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Cited by 30Oceanographic and Atmospheric ProcessesOcean Waves and Remote SensingArctic and Antarctic ice dynamics

EddyScan: A physically consistent ocean eddy monitoring application

Faghmous, James H., Styles, Luke, Mithal, Varun, Boriah, Shyam, Liess, Stefan, Kumar, Vipin, Vikebo, Frode, Mesquita, Michel dos Santos·2012·2012 Conference on Intelligent Data Understanding
DOI 10.1109/cidu.2012.6382189
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Abstract

Rotating coherent structures of water known as ocean eddies are the oceanic analog of storms in the atmosphere and a crucial component of ocean 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 activity is a central 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 improve upon the state-of-the-art connected component eddy monitoring algorithms to track eddies globally. This work makes three main contributions: first, we do not pre-process the data therefore minimizing the risk of wiping out important signals within the data. Second, we employ a physically-consistent convexity requirement on eddies based on theoretical and empirical studies to improve the accuracy and computational complexity of our method from quadratic to linear time in the size of each eddy. Finally, we accurately separate eddies that are in close spatial proximity, something existing methods cannot accomplish. We compare our results to those of the state of the art and discuss the impact of our improvements on the difference in results.

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Crossref · 10.1109/cidu.2012.6382189

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19 of 19 references verified0 citation errors · 0 not checkable

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  • The use of physical principles to constrain spatiotemporal pattern mining algorithms has been explored for finding ocean eddies from satellite data.

    “For example, the use of physical principles to constrain spatiotemporal pattern mining algorithms has been explored in [81], [82] for finding ocean eddies from satellite data.”

    From Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data (2017) · cites this paper as [81]

  • Faghmous et al. showed that large-scale pattern mining algorithms can discover false patterns if not constrained by basic theoretical facts.

    “Faghmous et al. 31 showed that largescale pattern mining algorithms can discover false patterns if not constrained by basic theoretical facts.”

    From A Big Data Guide to Understanding Climate Change: The Case for Theory-Guided Data Science (2014) · cites this paper as [31]

  • This physical consistency constrained a simple pattern-mining algorithm to produce more accurate features compared to methods that routinely merge eddies and could be identified as features with two or more extrema.

    “This physical consistency constrained a simple pattern-mining algorithm to produce more accurate features compared to methods that routinely merge eddies and could be identified as features with two or more extrema.”

    From A Big Data Guide to Understanding Climate Change: The Case for Theory-Guided Data Science (2014) · cites this paper as [31]

  • In their study, Faghmous et al. developed an unsupervised method to identify coherent rotating structures in the ocean known as ocean eddies.

    “In their study, the authors developed an unsupervised method to identify coherent rotating structures in the ocean known as ocean eddies.”

    From A Big Data Guide to Understanding Climate Change: The Case for Theory-Guided Data Science (2014) · cites this paper as [31]

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Related

Authors
Faghmous, J. H., Styles, L., Mithal, V., Boriah, S., Liess, S., Kumar, V., Vikebo, F., & Mesquita, M. D. S.
Affiliations
  • University of Minnesota (US)
  • Bjerknes Centre for Climate Research (NO)
ORCID
Venue
2012 Conference on Intelligent Data Understanding
Publisher
IEEE
Published
2012-10
Pages
96-103
DOI
10.1109/cidu.2012.6382189
OpenAlex
W1990633230
Cited by
29
References
20
Citations by year
2026: 1 · 2025: 2 · 2023: 2 · 2022: 2 · 2021: 2 · 2020: 3 · 2019: 1 · 2018: 1 · 2017: 2 · 2016: 1 · 2015: 3 · 2014: 5 · 2013: 4
FWCI
27.88
Type
conference paper
Language
en
Open access
closed
Locations
Conference on Intelligent Data Understanding (publishedVersion)
Topics
  • Physical Sciences › Earth and Planetary Sciences › Oceanography › Oceanographic and Atmospheric Processes
  • Physical Sciences › Earth and Planetary Sciences › Oceanography › Ocean Waves and Remote Sensing
  • Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Arctic and Antarctic ice dynamics
Keywords
ocean eddies, eddy tracking, sea surface height, satellite radar altimetry, convexity constraints, ocean dynamics, maritime transport

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