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Many recent advances in sequential assimilation of data into nonlinearhigh-dimensional models are modifications to particle filters which employefficient searches of a high-dimensional state space. In this work, we presenta complementary strategy that combines statistical emulators and particlefilters. The emulators are used to learn and offer a computationally cheapapproximation to the forward dynamic mapping. This emulator-particle filter(Emu-PF) approach requires a modest number of forward-model runs, but yieldswell-resolved posterior distributions even in non-Gaussian cases. We exploreseveral modifications to the Emu-PF that utilize mechanisms for dimensionreduction to efficiently fit the statistical emulator, and present a series ofsimulation experiments on an atypical Lorenz-96 system to demonstrate theirperformance. We conclude with a discussion on how the Emu-PF can be paired withmodern particle filtering algorithms.

A surrogate-based approach to nonlinear, non-Gaussian joint state-parameter data assimilation