Uncertainty quantification and data assimilation – computational challenges in large-scale geoscience models

Assessing and reducing the uncertainty of large-scale simulations in the geosciences have become fundamental to increase the reliability of model forecasts. One of the major challenges arises when the description of model uncertainty and the availability of field observations are not straightforward. Especially in these cases, it is necessary to develop robust assimilation approaches combined with computationally efficient procedures. For instance, the use of appropriate surrogate models and the implementation on emerging computing architectures can help reduce the computational burden, provided that the most significant non-linearities of the physical system are preserved. This mini-symposium aims to discuss recent advances in geoscience applications where parameter and state estimation problems are tackled. Contributions dealing with novel algorithmic approaches and efficient computational procedures used in challenging applications are welcome