Forcing and evaluating detailed snow cover models with stratigraphy observations
Viallon-Galinier, Léo ; Hagenmuller, Pascal ; Lafaysse, Matthieu
Snow cover models such as Crocus or Snowpack have been designed to simulate the detailed stratigraphy of snow properties. This is relevant, for instance, to assess snowpack stability in support of avalanche forecasting. However, such models have generally been evaluated on bulk or surface properties, such as snow depth, water equivalent of snow cover or surface albedo, but not on the detailed stratigraphy. The large number of snow profiles collected in observer networks have thus not been assimilated in such models hitherto. This study introduces a new method to (1) directly compare simulated and observed snow layering and (2) allow for the insertion of observed profile to initialize a snow cover model. This method is mainly based on a scheme to convert observations into state variables of snow cover models and matching observed and simulated layering, accounting for potential depth shifts. The developed methodology was applied to the Crocus snow cover model at three sites in the French Alps, for 15 winter seasons between 2000 and 2015. The performance of Crocus initialized with a bare ground at the end of the summer was evaluated against 739 observed profiles. The model performance varied with the considered winter season and sites. On average, Crocus reproduced snow depth with a median error of 12 cm, layer density with a median error of 50 kg m-3, layer grain shape with an error of 0.31 according to a specially developed metric. The re-initialization of the model with observed profiles during winter season enabled to reduce these simulation errors. One week after the direct insertion of a manual profile, the median error of the simulation decreased to 6.8 cm for snow depth, 39 kg m-3 for density and 0.25 for grain shape. However, the improvement provided by this re-initialization almost completely vanished one month after the insertion.</p>
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