In order to facilitate the measurement of liquid-water content of snow in high mountains, a portable calorimeter named “Endo-type snow-water content meter” was developed. It is composed of a metal-coated container made of insulating materials and a lid of the container with a small-thermistor thermometer. Its strong points are its light weight, small size and easy fabrication with cheap materials. The total weight of the device is as light as 250 g, which is less than 10% of the snow-water content meter widely used in Japan (Akitaya-type snow-water content meter). The results of experiments have revealed that the device is capable of measuring the liquid-water content within 2 minutes with an accuracy of 2% by weight.

A new constant-speed penetrometer for field and laboratory measurement has been developed. The initially independent work of SFISAR and CRREL has been brought together, and a portable field device is now in an advanced stage of testing. The new penetrometer has high rigidity and a high-resolution large dynamic range force sensor. It uses a much smaller sensing head (5 mm) than previous designs and has a constant-speed drive. With this construction, the penetration resistance of very fine layers and the influence of the bonding strength between snow grains can be more accurately determined than is possible with the rammsonde or Pandalp. Artificial foam layers as thin as 2 mm and thin layers in snow have been detected by the penetrometer. Thin snow layers detected from penetration-resistance profiles have been correlated to fine layering as determined from plane-section microphotographs of samples taken adjacent to the profile. The instrument’s measurements are highly repeatable and the lack of subjective decisions when operating the penetrometer makes the penetration resistance a quantitative measure of snow stratigraphy.

The determination of snow-grain characteristics (such as type or size) is crucial for validating snow models and interpreting remote-sensing data. Until now, the size and type of snow grains have been estimated in the field. Nowadays, the transport of a snow sample in iso-octane allows laboratory analyses. The objective analysis of snow-grain digital images has been developed at the Centre d'Études de la Neige since the mid 1980s. The images are taken in a cold room and stored on a video disk. The calculation of the relevant parameters for snow characterization has been developed. Recently, the hardware- and software-systems have been upgraded, allowing quicker and easier analyses. Finally, a procedure designed to determine snow types from image analysis has been developed and calibrated using 65 snow samples. For that purpose, independent experts have determined the snow type (as defined by the International Classification) of each sample. The procedure has been verified using an independent set of snow images. This semi-automated determination gives 97% correct results on granular-snow types. However, it is not reliable for precipitation particles.

Most algorithms to extract dry snowpack water equivalent (SWE) from satellite passive-microwave observations are based on point measurements of SWE or extrapolation of point measurements to the 30 km footprint of the satellite observations. SWE observations on a scale comparable to the satellite observations can be obtained from airborne gamma-ray attenuation techniques from flight lines that are approximately 10 km long. During the winter of 1989, the NOAA National Operational Hydrologic Remote Sensing Center (NOHRSC) flew 92 of these night lines over a 200 × 250 km area of the Red River basin which is located in the north-central part of the United States of America. These observations provide a unique dataset of snow water-equivalent determinations on spatial scales similar to the satellite passive-microwave observations as acquired by the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave Imager (SSM/I) F-8 satellite. Land-classification determinations from the Advanced Very High Resolution Radiometer (AVHRR) show that the eastern part of the region contains a coniferous forest of varying coverage, while the remainder is farmland or prairie. SSM/I data, including observations from a no-snow case in the preceding fall, the flight-line data and the AVHRR data were all co-registered to a common 20 km grid. The resulting dataset was analyzed using linear regression, artificial intelligence and general linear models. The results showed that the passive-microwave response was similar to the response predicted by Mie scattering theory. A comparison of the three techniques found that the artificial intelligence technique and the general linear model explained significantly more of the variance in the dataset, as evidenced by R2 values of 0.97 compared to 0.88 for the linear multiple-regression analysis. Hence, a neural network approach which was continually trained on new datasets as they became available, could provide better estimates of snowpack water equivalent than algorithms based on linear-regression techniques.

In a recent paper (Sturm and others, 1995), a global seasonal snow-cover classification system was developed with each class defined by snow properties like grain-size and type. Here, characteristic bulk density vs time curves are assigned to three classes using snow-course data from Alaskan and Canadian sites. Within each class, curves have similar slopes and intercepts but between classes they are different. The relationship between slope, intercept and snow rheology has been investigated using a finite-difference model in which snow layers are assumed to behave as viscous fluids. Using observed slopes, the density-dependent compactive viscosity of each class has been determined. These are consistent with published values. Results indicate that load and load history are less important to the compaction behavior than grain and bond characteristics, snow temperature and wetness. The study suggests that differences in compaction behavior arise primarily from differences in rheology, the result of climatically controlled differences in the character of the snow. This finding explains why regional snow densities have been successfully predicted from air temperature and wind speed alone, without considering snow depth.

