Snow formation and redistribution in complex terrain is highly related to atmospheric variables. During this presentation, we will present the results that we acquired in our respective Master's thesis. Both projects were conducted in the Snow processes team of SLF, under the supervision of the Dr. Sergi Gonzàlez Herrero, and involved working on numerical modelling and experimental datasets. Indeed, using models can help to fill in the gaps in our understanding of snow related processes in both space and time, in areas where harsh sampling conditions have impeded our ability to have extensive observations.
Regarding snow formation, parameters such as snow density influence both mechanical and thermal characteristics of the snowpack which are key components in many applications, including avalanche warning. Fresh snow density formation is investigated to enhance the current 1D snow density parameterization from snow model SNOWPACK, depending on meteorological input, to extend it to atmospheric variables. The location of this study lies on Weissfluhjoch site, whereas field measurement has been taken of daily data from SMP and density cutter, to compute fresh snow density by using numerical method. KENDA data from the meteorological model ICON-CH1, is used to compute atmospheric variables such as air supersaturation. Vertical profile of the Weissfluhjoch site is observed using ICON-CH1 within an hourly and 1 km resolution, and field measurement have been taken since 2015 on the SLF station. Jonathan’s project therefore focuses on enhancing fresh snow formation parametrization, by researching and including more correlation between atmospheric variables and fresh snow density. Actual retrieval method of fresh snow is trying to be improved in the same time. This project will help to have a better understanding of the snowpack, and more on the formation of the layers. Parametrization of this variable will help for a better accuracy in snow model as SNOWPACK and also on meteorological model as KENDA ensemble model.
Following snow formation, wind continuously erode, transport, and deposit this snow. Accurately resolving the spatial heterogeneity of snow in complex terrain, mainly due to redistribution processes such as drifting and blowing snow, at high spatial resolution is essential for quantifying hydrological resources. Previous simulations with the high-resolution snow model Alpine3D, a distributed version of SNOWPACK that includes drifting-snow physics, have shown that deposition patterns are strongly influenced by wind direction and topography. To this end, the goal of Jacquemine's work is to evaluate the snow redistribution simulated by CRYOWRF, against in-situ observations during a drifting-snow event. It aims at quantifying model skill, identifying the main sources of error, and assessing to what extent CRYOWRF improves the representation of snow redistribution processes. We more specifically use a recently developed model in our group, CRYOWRF, which couples the widely used atmospheric model WRF with SNOWPACK and incorporates an enhanced drifting-snow scheme, enabling a more physically consistent representation of atmosphere-snow interactions, constituting a land-surface snow cover model. However, its ability to realistically reproduce snow redistribution patterns has not yet been fully assessed. The different steps which will be presented consist in the processing of the observation data, the regridding to the model output and running, the CRYOWRF simulations and quantify models skill, identify the sources of error based on the results obtained.