of the model (r2) was 0.71 and 0.63 using average and daily loading rates, respectively. A companion paper, Part A, develops a forecasting model for weak layers of faceted crystals.

Changes in the shear strength and micro-penetration hardness of a buried surface-hoar layer

AbstractWe investigated a buried surface-hoar layer using the SnowMicroPen (SMP), an instrument designed to measure detailed snowpack profiles.We collected data from two adjacent parts of a slope 6 days apart. In addition, one manual snowpack profile was sampled each day, as well as 50 quantified loaded column tests (QLCTs) which provided an index of shear strength. For the SMP data, a 900 m2 area was sampled on both days in a grid with points 3 mapart, with some sub-areas of more closely spaced measurements. We collected 86 SMP profiles on the first day and 129 on the second day. Our analyses involved manually locating layer boundaries and calculating statistics for the force signal through the surface-hoar layer. The shear strength index increased by 40% between the two sampling days, but the SMP data show no statistical difference in layer thickness, and the mean, minimum, median, and a variety of percentile measures of the SMP force signal through the layer also do not change. Interestingly, the maximum hardness, and the variance and coefficient of variation of the SMP signal, increased. Since the small SMP tip might only break one or a couple of bonds as it passes through the weak layer, we interpret these changes as being indicative of increasing bond strength. Though we cannot specifically tie the increasing maximum hardness of the SMP signal to our QLCT results, our work suggests that the maximum SMP signal within buried surface-hoar layers may be useful for tracking increases in the shear strength of those layers.

A snow-profile-based forecasting model for skier-triggered avalanches on surface hoar layers in the Columbia Mountains of Canada

Surface hoar crystals grow on the snow surface and can form a persistent weak layer in the snowpack when buried. Skiers may trigger slab avalanches on such layers. These events can be difficult to forecast. During the winters 1995–2002, measurements of snowpack properties including surface hoar layers were collected at study sites in the Columbia Mountains of western Canada. These data were used to develop an empirical model to forecast the shear strength of buried surface hoar layers. The model, which is based on a snow profile and does not require shear strength measurements, accounts for 72% of the variability in the data. For five independent time series, the model estimates shear strength, on average, to within 22% of measured values within 8 days of the snow profile measurements. The profile-based shear strength model was used to forecast a stability index, which shows promise for forecasting skier-triggered slab avalanches on surface hoar layers in the Columbia Mountains. To further verify the suitability of the stability index, skier-triggered avalanche activity on buried surface hoar layers was correlated with the stability index based on shear frame tests, rather than snow profiles.

Avalanche characteristics of a transitional snow climate—Columbia Mountains, British Columbia, Canada

The focus of this study lies on the analysis of avalanche characteristics in the Columbia Mountains in relation to the local snow climate. First, the snow climate of the mountain range is examined using a recently developed snow climate classification scheme. Avalanche observations made by a large helicopter operator are used to examine the characteristics of natural avalanche activity. The results show that the Columbia Mountains have a transitional snow climate with a strong maritime influence. Depending on the maritime influence, the percentage of natural avalanche activity on persistent weak layers varies between 0% and 40%. Facet–crust combinations, which primarily form after rain-on-snow events in the early season, and surface hoar layers are the most important types of persistent weak layers. The avalanche activity characteristics on these two persistent weak layers are examined in detail. The study implies that, even though the ‘avalanche climate’ and ‘snow climate’ of an area are closely related, there should be a clear differentiation between these two terms, which are currently used synonymously. We suggest the use of the term ‘avalanche climate’ as a distinct adjunct to the description of the snow climate of an area. The more encompassing term should also include information, such as typically important snowpack weaknesses and avalanche activity statistics, which are directly relevant to avalanche forecasting.