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Lagrangian time series models for ocean surface drifter trajectories

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Lagrangian time series models for ocean surface drifter trajectories. / Sykulski, Adam M.; Olhede, Sofia C.; Lilly, Jonathan M. et al.
In: Journal of the Royal Statistical Society: Series C (Applied Statistics), Vol. 65, No. 1, 01.2016, p. 29-50.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Sykulski, AM, Olhede, SC, Lilly, JM & Danioux, E 2016, 'Lagrangian time series models for ocean surface drifter trajectories', Journal of the Royal Statistical Society: Series C (Applied Statistics), vol. 65, no. 1, pp. 29-50. https://doi.org/10.1111/rssc.12112

APA

Sykulski, A. M., Olhede, S. C., Lilly, J. M., & Danioux, E. (2016). Lagrangian time series models for ocean surface drifter trajectories. Journal of the Royal Statistical Society: Series C (Applied Statistics), 65(1), 29-50. https://doi.org/10.1111/rssc.12112

Vancouver

Sykulski AM, Olhede SC, Lilly JM, Danioux E. Lagrangian time series models for ocean surface drifter trajectories. Journal of the Royal Statistical Society: Series C (Applied Statistics). 2016 Jan;65(1):29-50. Epub 2015 Jun 23. doi: 10.1111/rssc.12112

Author

Sykulski, Adam M. ; Olhede, Sofia C. ; Lilly, Jonathan M. et al. / Lagrangian time series models for ocean surface drifter trajectories. In: Journal of the Royal Statistical Society: Series C (Applied Statistics). 2016 ; Vol. 65, No. 1. pp. 29-50.

Bibtex

@article{c11e3f8ab5ee4490b43ecf995ab6b245,
title = "Lagrangian time series models for ocean surface drifter trajectories",
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.",
keywords = "Complex-valued time series, Inertial oscillation, Mat{\'e}rn process, Non-stationary processes, Ornstein-Uhlenbeck process, Semiparametric models, Spatiotemporal variability, Surface drifter",
author = "Sykulski, {Adam M.} and Olhede, {Sofia C.} and Lilly, {Jonathan M.} and Eric Danioux",
year = "2016",
month = jan,
doi = "10.1111/rssc.12112",
language = "English",
volume = "65",
pages = "29--50",
journal = "Journal of the Royal Statistical Society: Series C (Applied Statistics)",
issn = "0035-9254",
publisher = "Wiley-Blackwell",
number = "1",

}

RIS

TY - JOUR

T1 - Lagrangian time series models for ocean surface drifter trajectories

AU - Sykulski, Adam M.

AU - Olhede, Sofia C.

AU - Lilly, Jonathan M.

AU - Danioux, Eric

PY - 2016/1

Y1 - 2016/1

N2 - 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.

AB - 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.

KW - Complex-valued time series

KW - Inertial oscillation

KW - Matérn process

KW - Non-stationary processes

KW - Ornstein-Uhlenbeck process

KW - Semiparametric models

KW - Spatiotemporal variability

KW - Surface drifter

U2 - 10.1111/rssc.12112

DO - 10.1111/rssc.12112

M3 - Journal article

AN - SCOPUS:84957847429

VL - 65

SP - 29

EP - 50

JO - Journal of the Royal Statistical Society: Series C (Applied Statistics)

JF - Journal of the Royal Statistical Society: Series C (Applied Statistics)

SN - 0035-9254

IS - 1

ER -