A Novel and Scalable Spatio-Temporal Technique for Ocean Eddy Monitoring
Swirls of ocean currents known as ocean eddies are a crucial component of the ocean's dynamics. In addition to dominating the ocean's kinetic energy, eddies play a significant role in the transport of water, salt, heat, and nutrients. Therefore, understanding current and future eddy patter...
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ftjaaai:oai:ojs.aaai.org:article/8181 2023-05-15T17:24:14+02:00 A Novel and Scalable Spatio-Temporal Technique for Ocean Eddy Monitoring Faghmous, James Chamber, Yashu Boriah, Shyam Vikebø, Frode Liess, Stefan dos Santos Mesquita, Michel Kumar, Vipin 2021-09-20 application/pdf https://ojs.aaai.org/index.php/AAAI/article/view/8181 https://doi.org/10.1609/aaai.v26i1.8181 eng eng Association for the Advancement of Artificial Intelligence https://ojs.aaai.org/index.php/AAAI/article/view/8181/8039 https://ojs.aaai.org/index.php/AAAI/article/view/8181 doi:10.1609/aaai.v26i1.8181 Copyright (c) 2021 Proceedings of the AAAI Conference on Artificial Intelligence Proceedings of the AAAI Conference on Artificial Intelligence; Vol. 26 No. 1 (2012): Twenty-Sixth AAAI Conference on Artificial Intelligence; 281-287 2374-3468 2159-5399 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 2021 ftjaaai https://doi.org/10.1609/aaai.v26i1.8181 2022-07-02T23:29:06Z Swirls of ocean currents known as ocean eddies are a crucial component of the ocean's dynamics. In addition to dominating the ocean's kinetic energy, eddies play a significant role in the transport of water, salt, heat, and nutrients. Therefore, understanding current and future eddy patterns is a central climate challenge to address future sustainability of marine ecosystems. The emergence of sea surface height observations from satellite radar altimeter has recently enabled researchers to track eddies at a global scale. The majority of studies that identify eddies from observational data employ highly parametrized connected component algorithms using expert filtered data, effectively making reproducibility and scalability challenging. In this paper, we frame the challenge of monitoring ocean eddies as an unsupervised learning problem. We present a novel change detection algorithm that automatically identifies and monitors eddies in sea surface height data based on heuristics derived from basic eddy properties. Our method is accurate, efficient, and scalable. To demonstrate its performance we analyze eddy activity in the Nordic Sea (60-80N and 20W-20E), an area that has received limited attention and has proven to be difficult to analyze using other methods. Article in Journal/Newspaper Nordic Sea AAAI Publications (Association for the Advancement of Artificial Intelligence) Proceedings of the AAAI Conference on Artificial Intelligence 26 1 281 287 |
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AAAI Publications (Association for the Advancement of Artificial Intelligence) |
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ftjaaai |
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English |
description |
Swirls of ocean currents known as ocean eddies are a crucial component of the ocean's dynamics. In addition to dominating the ocean's kinetic energy, eddies play a significant role in the transport of water, salt, heat, and nutrients. Therefore, understanding current and future eddy patterns is a central climate challenge to address future sustainability of marine ecosystems. The emergence of sea surface height observations from satellite radar altimeter has recently enabled researchers to track eddies at a global scale. The majority of studies that identify eddies from observational data employ highly parametrized connected component algorithms using expert filtered data, effectively making reproducibility and scalability challenging. In this paper, we frame the challenge of monitoring ocean eddies as an unsupervised learning problem. We present a novel change detection algorithm that automatically identifies and monitors eddies in sea surface height data based on heuristics derived from basic eddy properties. Our method is accurate, efficient, and scalable. To demonstrate its performance we analyze eddy activity in the Nordic Sea (60-80N and 20W-20E), an area that has received limited attention and has proven to be difficult to analyze using other methods. |
format |
Article in Journal/Newspaper |
author |
Faghmous, James Chamber, Yashu Boriah, Shyam Vikebø, Frode Liess, Stefan dos Santos Mesquita, Michel Kumar, Vipin |
spellingShingle |
Faghmous, James Chamber, Yashu Boriah, Shyam Vikebø, Frode Liess, Stefan dos Santos Mesquita, Michel Kumar, Vipin A Novel and Scalable Spatio-Temporal Technique for Ocean Eddy Monitoring |
author_facet |
Faghmous, James Chamber, Yashu Boriah, Shyam Vikebø, Frode Liess, Stefan dos Santos Mesquita, Michel Kumar, Vipin |
author_sort |
Faghmous, James |
title |
A Novel and Scalable Spatio-Temporal Technique for Ocean Eddy Monitoring |
title_short |
A Novel and Scalable Spatio-Temporal Technique for Ocean Eddy Monitoring |
title_full |
A Novel and Scalable Spatio-Temporal Technique for Ocean Eddy Monitoring |
title_fullStr |
A Novel and Scalable Spatio-Temporal Technique for Ocean Eddy Monitoring |
title_full_unstemmed |
A Novel and Scalable Spatio-Temporal Technique for Ocean Eddy Monitoring |
title_sort |
novel and scalable spatio-temporal technique for ocean eddy monitoring |
publisher |
Association for the Advancement of Artificial Intelligence |
publishDate |
2021 |
url |
https://ojs.aaai.org/index.php/AAAI/article/view/8181 https://doi.org/10.1609/aaai.v26i1.8181 |
genre |
Nordic Sea |
genre_facet |
Nordic Sea |
op_source |
Proceedings of the AAAI Conference on Artificial Intelligence; Vol. 26 No. 1 (2012): Twenty-Sixth AAAI Conference on Artificial Intelligence; 281-287 2374-3468 2159-5399 |
op_relation |
https://ojs.aaai.org/index.php/AAAI/article/view/8181/8039 https://ojs.aaai.org/index.php/AAAI/article/view/8181 doi:10.1609/aaai.v26i1.8181 |
op_rights |
Copyright (c) 2021 Proceedings of the AAAI Conference on Artificial Intelligence |
op_doi |
https://doi.org/10.1609/aaai.v26i1.8181 |
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Proceedings of the AAAI Conference on Artificial Intelligence |
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26 |
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1 |
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281 |
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287 |
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