Predictability of the Loop Current Variation and Eddy Shedding Process in the Gulf of Mexico Using an Artificial Neural Network Approach.
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| Title: | Predictability of the Loop Current Variation and Eddy Shedding Process in the Gulf of Mexico Using an Artificial Neural Network Approach. |
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| Authors: | Zeng, Xiangming, Li, Yizhen, He, Ruoying |
| Source: | Journal of Atmospheric & Oceanic Technology. May2015, Vol. 32 Issue 5, p1098-1111. 14p. 1 Chart, 8 Graphs, 2 Maps. |
| Subjects: | Loop Current, Ocean currents, Eddy currents (Electric), Electric currents, Artificial neural networks |
| Geographic Terms: | Gulf of Mexico |
| Abstract: | A novel approach based on an artificial neural network was used to forecast sea surface height (SSH) in the Gulf of Mexico (GoM) in order to predict Loop Current variation and its eddy shedding process. The empirical orthogonal function analysis method was applied to decompose long-term satellite-observed SSH into spatial patterns (EOFs) and time-dependent principal components (PCs). The nonlinear autoregressive network was then developed to predict major PCs of the GoM SSH in the future. The prediction of SSH in the GoM was constructed by multiplying the EOFs and predicted PCs. Model sensitivity experiments were conducted to determine the optimal number of PCs. Validations against independent satellite observations indicate that the neural network-based model can reliably predict Loop Current variations and its eddy shedding process for a 4-week period. In some cases, an accurate forecast for 5-6 weeks is possible. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Atmospheric & Oceanic Technology is the property of American Meteorological Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 102747892 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Predictability of the Loop Current Variation and Eddy Shedding Process in the Gulf of Mexico Using an Artificial Neural Network Approach. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zeng%2C+Xiangming%22">Zeng, Xiangming</searchLink><br /><searchLink fieldCode="AR" term="%22Li%2C+Yizhen%22">Li, Yizhen</searchLink><br /><searchLink fieldCode="AR" term="%22He%2C+Ruoying%22">He, Ruoying</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Atmospheric+%26+Oceanic+Technology%22">Journal of Atmospheric & Oceanic Technology</searchLink>. May2015, Vol. 32 Issue 5, p1098-1111. 14p. 1 Chart, 8 Graphs, 2 Maps. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Loop+Current%22">Loop Current</searchLink><br /><searchLink fieldCode="DE" term="%22Ocean+currents%22">Ocean currents</searchLink><br /><searchLink fieldCode="DE" term="%22Eddy+currents+%28Electric%29%22">Eddy currents (Electric)</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+currents%22">Electric currents</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Gulf+of+Mexico%22">Gulf of Mexico</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: A novel approach based on an artificial neural network was used to forecast sea surface height (SSH) in the Gulf of Mexico (GoM) in order to predict Loop Current variation and its eddy shedding process. The empirical orthogonal function analysis method was applied to decompose long-term satellite-observed SSH into spatial patterns (EOFs) and time-dependent principal components (PCs). The nonlinear autoregressive network was then developed to predict major PCs of the GoM SSH in the future. The prediction of SSH in the GoM was constructed by multiplying the EOFs and predicted PCs. Model sensitivity experiments were conducted to determine the optimal number of PCs. Validations against independent satellite observations indicate that the neural network-based model can reliably predict Loop Current variations and its eddy shedding process for a 4-week period. In some cases, an accurate forecast for 5-6 weeks is possible. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Atmospheric & Oceanic Technology is the property of American Meteorological Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1175/JTECH-D-14-00176.1 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 1098 Subjects: – SubjectFull: Loop Current Type: general – SubjectFull: Ocean currents Type: general – SubjectFull: Eddy currents (Electric) Type: general – SubjectFull: Electric currents Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Gulf of Mexico Type: general Titles: – TitleFull: Predictability of the Loop Current Variation and Eddy Shedding Process in the Gulf of Mexico Using an Artificial Neural Network Approach. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zeng, Xiangming – PersonEntity: Name: NameFull: Li, Yizhen – PersonEntity: Name: NameFull: He, Ruoying IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2015 Type: published Y: 2015 Identifiers: – Type: issn-print Value: 07390572 Numbering: – Type: volume Value: 32 – Type: issue Value: 5 Titles: – TitleFull: Journal of Atmospheric & Oceanic Technology Type: main |
| ResultId | 1 |
