Estimating the state of a geophysical system with sparse observations: time delay methods to achieve accurate initial states for prediction

TitleEstimating the state of a geophysical system with sparse observations: time delay methods to achieve accurate initial states for prediction
Publication TypeJournal Article
Year of Publication2017
AuthorsAn Z., Rey D., Ye J.X, Abarbanel H.DI
JournalNonlinear Processes in Geophysics
Volume24
Pagination9-22
Date Published2017/01
Type of ArticleArticle
ISBN Number1023-5809
Accession NumberWOS:000394058300001
Keywords4-dimensional variational assimilation; chaotic systems; dynamics; equations; field; lagrangian data; number; observability; operational implementation; parameter-estimation
Abstract

The problem of forecasting the behavior of a complex dynamical system through analysis of observational time-series data becomes difficult when the system expresses chaotic behavior and the measurements are sparse, in both space and/or time. Despite the fact that this situation is quite typical across many fields, including numerical weather prediction, the issue of whether the available observations are "sufficient" for generating successful forecasts is still not well understood. An analysis by Whartenby et al. (2013) found that in the context of the nonlinear shallow water equations on a beta plane, standard nudging techniques require observing approximately 70% of the full set of state variables. Here we examine the same system using a method introduced by Rey et al. (2014a), which generalizes standard nudging methods to utilize time delayed measurements. We show that in certain circumstances, it provides a sizable reduction in the number of observations required to construct accurate estimates and high-quality predictions. In particular, we find that this estimate of 70% can be reduced to about 33% using time delays, and even further if Lagrangian drifter locations are also used as measurements.

DOI10.5194/npg-24-9-2017
Short TitleNonlinear Process Geophys.
Student Publication: 
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