Snow Specific Surface Area Research Articles

In situ continuous visible and near-infrared spectroscopy of an alpine snowpack

Abstract. Snow spectral albedo in the visible/near-infrared range has been continuously measured during a winter season at Col de Porte alpine site (French Alps; 45.30° N, 5.77° E; 1325 m a.s.l.). The evolution of such alpine snowpack is complex due to intensive precipitation, rapid melt events and Saharan dust deposition outbreaks. This study highlights that the resulting intricate variations of spectral albedo can be successfully explained by variations of the following snow surface variables: specific surface area (SSA) of snow, effective light-absorbing impurities content, presence of liquid water and slope. The methodology developed in this study disentangles the effect of these variables on snow spectral albedo. The presence of liquid water at the snow surface results in a spectral shift of the albedo from which melt events can be identified with an occurrence of false detection rate lower than 3.5 %. Snow SSA mostly impacts spectral albedo in the near-infrared range. Impurity deposition mostly impacts the albedo in the visible range but this impact is very dependent on snow SSA and surface slope. Our work thus demonstrates that the SSA estimation from spectral albedo is affected by large uncertainties for a tilted snow surface and medium to high impurity contents and that the estimation of impurity content is also affected by large uncertainties, especially for low values below 50 ng g−1 black carbon equivalent. The proposed methodology opens routes for retrieval of SSA, impurity content, melt events and surface slope from spectral albedo. However, an exhaustive accuracy assessment of the snow black properties retrieval would require more independent in situ measurements and is beyond the scope of the present study. This time series of snow spectral albedo nevertheless already provides a new insight into our understanding of the evolution of snow surface properties.

Comparison of commonly-used microwave radiative transfer models for snow remote sensing

This paper reviews four commonly-used microwave radiative transfer models that take different electromagnetic approaches to simulate snow brightness temperature (TB): the Dense Media Radiative Transfer - Multi-Layer model (DMRT-ML), the Dense Media Radiative Transfer - Quasi-Crystalline Approximation Mie scattering of Sticky spheres (DMRT-QMS), the Helsinki University of Technology n-Layers model (HUT-nlayers) and the Microwave Emission Model of Layered Snowpacks (MEMLS). Using the same extensively measured physical snowpack properties, we compared the simulated TB at 11, 19 and 37GHz from these four models. The analysis focuses on the impact of using different types of measured snow microstructure metrics in the simulations. In addition to density, snow microstructure is defined for each snow layer by grain optical diameter (Do) and stickiness for DMRT-ML and DMRT-QMS, mean grain geometrical maximum extent (Dmax) for HUT n-layers and the exponential correlation length for MEMLS. These metrics were derived from either in-situ measurements of snow specific surface area (SSA) or macrophotos of grain sizes (Dmax), assuming non-sticky spheres for the DMRT models. Simulated TB sensitivity analysis using the same inputs shows relatively consistent TB behavior as a function of Do and density variations for the vertical polarization (maximum deviation of 18K and 27K, respectively), while some divergences appear in simulated variations for the polarization ratio (PR). Comparisons with ground-based radiometric measurements show that the simulations based on snow SSA measurements have to be scaled with a model-specific factor of Do in order to minimize the root mean square error (RMSE) between measured and simulated TB. Results using in-situ grain size measurements (SSA or Dmax, depending on the model) give a mean TB RMSE (19 and 37GHz) of the order of 16–26K, which is similar for all models when the snow microstructure metrics are scaled. However, the MEMLS model converges to better results when driven by the correlation length estimated from in-situ SSA measurements rather than Dmax measurements. On a practical level, this paper shows that the SSA parameter, a snow property that is easy to retrieve in-situ, appears to be the most relevant parameter for characterizing snow microstructure, despite the need for a scaling factor.