New categories for the climatic division of snowy areas according to their snow-cover character in mid-winter are proposed. They are a wet-snow region, a dry-snow region, an intermediate snow region and a depth-hoar region. The wet-snow region is defined as the region in which every layer of deposited snow is wet due to percolation of snowmelt water throughout the winter. In contrast, areas in which the snow cover is dry, at least in the coldest period of the winter season, are classified into two categories, that is the dry-snow region and the depth-hoar region. In the latter region, the small snow depth and low air temperature induce development of depth hoar. The intermediate snow region was introduced to indicate an intermediate character between the dry-snow and wet-snow regions. From the climatic dataset calculated by the Japanese Meteorological Agency and from snow surveys, it has been found that in snowy areas, which have a climatic monthly mean temperature in January (Tjan) higher than 0.3°C, snow would be expected to be wet throughout the winter and, in areas that have Tjan, lower than −1.1°C, to be dry at least in the coldest period. Snow covers, where Tjan is between these two values, are expected to have intermediate characters. Therefore, these temperatures are supposed to be critical values among the wet, dry and intermediate snow regions. The criterion that separates the depth-hoar region from the dry-snow areas was found to be given by a climatic mean temperature gradient. This value lies between 10 and 12°Cm−1, which is derived by dividing the absolute value of the average of the climatic monthly mean air temperature, which is always below 0°C, by the average of the monthly maximum snow depth during January and February.

Wind effects on snow distribution and increasing avalanche hazard are well known. Forecasting such phenomena is based on empirical rules and experience. For two years, the CEN has tested a new analysis program for forecasting blowing-snow events at an Alpine site. The aim is to improve avalanche-forecasting tools so as to increase the accuracy of avalanche-hazard forecasts. Our understanding of blowing-snow mechanisms began with observations and field measurements at a high altitude site during periods of strong wind. Using an empirical approach, snow-grain morphology, cohesion between snow particles and grain-size have been combined to define a function for the calculation of a “driftabilily index”. By comparing this index with the wind thresholds observed in field, a snowdrifting index has been defined. Snow profiles calculated by SAFRAN (estimation of relevant meteorological parameters for snow models) and Crocus (snowpack-evolution model) were analysed using an application called PROTÉON. Forecasts of wind velocity and snow profiles have been used to discriminate between periods with or without snow transport. For the subsequent 24 hours and for each snow layer, the value of this snowdrifting index is calculated and displayed on a graph. Validation consists of a comparison of forecasted blowing-snow periods with the field observations and measurements at a pass situated at 2700 m a.s.l. Meteorological and snow-parameter data are available at this site.

Snowdrift formation has been monitored in the eastern Spanish Pyrenees since 1992 at an experimental study plot at the La Molina ski resort between 2000 and 2400 m. Several instruments were installed to characterize the wind, quantify snow-mass flux, detect snowdnfting episodes and measure the evolution of a natural snowdrift.

This plot is situated within a natural snowdrift and is equipped with measuring instruments such as snow poles, three columns of snow collectors (prismatic boxes) facing the dominant winds and an anemographic station.

During the 4 years of data acquisition, the thickness of the snowdrift has been measured and the snow collectors checked once each week. An active interest has been taken in the relationship between snowdrift development and drifting snow during a wind episode.

The relationship observed between wind, drifting snow and the development of the snowdrift is presented The results show: (1) the amount of snow collected in the snow collectors is related to wind intensity and to the availability and the quality of drifting snow, and (2) wind intensity principally affects the relative location of maximum snowdrift growth rather than controlling the thickness of the snowdrift.

By comparing the evolution of the snowdrift with Tabler’s (1975) model, we observed that it fits with the topographic features of our study area for the three seasons shown.

During April 1995, a field and aircraft experiment was conducted in central Alaska in support of the Moderate Resolution Imaging Spectroradiometer (MODIS) snow-mapping project. The MODIS Airborne Simulator (MAS), a 50 channel spectroradiometer, was flown on board the NASA ER-2 aircraft. An objective of the mission was to determine the accuracy of mapping snow in different surface covers using an algorithm designed to map global snow cover after the launch of MODIS in 1998. The surface cover in this area of central Alaska is typically spruce, birch, aspen, mixed forest and muskeg. Integrated reflectance, Ri was calculated from the visible/near-infrared channels of the MAS sensor. The Ri was used to estimate different vegetation-cover densities because there is an inverse relationship between vegetation-cover density and albedo in snow-covered terrain. A vegetation-cover density map was constructed using MAS data acquired on 13 April 1995 over central Alaska. In the part of the scene that was mapped as having a vegetation-cover density of < 50%, the snow-mapping algorithm mapped 96.41% snow cover. These areas are generally composed of muskeg and mixed forests and include frozen lake. In the part of the scene that was estimated to have a vegetation-cover density of ≥50%, the snow-mapping algorithm mapped 71.23% snow cover. These areas are generally composed of dense coniferous or deciduous forests. Overall, the accuracy of the snow-mapping algorithm is > 87.41% for a 13 April MAS scene with a variety of surface covers (coniferous and deciduous and mixed forests, muskeg, tundra and frozen lake).

