Abstract. Snow cover is a key variable for climate monitoring and hydrological applications, yet optical satellite observations are strongly limited by cloud contamination, particularly during winter. The EUMETSAT H SAF H34 snow product derived from MSG-SEVIRI uses the unique 15-minute temporal resolution over the full SEVIRI disc to clear the clouds, but persistent clouds still cause substantial data gaps. In this study, we present a cloud-gap reconstruction framework that combines Numerical Weather Prediction data with machine learning to infer snow presence beneath cloud-covered pixels in the H34 product. Skin temperature, snow depth, and snow temperature fields from the Integrated Forecast System (IFS) were used as physically consistent predictors and resampled to the H34 grid, together with elevation information from SRTM. An XGBoost-based model was trained using cloud-free H34 snow observations and applied exclusively to cloud-contaminated pixels to estimate the probability of underlying snow presence. Pixels exceeding an 80% probability threshold were reclassified as snow. The approach was applied to the winter seasons of 2024 and 2025 and validated over the European Alps using in-situ snow observations from World Meteorological Organization (WMO) stations. Evaluation using probability of detection (POD), false alarm ratio (FAR), and overall accuracy (ACC) shows a clear improvement in snow detection under cloudy conditions, with a significant reduction in missing observations. Compared to conventional temporal gap-filling methods, the proposed framework reduces reliance on temporal interpolation by directly exploiting physically meaningful meteorological information, while preserving the high temporal resolution advantage of MSG-SEVIRI.
Kuter et al. (2026) studied this question.