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Model and data reduction for data assimilation: Particle filters employing projected forecasts and data with application to a shallow water model

https://doi.org/10.1016/j.camwa.2021.05.026

Albarakati, Aishah ; Budišić, Marko ; Crocker, Rose ; Glass-Klaiber, Juniper ; Iams, Sarah ; Maclean, John ; Marshall, Noah ; Roberts, Colin ; Van Vleck, Erik S. ( June 2022 , Computers & Mathematics with Applications)

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Particle filters for data assimilation based on reduced‐order data models

https://doi.org/10.1002/qj.4001

Maclean, John ; Vleck, Erik S. ( April 2021 , Quarterly Journal of the Royal Meteorological Society)
null (Ed.)
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A surrogate-based approach to nonlinear, non-Gaussian joint state-parameter data assimilation

https://doi.org/10.3934/fods.2021019

Maclean, John ; Spiller, Elaine T. ( January 2021 , Foundations of Data Science)
null (Ed.)

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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