Jonas Christian Knerr

Machine learning (ML) models are revolutionizing avalanche forecasting, providing increasingly reliable predictions. At present, however, such model output can hardly be integrated into the traditional operational forecast workflow. To close this gap, the Swiss Avalanche Warning Service has developed the Snow Avalanche Forecast Editor (SAFE), allowing a flexible integration of both human forecasts and model predictions. In SAFE, each hazard source (e.g., an avalanche problem) is assessed and described as a separate layer. This allows an entirely independent characterization of the severity of avalanche problems including their spatial extent, in line with the European Avalanche Warning Services standards. Once the assessment has been completed, all existing layers are automatically combined and published as regional avalanche forecasts. Forecast accuracy can be increased by minimizing noise. Decision theory advises to calculate an expected value from multiple independent assessments. For this reason, each of the two or three forecasters on duty assesses the regional avalanche danger in the forecast domain independently of each other. Another estimate comes from the danger-level model. To combine these, forecasters and models must assess the same hazard source using the same target variable. SAFE merges these independent assessments to expected values of the danger level including sub-levels, and the particularly affected elevations and aspects. This includes clustering into a limited number of danger regions, characterized by similar expected avalanche conditions. Following the discussion between forecasters, clusters can be refined. Lastly, the avalanche danger of each cluster is manually described. SAFE is conceived as open-source platform and a first version has been operational within the Swiss avalanche warning since winter 2023/24.

CAAML Version 6 for EAWS bulletins is a standardized data format designed to describe avalanche bulletins, providing a common language for conveying crucial information about snowpack conditions, avalanche activity, and hazards. Developed through collaborative efforts by international organizations and experts, CAAML Version 6 facilitates seamless data exchange, enhances data accuracy, and promotes effective decision-making in avalanche risk management. This paper offers a comprehensive overview of the format, discussing its fundamental principles, structure, and implementation guidelines. The support for both XML and JSON data formats, along with data validation guidelines and extensibility, ensures CAAML Version 6 remains relevant and adaptable to advances in avalanche science and information technology.