Accessibility navigation


An ensemble framework for time delay synchronisation

Pinheiro, F. R., Van Leeuwen, P. J. and Parlitz, U. (2018) An ensemble framework for time delay synchronisation. Quarterly Journal of the Royal Meteorological Society, 144 (711(partB)). pp. 305-316. ISSN 1477-870X

[img]
Preview
Text (Open Access) - Published Version
· Available under License Creative Commons Attribution.
· Please see our End User Agreement before downloading.

11MB
[img] Text - Accepted Version
· Restricted to Repository staff only

3MB

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

To link to this item DOI: 10.1002/qj.3204

Abstract/Summary

Synchronisation theory is based on a method that tries to synchronise a model with the true evolution of a system via the observations. In practice, an extra term is added to the model equations that hampers growth of instabilities transversal to the synchronisation manifold. Therefore, there is a very close connection between synchronisation and data assimilation. Recently, synchronisation with time delayed observations has been proposed, in which observations at future times are used to help synchronise a system that does not synchronise using only present observations, with remarkable successes. Unfortunately, these schemes are limited to small-dimensional problems. In this paper, we lift that restriction by proposing ensemble-based synchronisation scheme. Tests were performed using Lorenz96 model for 20, 100 and 1000-dimension systems. Results show global synchronisation errors stabilising 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.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > National Centre for Earth Observation (NCEO)
Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:73938
Publisher:Royal Meteorological Society

Downloads

Downloads per month over past year

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation