UCL logo

UCL Discovery

UCL home » Library Services » Electronic resources » UCL Discovery

Lagrangian Time Series Models for Ocean Surface Drifter Trajectories

Sykulski, AM; Olhede, SC; Lilly, JM; Danioux, E; (2015) Lagrangian Time Series Models for Ocean Surface Drifter Trajectories. Journal of the Royal Statistical Society: Series C (Applied Statistics) , 65 (1) pp. 29-50. 10.1111/rssc.12112. Green open access

[img]
Preview
Text
olhede_lagrangian.pdf

Download (6MB) | Preview

Abstract

This paper proposes stochastic models for the analysis of ocean surface trajectories obtained from freely-drifting satellite-tracked instruments. The proposed time series models are used to summarise large multivariate datasets and infer important physical parameters of inertial oscillations and other ocean processes. Nonstationary time series methods are employed to account for the spatiotemporal variability of each trajectory. Because the datasets 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 using semi-parametric 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.

Type: Article
Title: Lagrangian Time Series Models for Ocean Surface Drifter Trajectories
Open access status: An open access version is available from UCL Discovery
DOI: 10.1111/rssc.12112
Publisher version: http://dx.doi.org/10.1111/rssc.12112
Language: English
Additional information: © 2015 Royal Statistical Society
Keywords: Complex-valued time series; Inertial oscillation; Matérn process; Non-stationary processes; Ornstein–Uhlenbeck process; Semiparametric models; Spatiotemporal variability; Surface drifter
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: http://discovery.ucl.ac.uk/id/eprint/1421677
Downloads since deposit
42Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item