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A surrogate-based approach to nonlinear, non-Gaussian joint state-parameter data assimilation
Author(s) -
John Maclean,
Elaine Spiller
Publication year - 2021
Publication title -
foundations of data science
Language(s) - English
Resource type - Journals
ISSN - 2639-8001
DOI - 10.3934/fods.2021019
Subject(s) - particle filter , data assimilation , gaussian , nonlinear system , algorithm , computer science , dimension (graph theory) , mathematics , artificial intelligence , kalman filter , physics , quantum mechanics , meteorology , pure mathematics
Many recent advances in sequential assimilation of data into nonlinear high-dimensional models are modifications to particle filters which employ efficient searches of a high-dimensional state space. In this work, we present a complementary strategy that combines statistical emulators and particle filters. The emulators are used to learn and offer a computationally cheap approximation to the forward dynamic mapping. This emulator-particle filter (Emu-PF) approach requires a modest number of forward-model runs, but yields well-resolved posterior distributions even in non-Gaussian cases. We explore several modifications to the Emu-PF that utilize mechanisms for dimension reduction to efficiently fit the statistical emulator, and present a series of simulation experiments on an atypical Lorenz-96 system to demonstrate their performance. We conclude with a discussion on how the Emu-PF can be paired with modern particle filtering algorithms.

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