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Directional Spectra-Based Clustering for Visualizing Patterns of Ocean Waves and Winds

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Directional Spectra-Based Clustering for Visualizing Patterns of Ocean Waves and Winds. / Euán, Carolina; Sun, Ying.
In: Journal of Computational and Graphical Statistics, Vol. 0, No. 0, 29.04.2019, p. 1-15.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Euán, C & Sun, Y 2019, 'Directional Spectra-Based Clustering for Visualizing Patterns of Ocean Waves and Winds', Journal of Computational and Graphical Statistics, vol. 0, no. 0, pp. 1-15. https://doi.org/10.1080/10618600.2019.1575745

APA

Vancouver

Euán C, Sun Y. Directional Spectra-Based Clustering for Visualizing Patterns of Ocean Waves and Winds. Journal of Computational and Graphical Statistics. 2019 Apr 29;0(0):1-15. doi: 10.1080/10618600.2019.1575745

Author

Euán, Carolina ; Sun, Ying. / Directional Spectra-Based Clustering for Visualizing Patterns of Ocean Waves and Winds. In: Journal of Computational and Graphical Statistics. 2019 ; Vol. 0, No. 0. pp. 1-15.

Bibtex

@article{368e4146c80347cba25ab03342a7dee0,
title = "Directional Spectra-Based Clustering for Visualizing Patterns of Ocean Waves and Winds",
abstract = "The energy distribution of wind-driven ocean waves is of great interest in marine science. Discovering the generating process of ocean waves is often challenging and the direction is the key for a better understanding. Typically, wave records are transformed into a directional spectrum which provides information about the wave energy distribution across different frequencies and directions. Here, we propose a new time series clustering method for a series of directional spectra to extract the spectral features of ocean waves and develop informative visualization tools to summarize identified wave clusters. We treat directional distributions as functional data of directions and construct a directional functional boxplot to display the main directional distribution of the wave energy within a cluster. We also trace back when these spectra were observed, and we present color-coded clusters on a calendar plot to show their temporal variability. For each identified wave cluster, we analyze wind speed and wind direction hourly to investigate the link between wind data and wave directional spectra. The performance of the proposed clustering method is evaluated by simulations and illustrated by a real-world dataset from the Red Sea. Supplementary materials for this article are available online.",
author = "Carolina Eu{\'a}n and Ying Sun",
year = "2019",
month = apr,
day = "29",
doi = "10.1080/10618600.2019.1575745",
language = "English",
volume = "0",
pages = "1--15",
journal = "Journal of Computational and Graphical Statistics",
issn = "1061-8600",
publisher = "American Statistical Association",
number = "0",

}

RIS

TY - JOUR

T1 - Directional Spectra-Based Clustering for Visualizing Patterns of Ocean Waves and Winds

AU - Euán, Carolina

AU - Sun, Ying

PY - 2019/4/29

Y1 - 2019/4/29

N2 - The energy distribution of wind-driven ocean waves is of great interest in marine science. Discovering the generating process of ocean waves is often challenging and the direction is the key for a better understanding. Typically, wave records are transformed into a directional spectrum which provides information about the wave energy distribution across different frequencies and directions. Here, we propose a new time series clustering method for a series of directional spectra to extract the spectral features of ocean waves and develop informative visualization tools to summarize identified wave clusters. We treat directional distributions as functional data of directions and construct a directional functional boxplot to display the main directional distribution of the wave energy within a cluster. We also trace back when these spectra were observed, and we present color-coded clusters on a calendar plot to show their temporal variability. For each identified wave cluster, we analyze wind speed and wind direction hourly to investigate the link between wind data and wave directional spectra. The performance of the proposed clustering method is evaluated by simulations and illustrated by a real-world dataset from the Red Sea. Supplementary materials for this article are available online.

AB - The energy distribution of wind-driven ocean waves is of great interest in marine science. Discovering the generating process of ocean waves is often challenging and the direction is the key for a better understanding. Typically, wave records are transformed into a directional spectrum which provides information about the wave energy distribution across different frequencies and directions. Here, we propose a new time series clustering method for a series of directional spectra to extract the spectral features of ocean waves and develop informative visualization tools to summarize identified wave clusters. We treat directional distributions as functional data of directions and construct a directional functional boxplot to display the main directional distribution of the wave energy within a cluster. We also trace back when these spectra were observed, and we present color-coded clusters on a calendar plot to show their temporal variability. For each identified wave cluster, we analyze wind speed and wind direction hourly to investigate the link between wind data and wave directional spectra. The performance of the proposed clustering method is evaluated by simulations and illustrated by a real-world dataset from the Red Sea. Supplementary materials for this article are available online.

U2 - 10.1080/10618600.2019.1575745

DO - 10.1080/10618600.2019.1575745

M3 - Journal article

VL - 0

SP - 1

EP - 15

JO - Journal of Computational and Graphical Statistics

JF - Journal of Computational and Graphical Statistics

SN - 1061-8600

IS - 0

ER -