Development and calibration of an automatic spectral albedometer to estimate near-surface snow SSA time series

Abstract. Spectral albedo of the snow surface in the visible/near-infrared range has been measured for 3 years by an automatic spectral radiometer installed at Dome C (75° S, 123° E) in Antarctica in order to retrieve the specific surface area (SSA) of superficial snow. This study focuses on the uncertainties of the SSA retrieval due to instrumental and data processing limitations. We find that when the solar zenith angle is high, the main source of uncertainties is the imperfect angular response of the light collectors. This imperfection introduces a small spurious wavelength-dependent trend in the albedo spectra which greatly affects the SSA retrieval. By modeling this effect, we show that for typical snow and illumination conditions encountered at Dome C, retrieving SSA with an accuracy better than 15 % (our target) requires the difference of response between 400 and 1100 nm to not exceed 2 %. Such a small difference can be achieved only by (i) a careful design of the collectors, (ii) an ad hoc correction of the spectra using the actual measured angular response of the collectors, and (iii) for solar zenith angles less than 75°. The 3-year time series of retrieved SSA features a 3-fold decrease every summer which is significantly larger than the estimated uncertainties. This highlights the high dynamics of near-surface SSA at Dome C.

Summertime evolution of snow specific surface area close to the surface on the Antarctic Plateau

Abstract. On the Antarctic Plateau, snow specific surface area (SSA) close to the surface shows complex variations at daily to seasonal scales which affect the surface albedo and in turn the surface energy budget of the ice sheet. While snow metamorphism, precipitation and strong wind events are known to drive SSA variations, usually in opposite ways, their relative contributions remain unclear. Here, a comprehensive set of SSA observations at Dome C is analysed with respect to meteorological conditions to assess the respective roles of these factors. The results show an average 2-to-3-fold SSA decrease from October to February in the topmost 10 cm in response to the increase of air temperature and absorption of solar radiation in the snowpack during spring and summer. Surface SSA is also characterized by significant daily to weekly variations due to the deposition of small crystals with SSA up to 100 m2 kg−1 onto the surface during snowfall and blowing snow events. To complement these field observations, the detailed snowpack model Crocus is used to simulate SSA, with the intent to further investigate the previously found correlation between interannual variability of summer SSA decrease and summer precipitation amount. To this end, some Crocus parameterizations have been adapted to Dome C conditions, and the model was forced by ERA-Interim reanalysis. It successfully matches the observations at daily to seasonal timescales, except for the few cases when snowfalls are not captured by the reanalysis. On the contrary, the interannual variability of summer SSA decrease is poorly simulated when compared to 14 years of microwave satellite data sensitive to the near-surface SSA. A simulation with disabled summer precipitation confirms the weak influence in the model of the precipitation on metamorphism, with only 6 % enhancement. However, we found that disabling strong wind events in the model is sufficient to reconciliate the simulations with the observations. This suggests that Crocus reproduces well the contributions of metamorphism and precipitation on surface SSA, but snow compaction by the wind might be overestimated in the model.

Evolution of the specific surface area of snow during high‐temperature gradient metamorphism

AbstractThe structural evolution of low‐density snow under a high‐temperature gradient over a short period usually takes place in the surface layers on clear, cold nights. In this paper, X‐ray computed microtomography (microCT) was combined with numerical simulations to investigate the temperature gradient metamorphism (TGM) on different types of snow. Precipitation particles (PP), small rounded particles (RGsr), and large rounded particles (RGlr) were each observed in high‐temperature gradients (100–500 K m−1) at a mean temperature of −4°C. The specific surface area (SSA) was used to characterize the TGM, which were influenced by both the magnitude of the temperature gradient and the initial snow structures. PP samples experienced a logarithmic decrease of SSA with time, and the depth hoar structures created under high TGM (500 K m−1) have higher SSA compared to those under lower TGM. Unlike previous observations, for initial rounded and connected structures, like RGlr samples, the SSA increased during TGM. Simulated normal vapor flux distributions for different snow types were used to help understand the structural evolution under TGM. Understanding the SSA increase is important in order to predict the enhanced uptake of chemical species by snow or increase in snow albedo.

