This paper introduces the "snow barrel" method, a new approach to integrate satellite-derived snow observations into NWP models. Snow is a key component of the environment, helping to regulate surface temperature, atmospheric and soil conditions, and playing a key role in the water cycle. However, assimilating satellite snow data into NWP models remains challenging due to resolution mismatches and the complexity of handling snow extent observations. The snow barrel method addresses these challenges by aggregating satellite pixel observations from the EUMETSAT H-SAF H32 intermediate product into 10x10 pixel areas and creating pseudo-observations that align with NWP model scales. Implemented within the HARMONIE-AROME model in the MetCoOp operational domain covering northern Europe, this approach selectively applies snow barrel observations where they conflict with the model's background field, particularly in regions with thin or patchy snow cover. The results demonstrate improved representation of snow cover during the transitional seasons without disrupting areas of solid cover or imposing a significant computational burden. The method effectively combines the spatial coverage advantages of satellite data with the precision of in situ measurements, particularly benefiting areas with sparse ground observations. Although constrained by cloud cover and lighting conditions inherent to optical satellite products, the snow barrel methodology offers a flexible framework that could be expanded to other satellite platforms and potentially adapted for additional surface parameters beyond snow cover.