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Abstract

When creating regional avalanche reports, forecasters traditionally merge a set of predefined static micro-regions to outline a warning region with uniform avalanche danger characteristics. These microregions’ boundaries, historically defined by local expertise and refined through statistical analysis, aim to capture areas with similar snow climatology and thus similar avalanche conditions. Recent studies have shown that the granularity and aggregation of smaller micro-regions to larger warning regions has a major impact on the consistency of forecasting products. This study introduces a clustering algorithm to objectively identify regions with similar precipitation patterns. Assuming that similar precipitation patterns indicate similar avalanche conditions, this approach was developed to refine the forecasting domain of the avalanche warning service Tyrol (Austria) into small micro-regions. Based on ten years of precipitation data from the spatially distributed snow cover model SNOWGRID and a synoptic flow classification (cost733), we used a k-means clustering algorithm to analyze spatial precipitation patterns for four prevailing flow regimes. The results of the cluster analysis re-confirmed the existing expert-based micro-region splitting in the forecasting domain of Tyrol (Austria). The presented approach facilitated the refinement of certain micro-regions by including independent analyses for different general flow directions, which revealed finer distribution patterns. Remarkably, these refined divisions aligned with suggestions from local experts, underlining the effectiveness of the proposed methodology in enhancing the granularity and accuracy of avalanche forecasting regions. This led to the adaptation of 13 of the 77 micro-regions. By integrating statistical techniques with expert knowledge, our approach offers a robust framework for refining micro-regions and improving avalanche risk assessment and management strategies in mountainous regions such as Tyrol.