The growth of sublimation crystals and surface hoar on the Antarctic plateau

Abstract. On the Antarctic plateau, precipitation quantities are so low that the surface mass budget is for an important part determined by exchanges of water vapor between the snow surface and the atmosphere surface. At Dome C (75° S, 123° E), we have frequently observed the growth of crystals on the snow surface under calm sunny weather. Here we present the time variations of specific surface area (SSA) and density of these crystals. Using the detailed snow model Crocus, we conclude that the formation of these crystals was very likely due to the nighttime formation of surface hoar crystals and to the daytime formation of sublimation crystals. These latter crystals form by processes similar to those involved in the formation of frost flowers on young sea ice. The formation of these crystals impacts the albedo, mass and energy budget of the Antarctic plateau. In particular, the SSA variations of the surface layer can induce an instantaneous forcing at the snow surface up to −10 W m−2 at noon, resulting in a surface temperature drop of 0.45 K. This result confirms that snow SSA is a crucial variable to consider in the energy budget and climate of snow-covered surfaces.

Measuring the specific surface area of wet snow using 1310 nm reflectance

Abstract. The specific surface area (SSA) of snow can be used as an objective measurement of grain size and is therefore a central variable to describe snow physical properties such as albedo. Snow SSA can now be easily measured in the field using optical methods based on infrared reflectance. However, existing optical methods have only been validated for dry snow. Here we test the possibility to use the DUFISSS instrument, based on the measurement of the 1310 nm reflectance of snow with an integrating sphere, to measure the SSA of wet snow. We perform cold room experiments where we measure the SSA of a wet snow sample, freeze it and measure it again, to quantify the difference in reflectance between frozen and wet snow. We study snow samples in the SSA range 12–37 m2 kg−1 and in the mass liquid water content (LWC) range 5–32%. We conclude that the SSA of wet snow can be obtained from the measurement of its 1310 nm reflectance using three simple steps. In most cases, the SSA thus obtained is less than 10 {%} different from the value that would have been obtained if the sample had been considered dry, so that the three simple steps constitute a minor correction. We also run two optical models to interpret the results, but no model reproduces correctly the water–ice distribution in wet snow, so that their predictions of wet snow reflectance are imperfect. The correction on the determination of wet snow SSA using the DUFISSS instrument gives an overall uncertainty better than 11%, even if the LWC is unknown. If SSA is expressed as a surface to volume ratio (e.g., in mm−1), the uncertainty is then 13% because of additional uncertainties in the determination of the volume of ice and water when the LWC is unknown.

Persistent organic pollutant accumulation in seasonal snow along an altitudinal gradient in the Tyrolean Alps.

The snow capacity for storage of a large number of pollutants such as polybromodiphenyl ethers (PBDE), including BDE-209, polycyclic aromatic hydrocarbons (PAHs), polychlorobiphenyls (PCBs), hexachlorocyclohexanes (HCHs; α- and γ-isomers), endosulfans (α- and β-isomers and the sulphate residue) and hexachlorobenzene (HCB), in a steep altitudinal gradient (1,101-2,500 m above sea level (asl); maximum planar distance 16 km) in a typical European mountain system, the Tyrolean Alps (Austria), was studied here for the first time. Snow samples representing the whole snowpack accumulated at the end of the cold season were collected in all cases. The snow specific surface area (SSA) of these samples, 140-260 cm(2) g(-1), was characteristic of aged snow with low retention capacity. PAHs were the pollutant group in highest concentrations (500-8,400 pg L(-1)). PCBs and PBDEs were found in concentrations of 460-900 and 8.5-290 pg L(-1), respectively. From the fourteen investigated BDE congeners, only BDE-47, BDE-99, BDE-100 and BDE-209 were found above the detection limit, which is consistent with the results found in the only previous study in the Tatra Mountains (Slovakia) which also involved a steep gradient (1,683-2,634 m asl; maximum planar distance 5 km; Arellano et al. 2011) and confirm the capacity of these low-volatile compounds for long-range transport from distant sources. HCB was found in a concentration range of 34-55 pg L(-1). Snow deposition fluxes of PCB-118, PCB-153, γ-HCH, α-endosulfan and BDE-47 showed statistically significant correlations with altitude, involving higher values at higher elevation. This trend may reflect cold trapping effects in view of the snow particle contents and SSA values. However, these gradients were only significant for this limited number of compounds within each pollutant group which may be explained by differences in physical-chemical properties of the compounds and the limited capacity of the aged snow for organic pollutant retention. In some other cases, for example benzo[a]pyrene, the observed vertical gradients may reflect higher preservations at lower temperatures.

