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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Volume 24, issue 1
Nonlin. Processes Geophys., 24, 9–22, 2017
https://doi.org/10.5194/npg-24-9-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Nonlin. Processes Geophys., 24, 9–22, 2017
https://doi.org/10.5194/npg-24-9-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 16 Jan 2017

Research article | 16 Jan 2017

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

Zhe An1, Daniel Rey1, Jingxin Ye1, and Henry D. I. Abarbanel1,2 Zhe An et al.
  • 1Department of Physics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0374, USA
  • 2Marine Physical Laboratory (Scripps Institution of Oceanography) University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0374, USA

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 β 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.

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