The chemical composition of snow and meltwater in the 13 km2 catchment of Scott Turnerbreen, Svalbard, was investigated during the spring and summer of 1993. This paper assesses the provenance of solute in the snowpack and its impact on runoff chemistry. Dry snow contains 420μeql-1 of solute, is slightly acidic (pH 5.4) and is dominated by Na+ and Cl-. Wet snow is more dilute (total concentration 340μeql-1), and less acidic (pH 5.9). This is consistent with the elution of ions from the snowpack by meltwater. Snowpack solute can be partitioned into the following fractions: sea-salt aerosol, acid aerosol and crustal. About 98% of snowpack solute is sea salt, yielding 22000 kg km-2a-1. The behaviour of snowpack-derived Cl- in runoff is distinctive, peaking at over 800 μeql-1 early in the melt season as runoff picks up, before declining quasi-exponentially. This represents the discharge of snowmelt concentrated by elution within the snowpack which subsequently becomes relatively dilute. A solute yield of 140 kg km-2 a-1 can be attributed to anthropogenically generated acid aerosols, representing long-range atmospheric transport of pollutants, a potential contributor to Arctic runoff acidification.

The ability of the Utah energy-balance and snowmelt model (UEB) to simulate decline in snow water equivalent (SWE) at an extreme location was assessed. Field data were collected at Paternoster Valley, Signy Island, South Orkney Islands (60°43′S) during the austral summer of 1996–97. This is the first application of UEB in a maritime Antarctic site. UEB is a physically based snow melt model using a lumped snow-pack representation with primary state variables SWE and snow pack-energy content (U). Meteorological inputs are air temperature, wind speed, humidity, precipitation and total incoming solar and longwave radiation. The Paternoster Valley catchment was subdivided into eight non-contiguous terrain classes for sampling and modelling using a geographical information system (GIS). Simulations of SWE in each of these classes were compared พ with field observations. It is shown that initial U and snow-surface thermal conductance (Ks) affect model simulations. Good approximations of SWE depletion are obtained using measured incoming solar radiation to drive the model but there are shortcomings in the characterization of long wave radiation and sensible-heat fluxes.

This paper presents a modelling study of the influence of suspended snow on turbulence in the atmospheric surface layer. Turbulence is diminished in drifting and blowing snow, since part of the turbulent energy is used to keep the particles in suspension. This decrease in turbulence directly affects the vertical turbulent fluxes of momentum and snow particles (and other scalars), and can effectively be simulated by introducing an appropriate Richardson number to account for the stability effects of the stably stratified air-snow mixture. We use a one-dimensional model of the atmospheric surface layer in which the Reynolds stress and turbulent suspended snow flux are parameterized in terms of their mean vertical gradients (first-order closure). The model calculates steady-state vertical profiles of mean wind speed, suspended snow mass in 16 size classes and stability parameters. Using the model, the influence of snowdrifting on the wind-speed profile is quantified for various values of the initial friction Velocity (which determines the steepness of the initial wind-speed profile). It will be demonstrated why the roughness length appears to increase when snowdrifting occurs. Finally, we present a parameterization of the effects of snowdrifting on atmospheric stability which can be used in data analyses as a first-order approximation.

A physically based numerical model of drifting and blowing snow in three-dimensional terrain is developed. The model includes snow transport by saltation and suspension. As an example, a numerical simulation for an Alpine ridge is presented and compared with field measurements.

Measurements of sensible- and latent-heat fluxes under stable conditions are rare. In order to obtain indirect measurements of turbulent fluxes, meteorological data measured at the Col de Porte laboratory (1320 m a.s.l, France) under very stable conditions (cold, clear night with low wind) are used. The radiative fluxes are measured, the conduction within the snowpack is calculated using the snow model Crocus and the turbulent fluxes are determined as a residual term of the surface-energy balance equation. These data were used to fit a new parameterization of the turbulent fluxes for the snow model. The turbulent fluxes are increased as compared to the theory. Crocus was also applied to the data from the LEADEX92 experiment and the turbulent fluxes calculated by the model were compared to the fluxes measured using sonic anemometers/thermometers on the site.

