SAINT - Snow cover Atmosphere INTeraction

The seasonal mountain snow cover can lead to hazardous situations (avalanches, flooding), provides the background for hydrologic resources and constitutes an important factor for global and regional climate. Forecasting the snow cover evolution in complex terrain provides valuable information to assist with avalanche danger assessments as well as runoff estimations relevant for hydropower development and flood prevention. Furthermore, knowing the evolution and spatial distribution of the seasonal mountain snow cover allows for a better estimation of the surface albedo relevant for accurate climate modelling. At present the assessment of the snow cover atmosphere interaction is based on in situ observations for operational applications such as avalanche warning and snow cover models are typically forced by local observations. Therefore, the proposed research aims at coupling a complex snow cover model (SNOWPACK) with a high-resolution numerical weather prediction model (COSMO-2) in order to forecast the spatial distribution of the snow cover. The surface energy balance determined by atmospheric conditions controls the snow cover evolution. Hence, the capability of COSMO-2 to predict the surface energy balance in complex terrain needs to be validated. All components of the surface energy balance in complex terrain will be measured and analyzed during two winter seasons at a location of an alpine weather station, with a special focus on the turbulent fluxes. Model output statistics for the turbulent exchange variables in complex snow covered terrain for various atmospheric conditions will be developed and used to post- process COSMO-2 data for use in SNOWPACK. The model chain SNOWPACK/COSMO-2 will be verified using snow cover observations from the vicinity of the experimental site. This study will give insight into the turbulent exchange in complex snow covered terrain and will create a powerful forecasting tool for European researchers and operational warning services.

The seasonal mountain snow cover can lead to hazardous situations (e.g. avalanches, flooding), provides the background for hydrologic resources and constitutes an important factor for global and regional climate. Forecasting the snow cover evolution in complex terrain provides valuable information to assist with avalanche danger assessments as well as runoff estimations relevant for hydropower development and flood prevention. Furthermore, knowing the evolution and spatial distribution of the seasonal mountain snow cover allows for a better estimation of the surface albedo relevant for accurate weather and especially climate modeling. For this project we validated the performance of the numerical weather prediction model COSMO during winter in complex alpine terrain. The surface energy balance determined by atmospheric conditions controls the snow surface temperature and therefore the snow cover evolution in terms of metamorphism, settling and melting. Hence, in a first step the capability of COSMO to predict the snow surface temperature in complex alpine terrain was validated. Validation showed an insufficient modeling of the snow surface temperature. Therefore an improved scheme for the snow surface temperature was developed and validated. This improved snow cover scheme showed good agreement to observed snow surface temperatures for the Swiss Alps, i.e., daily cycles were forecasted accurately. An improved modeling of the snow surface temperature will allow an improved modeling of the near surface air temperature a typical output of climate models. Forcing the high-resolution Swiss snow cover model SNOWPACK with forecasted data from COSMO showed promising potential to become an operational tool for avalanche warning services. Although bias corrections for the forecasted data are required - especially precipitation the overall performance shows promising potential to predict snow height stratigraphy as well as stability. The SNOWPACK-COSMO model chain was tested with historical forecasted data and was used operationally for the first time in Austria, Germany and Switzerland.