Effects of temperature and grain type on time variation of snow specific surface area

The specific surface area (SSA) of snow is of particular interest to researchers because SSA is strongly related to snow albedo and is a comparatively better indicator of snow’s complexity than grain size. The time variation of SSA for fresh snow samples was observed in the laboratory under isothermal conditions at 226 K and 254 K using the gas adsorption method and Brunauer-Emmett-Teller theory. The SSA of the snow samples decreased with time under isothermal metamorphism. The decrease in SSA was fitted with the logarithmic equation proposed by Legagneux et al. (2003), and adjustable parameters were obtained. The rate of decrease in SSA depended on the shape of the initial snow type and temperature. Dendritic snow samples exhibited large initial SSAs, and their SSAs decreased faster compared with those of fragmented (collected from drifting snow) and plate-like precipitation particles with relatively small initial SSAs. The rate of decrease in SSA was lower at 226 K than that at 254 K.

Application of snow specific surface area measurement using an optical method based on near-infrared reflectance around 900-nm wavelength to wet snow zones in Japan

Application of snow specific surface area measurement using an optical method based on near-infrared reflectance around 900-nm wavelength to wet snow zones in Japan

Glaciological observations in 2012 and 2013 at SIGMA-A site, Northwest Greenland（北西グリーンランド , SIGMA-Aサイトにおける 2012年, 2013年の雪氷調査）

Glaciological observations were conducted in 2012 and 2013 at the SIGMA‑A site on the northwest Greenland ice sheet (78°03�06�N 67°37�42�W 1490 m a.s.l. as part of the Snow Impurity and Glacial Microbe effects on abrupt warming in the Arctic (SIGMA project. The meteorological conditions during the two observations were quite different. The meteorological condition during the 2012 observation period was warm and heavy rainfall occurred during the observation period thus the snow was very wet. In contrast the meteorological condition during the observation period in 2013 was cold with a blowing snow event thus the snow was quite dry. The glaciological observations in 2012 consisted of 1 snow‑stake measurements 2 snow pit observations 3 grain size observations for validation of satellite‑derived snow products 4 snow specific surface area measurements using a near‑infrared camera 5 snow sampling for chemical analyses and 6 drilling of firn cores with a hand auger. The glaciological observations in 2013 consisted of 1 snow‑stake measurements 2 snow pit observations and 3 snow sampling for chemical analyses.

Brightness Temperature Simulations of the Canadian Seasonal Snowpack Driven by Measurements of the Snow Specific Surface Area

