An ensemble framework for time delay synchronization
Synchronization based state estimation tries to synchronize a model with the true evolution of a system via the observations. In practice, an extra term is added to the model equations which hampers growth of instabilities transversal to the synchronization manifold. Therefore, there is a very close...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2018 |
| País: | Brasil |
| Institución: | Marinha do Brasil (MB) |
| Repositorio: | Repositório Institucional da Produção Científica da Marinha do Brasil (RI-MB) |
| Idioma: | inglés |
| OAI Identifier: | oai:www.repositorio.mar.mil.br:ripcmb/844607 |
| Acceso en línea: | http://www.repositorio.mar.mil.br/handle/ripcmb/844607 |
| Access Level: | acceso abierto |
| Palabra clave: | Sincronization Data Assimilation Ensemble |
| Sumario: | Synchronization based state estimation tries to synchronize a model with the true evolution of a system via the observations. In practice, an extra term is added to the model equations which hampers growth of instabilities transversal to the synchronization manifold. Therefore, there is a very close connection between synchronization and data assimilation. Recently, synchronization with time-delayed observations has been proposed, in which observations at future times are used to help synchronize a system that does not synchronize using only present observations, with remarkable successes. Unfortunately, these schemes are limited to small-dimensional problems. In this article, we lift that restriction by proposing an ensemble-based synchronization scheme. Tests were performed using the Lorenz’96 model for 20-, 100- and 1000-dimension systems. Results show global synchronization errors stabilizing at values of at least an order of magnitude lower than the observation errors, suggesting that the scheme is a promising tool to steer model states to the truth. While this framework is not a complete data assimilation method, we develop this methodology as a potential choice for a proposal density in a more comprehensive data assimilation method, like a fully nonlinear particle filter. |
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