THERMAL SCIENCE

International Scientific Journal

Meng-Ge Zhou

,

Xiao-Qun CaoORCID

,

Yan Chen

,

Yu-Chen Luo

,

Jia-Le Yao

DATA ASSIMILATION FOR NON-LINEAR SYSTEMS WITH A HYBRID NON-LINEAR PARTICLE FILTER

ABSTRACT
The proposed methodology utilizes a novel data assimilation technique based on hybrid non-linear particle filters, a framework that has demonstrated efficacy in non-linear and non-Gaussian scenarios within the domain of Earth sciences. In numerical sensitivity experiments conducted on a non-linear dynamical system (Lorenz 63), the new method prevents filter divergence using only 10 particles for both dense and sparse observation networks. A comparison of the newly developed hybrid non-linear method with the local ensemble transform Kalman filter (LETKF) reveals the merits of the former in data assimilation applications analogous to geophysical data. Specifically, the newly developed filter exhibits significant advantages over the LETKF, particularly when the observation network consists of densely spaced measurements that are non-linearly related to the model state, akin to remote sensing data frequently employed in atmospheric analyses.
KEYWORDS
data assimilation, non-linear, particle filter, proposal densities
PAPER SUBMITTED: 2024-12-25
PAPER REVISED: 2025-04-30
PAPER ACCEPTED: 2025-04-30
PUBLISHED ONLINE: 2026-04-12
DOI REFERENCE: https://doi.org/10.2298/TSCI2602973Z
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2026, VOLUME 30, ISSUE No. 2, PAGES [973 - 981]
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Data assimilation for non-linear systems with a hybrid non-linear particle filter

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