Snow grain size is the snowpack parameter that most affects the microwave snow emission. The specific surface area (SSA) of snow is a metric that allows rapid and reproducible field measurements and that well represents the grain size. However, this metric cannot be used directly in microwave snow emission models (MSEMs). The aim of this paper is to evaluate the suitability and the adaptations required for using the SSA in two MSEMs, i.e., the Dense Media Radiative Theory-Multilayer model (DMRT-ML) and the Helsinki University of Technology model (HUT n-layer), based on in situ radiometric measurements. Measurements of the SSA, using snow reflectance in the short-wave infrared, were taken at 20 snowpits in various environments (e.g., grass, tundra, and dry fen). The results show that both models required a scaling factor for the SSA values to minimize the root-mean-square error between the measured and simulated brightness temperatures. For DMRT-ML, the need for a scaling factor is likely due to the oversimplified representation of snow as spheres of ice with a uniform radius. We hypothesize that the need for a scaling factor is related to the grain size distribution of snow and the stickiness between grains. For HUT n-layer, using the SSA underestimates the attenuation by snow, particularly for snowpacks with a significant amount of depth hoar. This paper provides a reliable description of the grain size for DMRT-ML, which is of particular interest for the assimilation of satellite passive microwave data in snow models.

Simulating polarized light scattering in terrestrial snow based on bicontinuous random medium and Monte Carlo ray tracing

To date, the light scattering models of snow consider very little about the real snow microstructures. The ideal spherical or other single shaped particle assumptions in previous snow light scattering models can cause error in light scattering modeling of snow and further cause errors in remote sensing inversion algorithms. This paper tries to build up a snow polarized reflectance model based on bicontinuous medium, with which the real snow microstructure is considered. The accurate specific surface area of bicontinuous medium can be analytically derived. The polarized Monte Carlo ray tracing technique is applied to the computer generated bicontinuous medium. With proper algorithms, the snow surface albedo, bidirectional reflectance distribution function (BRDF) and polarized BRDF can be simulated. The validation of model predicted spectral albedo and bidirectional reflectance factor (BRF) using experiment data shows good results. The relationship between snow surface albedo and snow specific surface area (SSA) were predicted, and this relationship can be used for future improvement of snow specific surface area (SSA) inversion algorithms. The model predicted polarized reflectance is validated and proved accurate, which can be further applied in polarized remote sensing.

Snow spectral albedo at Summit, Greenland: measurements and numerical simulations based on physical and chemical properties of the snowpack

Abstract. The broadband albedo of surface snow is determined both by the near-surface profile of the physical and chemical properties of the snowpack and by the spectral and angular characteristics of the incident solar radiation. Simultaneous measurements of the physical and chemical properties of snow were carried out at Summit Camp, Greenland (72°36´ N, 38°25´ W, 3210 m a.s.l.) in May and June 2011, along with spectral albedo measurements. One of the main objectives of the field campaign was to test our ability to predict snow spectral albedo by comparing the measured albedo to the albedo calculated with a radiative transfer model, using measured snow physical and chemical properties. To achieve this goal, we made daily measurements of the snow spectral albedo in the range 350–2200 nm and recorded snow stratigraphic information down to roughly 80 cm. The snow specific surface area (SSA) was measured using the DUFISSS instrument (DUal Frequency Integrating Sphere for Snow SSA measurement, Gallet et al., 2009). Samples were also collected for chemical analyses including black carbon (BC) and dust, to evaluate the impact of light absorbing particulate matter in snow. This is one of the most comprehensive albedo-related data sets combining chemical analysis, snow physical properties and spectral albedo measurements obtained in a polar environment. The surface albedo was calculated from density, SSA, BC and dust profiles using the DISORT model (DIScrete Ordinate Radiative Transfer, Stamnes et al., 1988) and compared to the measured values. Results indicate that the energy absorbed by the snowpack through the whole spectrum considered can be inferred within 1.10%. This accuracy is only slightly better than that which can be obtained considering pure snow, meaning that the impact of impurities on the snow albedo is small at Summit. In the near infrared, minor deviations in albedo up to 0.014 can be due to the accuracy of radiation and SSA measurements and to the surface roughness, whereas deviations up to 0.05 can be explained by the spatial heterogeneity of the snowpack at small scales, the assumption of spherical snow grains made for DISORT simulations and the vertical resolution of measurements of surface layer physical properties. At 1430 and around 1800 nm the discrepancies are larger and independent of the snow properties; we propose that they are due to errors in the ice refractive index at these wavelengths. This work contributes to the development of physically based albedo schemes in detailed snowpack models, and to the improvement of retrieval algorithms for estimating snow properties from remote sensing data.

