Snow plays an essential role in the climatic and environmental challenges of the 21st century. The snow cover represents a key source of global water supply and climate regulation, and has shown high sensitivity to a warming climate. The amount of collected snow information is also constantly increasing due to novel automated methods for cheaper and easier measurements, especially imagery. During the last decades, instrumentation and measurement techniques, especially remote sensing, have advanced fast, providing significant amount of new information about the extent and properties of snow (e.g. snow water equivalent, (SWE), albedo, reflectance, microstructure, and impurities). In addition, novel technologies such as unmanned aerial vehicles (UAVs) and webcams provide new opportunities and challenges. Optimization and agreement on sampling strategies are important to get spatially distributed data at different scales, and ensure broad use of the acquired data. Data management has become an important issue after general open data policy, where data sets should be available and usable for other users. A large variety of NWP and hydrological models or operational applications routinely make use of snow data to improve their performance. Forecasting snow related hazards in Europe is mostly performed at the country or regional level, and heavily relies on the concurrent meteorological factors and snowpack properties, which are usually acquired from point measurements or physical models. A big challenge is bridging information from microstructural scales of the snowpack up to the grid resolution in models and then to provide knowledge-based information on potential impacts to society, economy and safety (e.g. hydro-power, water availability, transportation, tourism, flooding and avalanches). In this session we would like discuss recent developments and progresses on (1) Snow data collection, curation, and management including harmonized observation techniques for several snow parameters and remote sensing snow observations by applying novel techniques, (2) Snow models, satellite-derived snow products, and data assimilation including improved snow modelling and prediction at different scales taking into account macro and microscale snow properties and (3) Monitoring snow-related hazards and extreme events including latest reanalysis and satellite data sets and models to predict and forecast extreme events and snow-related natural hazards.