.png){kind=icon}
Return
Efficient nonlinear data assimilation using synchronization in a particle filter
.png){kind=icon}
OA
AI
DOI：10.1002/qj.3576.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Current data assimilation methods still face problems in strongly nonlinear cases. A promising solution is a particle filter, which provides a representation of the state probability density function (pdf) by a discrete set of particles. To allow a particle filter to work in high-dimensional systems, the proposal density freedom is explored. We used a proposal density from synchronization theory, in which one tries to synchronize the model with the true evolution of a system using one-way coupling, via the observations. This is done by adding an extra term to the model equations which will control the growth of instabilities transversal to the synchronization manifold. In this paper, an efficient ensemble-based synchronization scheme is used as a proposal density in the implicit equal-weights particle filter, a particle filter that avoids filter degeneracy by construction. Tests using the Lorenz96 model for a 1,000-dimensional system show successful results, where particles efficiently follow the truth, both for observed and unobserved variables. These first tests show that the new method is comparable to, and slightly outperforms, a well-tuned Local Ensemble Transform Kalman Filter. This methodology is a promising solution for high-dimensional nonlinear problems in the geosciences, such as numerical weather prediction.
Keywords:
data assimilation
ensemble
synchronization
nonlinear
particle filter
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
2.9
Papers:
5.8K
Citations:
2.4W
Organization
Cited Papers
ShareSaveShareSaveExtracellular Matrix Optimization for Enhanced Physiological Relevance in Hepatic Tissue-Chips.png)
Polymers
OAAI
PolymersIF0
OAAIShareSaveShareSaveShareSave
View more