Saddle Point Preconditioners for Weak Constraint Four-Dimensional Variational Data Assimilation

Data assimilation algorithms blend observation data together with prior information from a numerical model to obtain an improved estimate of the current state of a dynamical system of interest. In this setting, the use of correlated observation error covariance matrices has produced many opportunities for improved initial conditions for weather forecasts, but at the risk of more ill-conditioned linear systems which can be prohibitively expensive to solve. In this talk I will present novel preconditioners for a saddle point formulation of the weak-constraint 4DVar data assimilation problem, with a focus on the correlated observation error setting. I will present a number of new preconditioners that improve on current state-of-the-art, and allow for reductions in computational cost through the use of parallel architectures or matrix-oriented iterative methods. This is work with John Pearson (University of Edinburgh).

Jemima is an Assistant Professor at TU Eindhoven. She completed her PhD at the University of Reading in 2019 before postdoctoral positions at the University of Edinburgh, ICERM (Brown University) and most recently a Hooke Fellowship at the University of Oxford. Her main research interests are numerical linear algebra and data assimilation.

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