Speaker
Description
Modern Earth-system models simulate interacting atmospheric, oceanic, land-surface, cryospheric, and chemical processes. Yet estimating the current state of these systems from incomplete and noisy observations remains a fundamental challenge. Current approaches rely on separate estimation procedures for data assimilation in different Earth-system components, leading to inconsistencies, suboptimal estimates, and adjustment shocks.
This project explores whether the relationships in the background-error covariance model, currently prescribed through analytical uncertainty models can instead be learned directly from data. To this end, we develop graph-neural-network autoencoders that compress the coupled Earth system into a common low-dimensional latent space, where observations and model predictions can be combined consistently.
The approach offers three potential advantages. First, it enables observations from one Earth-system component to influence others through learned cross-component relationships. Second, nonlinear latent representations may better capture the non-Gaussian behaviour of variables such as air humidity, sea ice thickness, salinity, or ozone. Third, dimensionality reduction in latent space improves computational efficiency while preserving physically meaningful structures. Beyond the methodological advances, more physically consistent state estimates can improve both weather forecasts and Earth-system reanalyses.