The study objective was to compare the specific surface area (SSA) decrease rates and densification rates calculated with a parametrisation (Schleef et al. 2014b) and the widely used snow cover model SNOWPACK (Bartelt and Lehning 2002; Lehning et al. 2002b,a) with the values measured for samples extracted from the continuous stratigraphy measurements during a recent snow campaign at Weissfluhjoch, Switzerland. The vertical profiles of penetration force and the structural element length measured with the Snow Micro Penetrometer (SMP) were used to infer the SSA and density profiles using a statistical relation, thus it was possible to calculate the SSA decrease rate and the densification rate for each chosen snow sample. Knowing the sample temperature it was also possible to use the parametrisation to calculate the SSA decrease rates and densification rates and to compare them to the measured rates. The SSA decrease rates and densification rates of the samples were also calculated from the optical-equivalent grain size simulated with SNOWPACK and then compared to the measured rates. It was demonstrated that the parametrisation reproduced better than the model the measured SSA decrease rates but in both cases the measured rates were underestimated, especially for samples subject to high overburden stress. A new fit of the SSA decrease rate parametrisation on the measured rates improved the correlation but did not change the parametrisation performances for samples subject to high load. This is consistent with the fact that the parametrization was derived for new snow. It was also shown that the parametrisation reproduced much better than the model the measured densification rates, even if they were slightly underestimated. A new fit of the densification rate parametrisation on the measured rates improved the correlation but it still did not provide a one-to-one relation between parametrised and measured rates. All the results show that order of magnitude agreement is readily achieved, while it is necessary to further discern methodological uncertainties from potentially missing physical processes in the parametrizations. Nevertheless the thesis demonstrates the new analysis opportunities for validating models and parametrizations from continuous SMP measurements.