Premium
An ensemble framework for time delay synchronization
Author(s) -
Pinheiro Flavia R.,
van Leeuwen Peter Jan,
Parlitz Ulrich
Publication year - 2018
Publication title -
quarterly journal of the royal meteorological society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.744
H-Index - 143
eISSN - 1477-870X
pISSN - 0035-9009
DOI - 10.1002/qj.3204
Subject(s) - data assimilation , synchronization (alternating current) , computer science , lift (data mining) , particle filter , control theory (sociology) , nonlinear system , dimension (graph theory) , filter (signal processing) , mathematics , data mining , artificial intelligence , physics , meteorology , computer network , channel (broadcasting) , control (management) , quantum mechanics , pure mathematics , computer vision
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.