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Snowpack models simulate the evolution of the snow stratigraphy based on meteorological inputs and have the potential to support avalanche risk management operations with complementary information relevant to their avalanche hazard assessment, especially in data-sparse regions or at times of unfavorable weather and hazard conditions. However, the adoption of snowpack models in operational avalanche forecasting has been limited, predominantly due to missing data processing algorithms and uncertainty around model validity. Thus, to enhance the usefulness of snowpack models for the avalanche industry, numerical methods are required that evaluate and summarize snowpack model output in accessible and relevant ways. We present algorithms that compare and assess generic snowpack data from both human observations and models. Our approach exploits Dynamic Time Warping, a well-established method in the data sciences, to match layers between snow profiles and thereby align them. The similarity of the aligned profiles is then evaluated by our independent similarity measure based on characteristics relevant for avalanche hazard assessment. Since our methods provide the necessary quantitative link to data clustering and aggregating methods, we demonstrate how snowpack model output can be grouped and summarized according to similar hazard conditions. Through emulating a human avalanche hazard assessment approach, our methods aim to promote the operational application of snowpack models so that avalanche forecasters can begin to build understanding in how to interpret and when to trust operational snowpack simulations.
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Dynamic Time Warping can be used to automate the alignment of two snow profiles. We call those two profiles query and reference. A first intermediate step is to compute the alignment maps that link layers in the query profile to layers in the reference profile (drawn in dashed gray lines between the corresponding layers – every line corresponds to one layer). With the aid of those alignment maps, the query profile can be warped and then represented as the warped query. The warped query contains all layer information of the query, but its layers have been stretched and compressed to match the corresponding reference height. In a second step, the alignment maps are exploited to compute a similarity measure between the profiles. This measure is a scalar value that represents the similarity between the two profiles while taking into account the structural stratigraphy of the snowpack.
Both the alignment maps and the similarity measure serve as the basis for snow profile aggregation and clustering: A group of profiles will be aligned to compute the group’s representative profile, whereas the similarity measure can be used to distinguish between different groups. As the method can be applied to both modeled and human profiles, future application cases can be, e.g. (i) aggregating human observations, (ii) evaluating modeled snowpack summaries against human observations.
Checking the date checkbox will allow to configure how strongly the alignment is supposed to be based on the date information in relation to the grain type and hardness information. The choice of those weights is heavily dependent on the profiles at hand: A small spatial scale will allow to use date information alone, as the same meteorological processes are happening at the same time. However, the larger the spatial scale, the more importance may be given to the structural stratigraphy. Additionally to the weights, the date normalization factor needs to be specified: For example, a value of 3 indicates that two layers that have been formed with a delay of three days, are as different as two grain types that have a similarity of zero. In other words, that factor can be interpreted as the meteorological delay that is anticipated between the two locations of the profiles.
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For any questions about the snow profile alignment app and its application, please contact Florian Herla at email@example.com.