Learning from the past: statistical performance measures for avalanche warning services

Avalanche warning services (AWS) are operated to protect communities and traffic lines in avalanche-prone regions of the Alps and other mountain ranges. In times of high avalanche danger, these services may decide to close roads or to evacuate settlements. Closing decisions are based on field observations, avalanche release statistics, and snow forecasts issued by weather services. Because of the spatial variability in the snowpack and the insufficient understanding of avalanche triggering mechanisms, closing decisions are characterized by large uncertainties and the information based on which AWS have to decide is always incomplete. In this paper, we illustrate how signal detection theory can be applied to make better use of the information at hand. The proposed framework allows the evaluation of past road closures and points to how the decision performance of AWS could be improved. To illustrate the proposed framework, we evaluate the decision performance of two AWS in Switzerland and discuss the advantages of such a formalized decisionmaking approach.