Snow specific surface area simulation using the one-layer snow model in the Canadian LAnd Surface Scheme (CLASS)

Abstract. Snow grain size is a key parameter for modeling microwave snow emission properties and the surface energy balance because of its influence on the snow albedo, thermal conductivity and diffusivity. A model of the specific surface area (SSA) of snow was implemented in the one-layer snow model in the Canadian LAnd Surface Scheme (CLASS) version 3.4. This offline multilayer model (CLASS-SSA) simulates the decrease of SSA based on snow age, snow temperature and the temperature gradient under dry snow conditions, while it considers the liquid water content of the snowpack for wet snow metamorphism. We compare the model with ground-based measurements from several sites (alpine, arctic and subarctic) with different types of snow. The model provides simulated SSA in good agreement with measurements with an overall point-to-point comparison RMSE of 8.0 m2 kg–1, and a root mean square error (RMSE) of 5.1 m2 kg–1 for the snowpack average SSA. The model, however, is limited under wet conditions due to the single-layer nature of the CLASS model, leading to a single liquid water content value for the whole snowpack. The SSA simulations are of great interest for satellite passive microwave brightness temperature assimilations, snow mass balance retrievals and surface energy balance calculations with associated climate feedbacks.

Intercomparison of retrieval algorithms for the specific surface area of snow from near-infrared satellite data in mountainous terrain, and comparison with the output of a semi-distributed snowpack model

Abstract. This study compares different methods to retrieve the specific surface area (SSA) of snow from satellite radiance measurements in mountainous terrain. It aims at addressing the effect on the retrieval of topographic corrections of reflectance, namely slope and aspect of terrain, multiple reflections on neighbouring slopes and accounting (or not) for the anisotropy of snow reflectance. Using MODerate resolution Imaging Spectrometer (MODIS) data for six different clear sky scenes spanning a wide range of snow conditions during the winter season 2008–2009 over a domain of 46 × 50 km in the French Alps, we compared SSA retrievals with and without topographic correction, with a spherical or non-spherical snow reflectance model and, in spherical case, with or without anisotropy corrections. The retrieved SSA values were compared to field measurements and to the results of the detailed snowpack model Crocus, fed by driving data from the SAFRAN meteorological analysis. It was found that the difference in terms of surface SSA between retrieved values and SAFRAN-Crocus output was minimal when the topographic correction was taken into account, when using a retrieval method assuming disconnected spherical snow grains. In this case, the root mean square deviation was 9.4 m2 kg−1 and the mean difference was 0.1 m2 kg−1, based on 3170 pairs of observation and simulated values. The added-value of the anisotropy correction was not significant in our case, which may be explained by the presence of mixed pixels and surface roughness. MODIS retrieved data show SSA variations with elevation and aspect which are physically consistent and in good agreement with SAFRAN-Crocus outputs. The variability of the MODIS retrieved SSA within the topographic classes of the model was found to be relatively small (3.9 m2 kg−1). This indicates that semi-distributed snowpack simulations in mountainous terrain with a sufficiently large number of classes provides a representation of the snowpack variability consistent with the scale of MODIS 500 m pixels.

