Monitoring dry snow metamorphism using 4D tomography across 20 experimental conditions

Abstract. Refined observations of the temporal evolution of snow microstructure are crucial for improving the understanding and modeling of snow metamorphism. X-ray tomography has opened new possibilities for observing the microstructure of dry snow by enabling 3D imaging of the ice and air arrangement with micrometric resolution. The development of cells that control the thermal boundary conditions of a snow sample during scanning has made in-situ monitoring of microstructural changes during metamorphism possible. However, such data sets remain scarce and are often limited in terms of the snow evolution conditions explored. In this work, we use highly resolved X-ray tomography to characterize the temporal evolution of dry snow microstructure under a wide range of thermal boundary conditions. We designed a snow-metamorphism cell to continuously control the temperature at the boundaries of a centimeter-sized snow sample directly inside the tomograph. Using this setup, we conducted a total of 20 snow metamorphism experiments, covering mean snow temperatures from −3 to −17 °C, snow temperature gradients from 0 to 100 K m−1, and five initial snow samples with varying snow types, densities, and specific surface areas. Each experiment lasted 7 d, during which tomographic measurements were performed every 4 h at a spatial resolution of 8.5 µm. We provide a unique set of 4D data in .zarr format, consisting of time series of binary 3D images of snow undergoing the aforementioned experiments. These images are particularly well-suited for investigating local processes, such as the interface growth velocity, as well as the geometrical properties of snow, such as specific surface area, chord length distribution, or surface curvature, to name just a few. Computing these properties could help to model their evolution during dry snow metamorphism. In addition, videos showing the temporal evolution of the snow microstructure for the 20 experiments are provided. The data set and the videos are available at https://doi.org/10.1594/PANGAEA.992556 (Dick et al., 2026).