This interdisciplinary session brings together modellers and observationalists to present results and exchange knowledge and experience in the use of data assimilation in the cryospheric sciences such as inverse methods, geostatistics and machine learning. In numerous research fields it is now possible to not only deduce static features of a physical system but also to retrieve information on transient processes between different states or even regime shifts. In the cryospheric sciences a large potential for future developments lies at the intersection of observations and models with the aim to improve prognostic capabilities in space and time. Compared to other geoscientific disciplines like meteorology or oceanography, where techniques such as data assimilation have been well established for decades, in the cryospheric sciences only the foundation has been laid for the use of these techniques, one reason often being the sparsity of observations. We invite contributions from a wide range of methodological backgrounds - from satellite observations to deep-looking geophysical methods and advancements in numerical techniques - and research topics including permafrost, sea ice and snow to glaciers and ice sheets, covering static system characterisation as well as transient processes.