The increasing availability of in-situ and remote sensing observations provides new opportunities to constrain glacier and ice sheet models and improve the representation of model parameters and states. In this session, we aim to bring together researchers who develop observational products and those developing new approaches in calibration, inverse modelling and data assimilation to better inform models and uncertainties in the predictions.

We invite studies developing direct and indirect observations, including, but not limited to, mass balance, surface elevation, ice thickness, velocity, glacier geometry, transient snowlines, albedo, snow cover, and firn properties. We also welcome contributions on model calibration, parameter estimation and inverse modelling, including optimisation, Bayesian methods, automatic differentiation and multi-objective approaches. Contributions on data assimilation and state estimation, including ensemble, particle-filter and variational methods as well as machine-learning-assisted approaches, are also encouraged. We also welcome studies addressing observational, parameter and model uncertainties and their propagation through calibration and data assimilation.