Advancing snow grain classification for snow micro-penetrometer signals using machine learning

The layered nature of snow is a key characteristic of the seasonal alpine snowpack. In fact, snow stratigraphy influences all physical processes e.g., mechanical or thermal behavior. In order to describe these physical processes precisely, a profound and objective representation of the snow stratigraphy is paramount. The Snow-Micro Penetrometer (SMP) is a rod-driven snow penetrometer that provides resistance-force profiles across snow depth, offering an objective method to measure vertical snow stratigraphy. These submillimeter-scale profiles facilitate the derivation of a micro-mechanical snow model. These derivatives have the potential to initialize complex, physics-based snow cover models (e.g., SNOWPACK). While many parameters for snowpack simulations can be derived directly, determining grain type remains challenging due to the absence of a clear physical correlation. To address this, machine learning (ML) approaches have been investigated. However, prior ML models are limited in their number of snow grain type classes and datasets, which prevents the operational use of these models. Recently, Kaltenborn et al. introduced Snowdragon, a ML benchmark for automated classification and segmentation of SMP profiles. The current version of Snowdragon is trained on SMP profiles collected during the MOSAiC expedition and contains only specific non-standardized grain types typically observed for snow on Arctic sea ice. In this work, we re-trained the supervised models of the Snowdragon benchmark on Alpine snow. To enable the usage of Snowdragon for a broader community, we adapted the classification of grain types according to the international standard for seasonal snow. Our dataset comprises 52 manually labeled SMP profiles recorded in Alpine snow in Switzerland. Previously identified high-performing ML models were re-trained without additional hyperparameter tuning and subsequently evaluated. We found that the ML model Random Forest performed best but nevertheless had difficulties in recognizing faceted crystals, similar to the other models. Additionally, all models react sensitive to minor force changes in the SMP profiles, often leading to predictions of alternating micro-classes between two grain types. These preliminary results demonstrate the feasibility of this approach for grain type classification, but underscore the limitations posed by the small dataset size. Future work will focus on expanding the training dataset and developing a robust interface for operational use of the prediction output. This work marks a step toward more reliable and generalizable snow grain classification of SMP signals for operational use, like snowpack modeling and avalanche assessment.

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