Progress and outputs

Statistical framework

Progress (April 2018)

A major focus of the project over its first 18 months has been the development and testing of the Bayesian Hierarchical Model (BHM) – the statistical framework that is fundamental to the GlobalMass project. It is essential that the framework is working correctly before we can start to add in more data and use it to derive geophysical results.

We have extended the BHM framework from resolving regional processes on a cartesian grid (as used previously, for example in the NERC-funded RATES project) to global processes operating on a sphere, and have proposed two generic models for dealing with non-stationarity of the global process. As such, the framework now has a potentially wide range of global data assimilation applications.

In addition, we have developed and tested a more efficient approach for solving the stochastic partial differential equations in the BHM know as INLA (Integrated Nested Laplace Approximation). This is an important step, as it is computationally more efficient, which was a technical constraint for the implementing the BHM at a global scale with adequate resolution.


Sha, Z., Rougier, J., Schumacher, M. and Bamber, J. L. (preprint), Bayesian model-data synthesis with an application to global Glacio-Isostatic Adjustment, arKiv:1804.06285.

Other outputs

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