Unsupervised classification of the Northwestern European seas based on satellite altimetry data

From generating metrics representative of a wide region to saving costs by reducing the density of an observational network, the reasons to split the ocean into distinct regions are many. Traditionally, this has been done somewhat arbitrarily, using the bathymetry and potentially some artificial lat...

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Main Authors: Poropat, Lea, Jones, Dan(i), Thomas, Simon D. A., Heuzé, Céline
Format: Article in Journal/Newspaper
Language:English
Published: Copernicus Publications 2023
Subjects:
Online Access:https://doi.org/10.5194/egusphere-2023-1468
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spelling ftnonlinearchiv:oai:noa.gwlb.de:cop_mods_00067705 2023-07-30T04:05:56+02:00 Unsupervised classification of the Northwestern European seas based on satellite altimetry data Poropat, Lea Jones, Dan(i) Thomas, Simon D. A. Heuzé, Céline 2023-07 electronic https://doi.org/10.5194/egusphere-2023-1468 https://noa.gwlb.de/receive/cop_mods_00067705 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00066152/egusphere-2023-1468.pdf https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1468/egusphere-2023-1468.pdf eng eng Copernicus Publications https://doi.org/10.5194/egusphere-2023-1468 https://noa.gwlb.de/receive/cop_mods_00067705 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00066152/egusphere-2023-1468.pdf https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1468/egusphere-2023-1468.pdf https://creativecommons.org/licenses/by/4.0/ uneingeschränkt info:eu-repo/semantics/openAccess article Verlagsveröffentlichung article Text doc-type:article 2023 ftnonlinearchiv https://doi.org/10.5194/egusphere-2023-1468 2023-07-16T23:19:15Z From generating metrics representative of a wide region to saving costs by reducing the density of an observational network, the reasons to split the ocean into distinct regions are many. Traditionally, this has been done somewhat arbitrarily, using the bathymetry and potentially some artificial latitude/longitude boundaries. We use an ensemble of Gaussian Mixture Models (GMM, unsupervised classification) to separate the complex northwestern European coastal region into classes based on sea level variability observed by satellite altimetry. To reduce the dimensionality of the data, we perform a principal component analysis on 25 years of observations and use the spatial components as input for the GMM. The number of classes or mixture components is determined by locating the maximum of the silhouette score and by testing several models. We use an ensemble approach to increase the robustness of the classification and to allow the separation into more regions than a single GMM can achieve. We also vary the number of empirical orthogonal function maps (EOFs) and show that more EOFs result in a more detailed classification. With three EOFs, the area is classified into four distinct regions delimited mainly by bathymetry. Adding more EOFs results in further subdivisions that resemble oceanic fronts. To achieve a more detailed separation, we use a model focused on smaller regions, specifically the Baltic Sea, North Sea, and the Norwegian Sea. Article in Journal/Newspaper Norwegian Sea Niedersächsisches Online-Archiv NOA Norwegian Sea
institution Open Polar
collection Niedersächsisches Online-Archiv NOA
op_collection_id ftnonlinearchiv
language English
topic article
Verlagsveröffentlichung
spellingShingle article
Verlagsveröffentlichung
Poropat, Lea
Jones, Dan(i)
Thomas, Simon D. A.
Heuzé, Céline
Unsupervised classification of the Northwestern European seas based on satellite altimetry data
topic_facet article
Verlagsveröffentlichung
description From generating metrics representative of a wide region to saving costs by reducing the density of an observational network, the reasons to split the ocean into distinct regions are many. Traditionally, this has been done somewhat arbitrarily, using the bathymetry and potentially some artificial latitude/longitude boundaries. We use an ensemble of Gaussian Mixture Models (GMM, unsupervised classification) to separate the complex northwestern European coastal region into classes based on sea level variability observed by satellite altimetry. To reduce the dimensionality of the data, we perform a principal component analysis on 25 years of observations and use the spatial components as input for the GMM. The number of classes or mixture components is determined by locating the maximum of the silhouette score and by testing several models. We use an ensemble approach to increase the robustness of the classification and to allow the separation into more regions than a single GMM can achieve. We also vary the number of empirical orthogonal function maps (EOFs) and show that more EOFs result in a more detailed classification. With three EOFs, the area is classified into four distinct regions delimited mainly by bathymetry. Adding more EOFs results in further subdivisions that resemble oceanic fronts. To achieve a more detailed separation, we use a model focused on smaller regions, specifically the Baltic Sea, North Sea, and the Norwegian Sea.
format Article in Journal/Newspaper
author Poropat, Lea
Jones, Dan(i)
Thomas, Simon D. A.
Heuzé, Céline
author_facet Poropat, Lea
Jones, Dan(i)
Thomas, Simon D. A.
Heuzé, Céline
author_sort Poropat, Lea
title Unsupervised classification of the Northwestern European seas based on satellite altimetry data
title_short Unsupervised classification of the Northwestern European seas based on satellite altimetry data
title_full Unsupervised classification of the Northwestern European seas based on satellite altimetry data
title_fullStr Unsupervised classification of the Northwestern European seas based on satellite altimetry data
title_full_unstemmed Unsupervised classification of the Northwestern European seas based on satellite altimetry data
title_sort unsupervised classification of the northwestern european seas based on satellite altimetry data
publisher Copernicus Publications
publishDate 2023
url https://doi.org/10.5194/egusphere-2023-1468
https://noa.gwlb.de/receive/cop_mods_00067705
https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00066152/egusphere-2023-1468.pdf
https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1468/egusphere-2023-1468.pdf
geographic Norwegian Sea
geographic_facet Norwegian Sea
genre Norwegian Sea
genre_facet Norwegian Sea
op_relation https://doi.org/10.5194/egusphere-2023-1468
https://noa.gwlb.de/receive/cop_mods_00067705
https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00066152/egusphere-2023-1468.pdf
https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1468/egusphere-2023-1468.pdf
op_rights https://creativecommons.org/licenses/by/4.0/
uneingeschränkt
info:eu-repo/semantics/openAccess
op_doi https://doi.org/10.5194/egusphere-2023-1468
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