Gerd Weiss

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.

The step towards interactively displaying the avalanche bulletins twice a day in four languages raised the need for specifically designed software tools to support the avalanche forecasters in their daily work of producing the avalanche bulletin and make them publicly accessible. Due to the various forms of visualization and location specific bulletins, the final products (printed reports, interactive web-pages, interactive mobile applications) cannot be produced manually. Data entry graphical user interfaces are required to provide the avalanche forecasters with a platform to define and describe the current situation and store the abstracted data into a database. Based on this abstracted data, other software tools will then produce the intermediate (maps, web-page fragments, text blocks) and final products. Finally, web technology and mobile applications present the generated avalanche bulletin in an interactive form to the end user. We present a technical insight into the specifically developed software required to generate and publish the avalanche bulletin, starting with the data entry and ending with the visualization of the final products to the end user.