Snow avalanches are one of the deadliest natural hazards in the Alps, causing around 100 casualties annually. Reliable avalanche forecasts are therefore essential for public safety. In Switzerland, regional avalanche forecasts increasingly rely on physics-based snowpack simulations driven by measurements from around 200 automated weather stations. Their output is processed by operational decision-support models to identify critical snowpack characteristics, such as the presence of weak layers and their potential for human- or naturally triggered avalanche release. However, while weather stations provide high-quality observations at specific locations, they cannot fully capture the spatial variability of weather and snow conditions in complex alpine terrain. We therefore ask whether high-resolution numerical weather prediction can provide sufficiently accurate meteorological input to simulate snowpack evolution beyond weather-station locations. To answer this question, we drive the SNOWPACK model with 1 km MeteoSwiss weather analysis data (KENDA-CH1) and validate the simulations against snow profile observations obtained across Switzerland. We quantify agreement between manual profiles and simulated snowpack stratigraphy using an automated profile-matching algorithm specifically designed to evaluate the similarity of snowpack characteristics relevant for avalanche release. The study will provide a systematic assessment of the potential of high-resolution weather models to drive operational snowpack simulations beyond weather-station locations. The resulting framework will also allow the operational monitoring of snowpack simulations. If successful, reliable spatially distributed snowpack simulations would provide the foundation for higher-resolution avalanche forecasts and represent an important step towards a future high-resolution avalanche warning system in Switzerland.