3-D image-based numerical computations of snow permeability: links to specific surface area, density, and microstructural anisotropy

Abstract. We used three-dimensional (3-D) images of snow microstructure to carry out numerical estimations of the full tensor of the intrinsic permeability of snow (K). This study was performed on 35 snow samples, spanning a wide range of seasonal snow types. For several snow samples, a significant anisotropy of permeability was detected and is consistent with that observed for the effective thermal conductivity obtained from the same samples. The anisotropy coefficient, defined as the ratio of the vertical over the horizontal components of K, ranges from 0.74 for a sample of decomposing precipitation particles collected in the field to 1.66 for a depth hoar specimen. Because the permeability is related to a characteristic length, we introduced a dimensionless tensor K*=K/res2, where the equivalent sphere radius of ice grains (res) is computed from the specific surface area of snow (SSA) and the ice density (ρi) as follows: res=3/(SSA×ρi. We define K and K* as the average of the diagonal components of K and K*, respectively. The 35 values of K* were fitted to snow density (ρs) and provide the following regression: K = (3.0 ± 0.3) res2 exp((−0.0130 ± 0.0003)ρs). We noted that the anisotropy of permeability does not affect significantly the proposed equation. This regression curve was applied to several independent datasets from the literature and compared to other existing regression curves or analytical models. The results show that it is probably the best currently available simple relationship linking the average value of permeability, K, to snow density and specific surface area.

Measurements and modeling of the vertical profile of specific surface area of an alpine snowpack

The specific surface area of snow (SSA) is a useful variable to describe the physical and chemical properties of snow, including a quantitative link to snow metamorphism and the optical properties of snow. Here we present a series of 16 weekly profiles of snow physical properties including SSA measured using the DUFISSS instrument spanning the period from January to April 2010 at the Col de Porte field site in the French Alps near Grenoble. Measured SSA values for dry snow ranged between ca. 5 and 80m2kg−1, and generally decreased over time in a given snow layer. Wet snow conditions encountered towards the end of the snow season show SSA values between 3 and 10m2kg−1. This unique dataset is compared with simulations carried out using the Crocus snowpack model, using two parameterizations of snow SSA: one simply derived from the internal computation of the optical radius in Crocus, and the other one determined from density and snow type. Both parameterizations perform rather satisfactorily qualitatively and quantitatively, compared to the performance in terms of snow density profile. Ample room for improvement exists, in particular through the implementation of SSA as a fully fledged prognostic variable in Crocus, which is currently in progress.

New shortwave infrared albedo measurements for snow specific surface area retrieval

AbstractSnow grain-size characterization, its vertical and temporal evolution is a key parameter for the improvement and validation of snow and radiative transfer models (optical and microwave) as well as for remote-sensing retrieval methods. We describe two optical methods, one active and one passive shortwave infrared, for field determination of the specific surface area (SSA) of snow grains. We present a new shortwave infrared (SWIR) camera approach. This new method is compared with a SWIR laser- based system measuring snow albedo with an integrating sphere (InfraRed Integrating Sphere (IRIS)). Good accuracy (10%) and reproducibility in SSA measurements are obtained using the IRIS system on snow samples having densities greater than 200 kg m-3, validated against X-ray microtomography measurements. The SWIRcam approach shows improved sensitivity to snow SSA when compared to a near-infrared camera, giving a better contrast of the snow stratigraphy in a snow pit.

Determination of the Specific Surface Area of Snow Using Ozonation of 1,1-Diphenylethylene

We measured the kinetics of ozonation reaction of 1,1-diphenylethylene (DPE) in artificial snow, produced by shock freezing of DPE aqueous solutions sprayed into liquid nitrogen. It was demonstrated that most of the reactant molecules are in direct (productive) contact with gaseous ozone, thus the technique produces snow with organic molecules largely ejected to the surface of snow grains. The kinetic data were used to evaluate the snow specific surface area (∼70 cm(2) g(-1)). This number is a measure of the availability of the molecules on the surface for chemical reaction with gaseous species. The experimental results were consistent with the Langmuir-Hinshelwood type reaction mechanism. DPE represents environmentally relevant compounds such as alkenes which can react with atmospheric ozone, and are relatively abundant in natural snow. For typical atmospheric ozone concentrations in polar areas (20 ppbv), we estimated that half-life of DPE on the surface of snow grains is ∼5 days at submonolayer coverages and -15 °C.