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The mountainous snow cover is highly variable at all temporal and spatial scales. Snowpack models only imperfectly represent this variability, because of uncertain meteorological inputs, physical parameterizations, and unresolved terrain features. In situ observations of the height of snow (HS), despite their limited representativeness, could help constrain intermediate and large-scale modeling errors by means of data assimilation. In this work, we assimilate HS observations from an in situ network of 295 stations covering the French Alps, Pyrenees, and Andorra, over the period 2009–2019. In view of assimilating such observations into a spatialized snow cover modeling framework, we investigate whether such observations can be used to correct neighboring snowpack simulations. We use CrocO, an ensemble data assimilation framework of snow cover modeling, based on a particle filter suited to the propagation of information from observed to unobserved areas. This ensemble system already benefits from meteorological observations, assimilated within SAFRAN analysis scheme. CrocO also proposes various localization strategies to assimilate snow observations. These approaches are evaluated in a leave-one-out setup against the operational deterministic model and its ensemble open-loop counterpart, both running without HS assimilation. Results show that an intermediate localization radius of 35–50 km yields a slightly lower root mean square error (RMSE), and a better spread–skill than the strategy of assimilating all the observations from a whole mountain range. Significant continuous ranked probability score (CRPS) improvements of about 13 % are obtained in the areas where the open-loop modeling errors are the largest, e.g., the Haute-Ariège, Andorra, and the extreme southern Alps. Over these areas, weather station observations are generally sparser, resulting in more uncertain meteorological analyses and, therefore, snow simulations. In situ HS observations thus show an interesting complementarity with meteorological observations to better constrain snow cover simulations over large areas. [ABSTRACT FROM AUTHOR]

The small scale distribution of the snowpack in mountain areas is highly heterogeneous, and is mainly controlled by the interactions between the atmosphere and local topography. However, the influence of different terrain features in controlling variations in the snow distribution depends on the characteristics of the study area. As this leads to uncertainties in high spatial resolution snowpack simulations, a deeper understanding of the role of terrain features on the small scale distribution of snow depth is required. This study applied random forest algorithms to investigate the temporal evolution of snow depth in complex alpine terrain using as predictors various topographical variables and in situ snow depth observations at a single location. The high spatial resolution (1 m x 1 m) snow depth distribution database used in training and evaluating the random forests was derived from terrestrial laser scanner (TLS) devices at three study sites, in the French Alps (2 sites) and the Spanish Pyrenees (1 site). The results show the major importance of two topographic variables, the topographic position index and the maximum upwind slope parameter. For these variables the search distances and directions depended on the characteristics of each site and the TLS acquisition date, but are consistent across sites and are tightly related to main wind directions. The weight of the different topographic variables on explaining snow distribution evolves while major snow accumulation events still take place and minor changes are observed after reaching the annual snow accumulation peak. Random forests have demonstrated good performance when predicting snow distribution for the sites included in the training set with R2 values ranging from 0.82 to 0.94 and mean absolute errors always below 0.4 m. Oppositely, this algorithm failed when used to predict snow distribution for sites not included in the training set, with mean absolute errors above 0.8 m. [ABSTRACT FROM AUTHOR]