This work introduces the S2M (SAFRAN–SURFEX/ISBA–Crocus–MEPRA) meteorological and snow cover reanalysis in the French Alps, Pyrenees and Corsica, spanning the time period from 1958 to 2021. The simulations are made over elementary areas, referred to as massifs, designed to represent the main drivers of the spatial variability observed in mountain ranges (elevation, slope and aspect). The meteorological reanalysis is performed by the SAFRAN system, which combines information from numerical weather prediction models (ERA-40 reanalysis from 1958 to 2002, ARPEGE from 2002 to 2021) and the best possible set of available in situ meteorological observations. SAFRAN outputs are used to drive the Crocus detailed snow cover model, which is part of the land surface scheme SURFEX/ISBA. This model chain provides simulations of the evolution of the snow cover, underlying ground and the associated avalanche hazard using the MEPRA model. This contribution describes and discusses the main climatological characteristics (climatology, variability and trends) and the main limitations of this dataset. We provide a short overview of the scientific applications using this reanalysis in various scientific fields related to meteorological conditions and the snow cover in mountain areas. An evaluation of the skill of S2M is also displayed, in particular through comparison to 665 independent in situ snow depth observations. Further, we describe the technical handling of this open-access dataset, available at https://doi.org/10.25326/37#v2020.2. The S2M data are provided by Météo-France – CNRS, CNRM, Centre d'Études de la Neige, through AERIS (Vernay et al., 2022).

X-ray microtomography has become an essential tool for investigating the mechanical and physical properties of snow, which are tied to its microstructure. To allow a quantitative characterization of the microstructure, the grayscale X-ray attenuation coefficient image has to be segmented into a binary ice/pore image. This step, called binary segmentation, is crucial and affects all subsequent analysis and modeling. Common segmentation methods are based on thresholding. In practice, these methods present some drawbacks and often require time-consuming manual post-processing. Here we present a binary segmentation algorithm based on the minimization of a segmentation energy. This energy is composed of a data fidelity term and a regularization term penalizing large interface area, which is of particular interest for snow where sintering naturally tends to reduce the surface energy. The accuracy of the method is demonstrated on a synthetic image. The method is then successfully applied on microtomographic images of snow and compared to the threshold-based segmentation. The main advantage of the presented approach is that it benefits from local spatial information. Moreover, the effective resolution of the segmented image is clearly defined and can be chosen a priori.

Rapid and large deformations of snow are mainly controlled by grain rearrangements, which occur through the failure of cohesive bonds and the creation of new contacts. We exploit a granular description of snow to develop a discrete element model based on the full 3-D microstructure captured by microtomography. The model assumes that snow is composed of rigid grains interacting through localized contacts accounting for cohesion and friction. The geometry of the grains and of the intergranular bonding system are explicitly defined from microtomographic data using geometrical criteria based on curvature and contiguity. Single grains are represented as rigid clumps of spheres. The model is applied to different snow samples subjected to confined compression tests. A detailed sensitivity analysis shows that artifacts introduced by the modeling approach and the influence of numerical parameters are limited compared to variations due to the geometry of the microstructure. The model shows that the compression behavior of snow is mainly controlled by the density of the samples, but that deviations from a pure density parameterization are not insignificant during the first phase of deformation. In particular, the model correctly predicts that, for a given density, faceted crystals are less resistant to compression than rounded grains or decomposed snow. For larger compression strains, no clear differences between snow types are observed.

Predicting avalanche activity from meteorological and snow cover simulations is critical in mountainous areas to support operational forecasting. Several numerical and statistical methods have tried to address this issue. However, it remains unclear how combining snow physics, mechanical analysis of snow profiles and observed avalanche data improves avalanche activity prediction. This study combines extensive snow cover and snow stability simulations with observed avalanche occurrences within a random forest approach to predict avalanche situations at a spatial resolution corresponding to elevations and aspects of avalanche paths in a given mountain range. We develop a rigorous leave-one-out evaluation procedure including an independent evaluation set, confusion matrices and receiver operating characteristic curves. In a region of the French Alps (Haute-Maurienne) and over the period 1960–2018, we show the added value within the machine learning model of considering advanced snow cover modelling and mechanical stability indices instead of using only simple meteorological and bulk information. Specifically, using mechanically based stability indices and their time derivatives in addition to simple snow and meteorological variables increases the probability of avalanche situation detection from around 65 % to 76 %. However, due to the scarcity of avalanche events and the possible misclassification of non-avalanche situations in the training dataset, the predicted avalanche situations that are really observed remains low, around 3.3 %. These scores illustrate the difficulty of predicting avalanche occurrence with a high spatio-temporal resolution, even with the current data and modelling tools. Yet, our study opens perspectives to improve modelling tools supporting operational avalanche forecasting.

Microtomography can measure the X-ray attenuation coefficient in a 3-D volume of snow with a spatial resolution of a few microns. In order to extract quantitative characteristics of the microstructure, such as the specific surface area (SSA), from these data, the greyscale image first needs to be segmented into a binary image of ice and air. Different numerical algorithms can then be used to compute the surface area of the binary image. In this paper, we report on the effect of commonly used segmentation and surface area computation techniques on the evaluation of density and specific surface area. The evaluation is based on a set of 38 X-ray tomographies of different snow samples without impregnation, scanned with an effective voxel size of 10 and 18 μm. We found that different surface area computation methods can induce relative variations up to 5 % in the density and SSA values. Regarding segmentation, similar results were obtained by sequential and energy-based approaches, provided the associated parameters were correctly chosen. The voxel size also appears to affect the values of density and SSA, but because images with the higher resolution also show the higher noise level, it was not possible to draw a definitive conclusion on this effect of resolution.

Heat transport in snowpacks is understood to occur through the two processes of heat conduction and latent heat transport carried by water vapor, which are generally treated as decoupled from one another. This paper investigates the coupling between both these processes in snow, with an emphasis on the impacts of the kinetics of the sublimation and deposition of water vapor onto ice. In the case when kinetics is fast, latent heat exchanges at ice surfaces modify their temperature and therefore the thermal gradient within ice crystals and the heat conduction through the entire microstructure. Furthermore, in this case, the effective thermal conductivity of snow can be expressed by a purely conductive term complemented by a term directly proportional to the effective diffusion coefficient of water vapor in snow, which illustrates the inextricable coupling between heat conduction and water vapor transport. Numerical simulations on measured three-dimensional snow microstructures reveal that the effective thermal conductivity of snow can be significantly larger, by up to about 50 % for low-density snow, than if water vapor transport is neglected. A comparison of our numerical simulations with literature data suggests that the fast kinetics hypothesis could be a reasonable assumption for modeling heat and mass transport in snow. Lastly, we demonstrate that under the fast kinetics hypothesis the effective diffusion coefficient of water vapor is related to the effective thermal conductivity by a simple linear relationship. Under such a condition, the effective diffusion coefficient of water vapor is expected to lie in the narrow 100 % to about 80 % range of the value of the diffusion coefficient of water vapor in air for most seasonal snows. This may greatly facilitate the parameterization of water vapor diffusion of snow in models.