Item: SAFE - A LAYER-BASED AVALANCHE FORECAST EDITOR FOR BETTER INTEGRATION OF MODEL PREDICTIONS
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Title: SAFE - A LAYER-BASED AVALANCHE FORECAST EDITOR FOR BETTER INTEGRATION OF MODEL PREDICTIONS
Proceedings: International Snow Science Workshop 2024, Tromsø, Norway
Authors:
- Kurt Winkler [ WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland ]
- Jürg Trachsel [ WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland ]
- Jonas Knerr [ WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland ]
- Ueli Niederer [ WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland ]
- Gerd Weiss [ WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland ]
- Marc Ruesch [ WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland ]
- Frank Techel [ WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland ]
Date: 2024-09-23
Abstract: 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.
Object ID: ISSW2024_P1.9.pdf
Language of Article: English
Presenter(s): Jürg Trachsel
Keywords: Avalanche Forecast, Bulletin Editor, Numerical Models, Decision Support, Hazard Assessment
Page Number(s): 124 - 131
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