GlobalMass is a 5-year ERC-funded project which aims to – for the first time at a global scale – rigorously combine satellite and in-situ data related to different aspects of the sea level budget, so that observed sea level rise can be attributed to its component parts.
The project will pioneer a powerful and generalised approach to the classical signal processing problem of “source separation”. Our approach is based on a Bayesian Hierarchical Model (BHM) – shown schematically below – which contains three ‘layers’ of information: (i) an observational layer that utilises all available direct observations, (ii) a process layer that describes the relationship between the physical processes and the observations; and (iii) a parameter layer that contains prior information about the unknown parameters in the other two layers.