This conceptual model of avalanche hazard identifies the key components of avalanche hazard and structures them into a systematic, consistent workflow for hazard and risk assessments. The method is applicable to all types of avalanche forecasting operations, and the underlying principles can be applied at any scale in space or time. The concept of an avalanche problem is introduced, describing how different types of avalanche problems directly influence the assessment and management of the risk. Four sequential questions are shown to structure the assessment of avalanche hazard, namely: (1) What type of avalanche problem(s) exists? (2) Where are these problems located in the terrain? (3) How likely is it that an avalanche will occur? and (4) How big will the avalanche be? Our objective was to develop an underpinning for qualitative hazard and risk assessments and address this knowledge gap in the avalanche forecasting literature. We used judgmental decomposition to elicit the avalanche forecasting process from forecasters and then described it within a risk-based framework that is consistent with other natural hazards disciplines.

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Abstract. A climate version of the Regional Atmospheric Modeling System (RAMS) is used to simulate snow-related land-atmosphere interactions in the Great Plains and Rocky Mountain regions of the United States. The availability of observed snow-distribution products allow snow-water-equivalent distribution data to be assimilated directly into the RAMS simulations. By performing two kinds of model integrations, one with and one without assimilating the snow-distribution observations, the differences between the model runs are used to highlight model deficiencies and limitations and thus identify areas of possible improvement in the atmospheric model. The need to simulate subgrid snow distributions is identified and addressed by implementing a snow submodel that accounts for subgrid variations in air temperature and precipitation. This subgrid snow model is found to significantly improve the model's simulation of snow-related processes. IntroductionWith its high albedo, low thermal conductivity, and considerable spatial and temporal variability, seasonal snow cover overlying land plays a key role in governing the Earth's global radiation balance; this balance is the primary driver of the Earth's atmospheric circulation system and associated climate. Realistically representing seasonal snow accumulation and depletion in regional and global atmospheric and hydrologic models is complex because key snow-related features possess considerable temporal and spatial variability. These differences also occur at scales below those resolved by the models. As an example of this variability, over the winter landscape in middle and high latitudes, the interactions between wind, vegetation, and topography produce snowcovers of nonuniform depth and density [e.g., Liston and Sturm, 1998]. In addition, orographically produced precipitation can display significant

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