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https://hdl.handle.net/2440/135776
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Type: | Journal article |
Title: | A surrogate-based approach to nonlinear, non-Gaussian joint state-parameter data assimilation |
Author: | Maclean, J. Spiller, E.T. |
Citation: | FOUNDATIONS OF DATA SCIENCE, 2021; 3(3):589-614 |
Publisher: | American Institute of Mathematical Sciences (AIMS) |
Issue Date: | 2021 |
ISSN: | 2639-8001 |
Statement of Responsibility: | John Maclean, Elaine T. Spiller |
Abstract: | 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. |
Keywords: | Data assimilation; uncertainty quantification; data science; statistical surrogates; parameter estimation |
Rights: | ©American Institute of Mathematical Sciences |
DOI: | 10.3934/fods.2021019 |
Grant ID: | http://purl.org/au-research/grants/arc/DP180100050 |
Published version: | http://dx.doi.org/10.3934/fods.2021019 |
Appears in Collections: | Aurora harvest 4 Mathematical Sciences publications |
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