Abstract

Detailed snow cover models such as SNOWPACK and CROCUS have become valuable tools for avalanche warning services. Increasingly ambitious applications, including gridded simulations, ensembles, and semi-automated products, now produce large multidimensional datasets that are difficult to manage and analyze. In practice, post-processing has often relied on institution-specific and often ad-hoc solutions. This has strained limited development resources, challenged scalability, and made it difficult to share methods across the international snow and avalanche community. The need for a shared foundation surfaced through AvaCollabra, an international snow and avalanche modeling community, while comparing existing approaches and discussing best practices. This bottom-up initiative resulted in xsnow, an open-source Python library that connects raw snow cover model output with downstream processing applications. Its foundation is a labeled, multidimensional data model that represents collections of profiles across locations, times, slopes, model realizations, and snow layers. A common input-output layer and extension framework allow specialized methods to build on the same foundation and be shared across the community. Several projects already use this foundation for direct model output, interactive profile visualization, community post-processing extensions, and weather-forcing pre-processing. This paper introduces xsnow’s conceptual architecture and extension philosophy and provides an early view of its emerging ecosystem. As the library continues to evolve, up-to-date tutorials, detailed documentation, and links to external extensions and applications are maintained at https://xsnow.avacollabra.org.

You can download Florian’s paper by clicking here.