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 patterns is a ce...
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ftciteseerx:oai:CiteSeerX.psu:10.1.1.367.6935 2023-05-15T17:24:14+02:00 A novel and scalable spatio-temporal technique for ocean eddy monitoring James H. Faghmous Yashu Chamber Shyam Boriah Stefan Liess Vipin Kumar Frode Vikebø The Pennsylvania State University CiteSeerX Archives 2012 application/pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.367.6935 http://www-users.cs.umn.edu/~sboriah/PDFs/FaghmousFCVBLSK2012.pdf en eng http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.367.6935 http://www-users.cs.umn.edu/~sboriah/PDFs/FaghmousFCVBLSK2012.pdf Metadata may be used without restrictions as long as the oai identifier remains attached to it. http://www-users.cs.umn.edu/~sboriah/PDFs/FaghmousFCVBLSK2012.pdf text 2012 ftciteseerx 2016-01-08T01:07:45Z 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 − 80 ◦ N and 20 ◦ W − 20 ◦ E), an area that has received limited attention and has proven to be difficult to analyze using other methods. 1 Text Nordic Sea Unknown |
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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 − 80 ◦ N and 20 ◦ W − 20 ◦ E), an area that has received limited attention and has proven to be difficult to analyze using other methods. 1 |
author2 |
The Pennsylvania State University CiteSeerX Archives |
format |
Text |
author |
James H. Faghmous Yashu Chamber Shyam Boriah Stefan Liess Vipin Kumar Frode Vikebø |
spellingShingle |
James H. Faghmous Yashu Chamber Shyam Boriah Stefan Liess Vipin Kumar Frode Vikebø A novel and scalable spatio-temporal technique for ocean eddy monitoring |
author_facet |
James H. Faghmous Yashu Chamber Shyam Boriah Stefan Liess Vipin Kumar Frode Vikebø |
author_sort |
James H. Faghmous |
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 |
publishDate |
2012 |
url |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.367.6935 http://www-users.cs.umn.edu/~sboriah/PDFs/FaghmousFCVBLSK2012.pdf |
genre |
Nordic Sea |
genre_facet |
Nordic Sea |
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http://www-users.cs.umn.edu/~sboriah/PDFs/FaghmousFCVBLSK2012.pdf |
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http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.367.6935 http://www-users.cs.umn.edu/~sboriah/PDFs/FaghmousFCVBLSK2012.pdf |
op_rights |
Metadata may be used without restrictions as long as the oai identifier remains attached to it. |
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