First, wind-tunnel experiments were carried out to measure the trajectories of saltating snow particles with varying friction velocity. Trajectories of saltating particles were recorded by a video system with a laser sheet and trajectory statistics, such as ejection and impact velocities and angles, were obtained for each particle. Parabolic trajectories are considerably elongated with an increase in the friction velocity; impact angle was approximately the same but ejection angle decreased with increasing friction velocity. Furthermore, it should be noted that the gradient of flux decay with height decreased with friction velocity. In the experiments, a snow-particle counter, which can sense not only the number of particles but also their diameters, was introduced. The flux distribution and the transport rate obtained as a function of the particle size gave a new insight into the relationship with the friction velocity.

Trajectories of saltating grains were computed, using the measurements of the initial ejection velocities, angles and the mean velocity profile of the air. The results agreed reasonably with our measurements. Using the measured probability distribution of the ejection velocities, an ensemble of trajectories was computed and thence the vertical profiles of stream-wise fluxes. The exponential decay of the flux on height was obtained in all cases and it supports the basic validity of the model, although agreement is less than expected.

Earlier works on numerical modelling are analysed. Anderson and Haff (1991) proposed a model using the “splash” function which was defined for cohesionless sand. The Uematsu and others (1989, 1991) and Liston and others (1993,1994) approaches are based on fluid-mechanics conservation laws where the snow is transported and diffused by the air flow. These models consider the saltation layer as a boundary condition.

For the flow, and for the suspension, we adopt the same model as that of Uematsu and Liston. For mass exchange between the flow and snow surface, we have developed an erosion–deposition model where mass exchange is defined in relation to flow turbulence, threshold-friction velocity and snow concentration. Our snow-erosion model was calibrated using Takeuchi's(1980) field measurements. The deposition model was tested by comparing numerical results with wind-tunnel ones, for sawdust-accumulation windward and leeward of a solid snow fence with a bottom gap. The numerical results obtained are close to the experimental results. The main results of the various sensitivity experiments are: the leeward accumulation is very sensitive to the ratio (u*/u*t) (it appears for (u*/u*t) close to 1 and disappears for (u*/u*t) > 1.2), the global accumulation produced by the fence increases as (u*/u*t) decreases and the back reaction of particles on turbulence extends slightly the windward accumulation.

This paper describes the early stages in the development and testing of a spatio-temporal rule-based model of snow transport by wind over complex terrain. The model uses a cell-based approach and takes into account a range of meteorological, snow-pack and topographic variables. Simple rules are used to determine movement of material from cell-to-cell within the model.

A geographic information system is utilized in modelling wind vectors and the influence of topography upon those vectors, and creating the resulting pattern of snow accumulation at a resolution of 50 m. These patterns of accumulation have been compared with empirical work in the field and, although field data were limited, a good qualitative correlation has been found; for example, modelled accumulations correlate with known areas of accumulation such as gullies and hollows.

A preliminary simulation of blowing snow over the Antarctic continent made with a mesoscale atmospheric model is presented. Sensitivity experiments show that the increase of surface friction arising in conjunction with blowing snow has a relatively more important impact on the dynamics of strong katabatic winds than previously supposed. Sublimation in blowing snow over the Antarctic continent also contributes to the global sea-level budget. It is found that this contribution is of the same order of magnitude as the estimated present sea-level rise.

Determining the electrostatic force acting on saltating snow particles requires knowledge of the electric field in the saltation region and the charge on the particle. To date, measurements of average charge-to-mass ratios for blowing-snow samples have been made but Schmidt and Schmidt (1993) have indicated these may underestimate charge-to-mass ratios for individual particles due to a mixture of positive and negative charge in the samples. This paper reports on measurements of charge-to-mass ratio for individual blowing-snow particles during a moderate blowing-snow storm in southwestern Wyoming. We measured charge-to-mass ratios as large as -208 μC, kg-1, significantly higher than the -10μC kg -1 measured by Latham and Montagne (1970) and -50 μC kg-1 measured by Wishart (1970). This large charge-to-mass ratio was accompanied by a distribution of positive and negative charge on the particles, supporting the conclusions of Schmidt and Schmidt (1993).

This paper deals with the influence of the vertical and horizontal variation of Aeolian snow flux on estimations of snow-storm duration and on snowdrift patterns simulated in a wind tunnel.

First of all, we shall review recent developments, including our own simulations, concerning the horizontal increase of mass flux until drifting-snow saturation has been reached.

Next, after a critical examination of various snowdrift-modelling criteria, we present the results of an experiment undertaken in our wind tunnel: We studied cross-section drill-area changes behind a small-scale snow fence as a function of time for different fetches but for the same mean wind speed at the beginning of the testing section. For each fetch, we carried out vertical profiles of sand particles using image processing and we studied the geometrical parameters of the drift. We conclude dial changes in the cross-section drift area depend on the ablation length and if particle saturation is not reached, the use of a storm-duration scale leads to Overevaluation of prototype storm duration. Moreover, we point out that drift pattern vary as a function of the concentration profiles.