Lagrangian time series models for ocean surface drifter trajectories

Sykulski, Adam M. and Olhede, Sofia C. and Lilly, Jonathan M. and Danioux, Eric (2016) Lagrangian time series models for ocean surface drifter trajectories. Journal of the Royal Statistical Society: Series C (Applied Statistics), 65 (1). pp. 29-50. ISSN 0035-9254

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Abstract

The paper proposes stochastic models for the analysis of ocean surface trajectories obtained from freely drifting satellite-tracked instruments. The time series models proposed are used to summarize large multivariate data sets and to infer important physical parameters of inertial oscillations and other ocean processes. Non-stationary time series methods are employed to account for the spatiotemporal variability of each trajectory. Because the data sets are large, we construct computationally efficient methods through the use of frequency domain modelling and estimation, with the data expressed as complex-valued time series. We detail how practical issues related to sampling and model misspecification may be addressed by using semiparametric techniques for time series, and we demonstrate the effectiveness of our stochastic models through application to both real world data and to numerical model output.

Item Type:
Journal Article
Journal or Publication Title:
Journal of the Royal Statistical Society: Series C (Applied Statistics)
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1800/1804
Subjects:
ID Code:
87317
Deposited By:
Deposited On:
10 Aug 2017 13:38
Refereed?:
Yes
Published?:
Published
Last Modified:
13 May 2020 04:32