Evaluating a prediction system for snow management

Abstract. The evaluation of snowpack models capable of accounting for snow management in ski resorts is a major step towards acceptance of such models in supporting the daily decision-making process of snow production managers. In the frame of the EU H2020 project PROSNOW, a service to enable real-time optimisation of grooming and snow-making in ski resorts was developed. We applied snow management strategies integrated in the snowpack simulations of AMUNDSEN, Crocus and SNOWPACK/Alpine3D for nine PROSNOW ski resorts located in the European Alps. We assessed the performance of the snow simulations for five winter seasons (2015–2020) using both, ground-based data (GNSS measured snow depth) and space-borne snow maps derived from Copernicus Sentinel-2. Particular attention has been devoted to characterize the spatial performance of the simulated piste snow management at a resolution of 10 meters. The simulated results showed a high overall accuracy of more than 80 % compared to the Sentinel-2 data. Moreover, the correlation to the ground observation data was high. Potential sources for the overestimation of the snow depth by the simulations are mainly due to the impact of snow redistribution by skiers or spontaneous local adaptions of the snow management, which were not reflected in the simulations. Subdividing each individual ski resort in differently-sized ski resort reference units (SRU) based on topography showed a slight decrease in mean deviation. Although this work shows plausible and robust results on the ski-slope scale by all three snowpack models, the accuracy of the results is mainly dependent on the detailed representation of the real-world snow management practices in the models. This calls for an assessment of impacts from meteorological station measurements and their interpolations in the ski resorts as well as potential limitations in describing the snow cover, especially managed snow, by simulations.