At SkiWeather.eu, our snow forecast maps are built on a robust statistical modeling framework that integrates high-resolution meteorological data, advanced geospatial modeling, and adaptive statistical learning. The foundation of our system consists of approximately 20,000 observation points across the Alps, capturing a wide range of climatic conditions at varying altitudes. This rich dataset enables us to generate precise, elevation-sensitive snow forecasts.
To account for vertical variation in snowfall, forecast values are dynamically adjusted based on altitude relative to the freezing level, a critical step for modeling temperature-dependent precipitation phases. This elevation correction refines our ability to accurately reflect snow distribution patterns, especially in regions near the rain–snow transition zone.
At the heart of our prediction engine is a Generalized Additive Model (GAM), which captures the nonlinear relationships between snow accumulation and key predictors such as spatial coordinates, altitude, and microclimate clusters. The microclimate effect is derived from detailed time series of snow depth, snowfall, and temperature at both valley and mountain stations, allowing us to model localized snow behavior with high precision.
The GAM was fitted using a global spatial smooth, cluster-specific deviations with shrinkage selection, and an adaptive smooth for altitude. Rare clusters were pooled to avoid overfitting while preserving all data. The final model achieved a deviance explained of 76.91%, offering a strong balance between accuracy and interpretability. Adaptive smoothing of altitude improved model parsimony while maintaining performance, and shrinkage penalization automatically removed uninformative cluster terms.
To produce continuous and highly localized snow maps, we apply kriging interpolation, a geostatistical method that estimates snow accumulation at unmeasured locations based on nearby observations. This creates a seamless forecast surface, extending the predictive power of our model to every part of the Alps — even in data-sparse regions.
We also implement rigorous data filtering procedures, eliminating outliers and ensuring model inputs are reliable and meteorologically sound. By combining these statistical techniques with detailed topographic and climatic data, SkiWeather.eu delivers highly localized, realistic, and actionable snow forecasts, empowering skiers and resorts alike with accurate, next-generation insights into upcoming snow conditions.