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A neural network model for predicting the bulk-skin temperature difference at the sea surface.

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Title: A neural network model for predicting the bulk-skin temperature difference at the sea surface.
Authors: Ward, Brian, Redfern, Sam
Source: International Journal of Remote Sensing. 12/15/99, Vol. 20 Issue 18, p3533-3548. 16p. 2 Diagrams, 3 Charts, 16 Graphs.
Subjects: Artificial neural networks, Ocean temperature, Measurement
Geographic Terms: North Sea
Abstract: Night-time radiometric sea surface temperature (SST) observations were carried out on a research platform in the North Sea during the second campaign of the ASGAMAGE experiment. An extensive series of atmospheric measurements was also made, allowing a comparison between measurements of the bulk-skin temperature difference, Delta T, and several current theoretical models. An artificial neural network (ANN) was empirically designed and trained on a subset of the net heat flux and wind speed parameters. The remaining dataset was then applied to the output of the ANN. The neural network-based model reproduced the observed Delta T values with a higher level of accuracy than any of the other current models. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Remote Sensing is the property of Taylor & Francis Ltd 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
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  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 3860427
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
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  Data: A neural network model for predicting the bulk-skin temperature difference at the sea surface.
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  Data: <searchLink fieldCode="AR" term="%22Ward%2C+Brian%22">Ward, Brian</searchLink><br /><searchLink fieldCode="AR" term="%22Redfern%2C+Sam%22">Redfern, Sam</searchLink>
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Remote+Sensing%22">International Journal of Remote Sensing</searchLink>. 12/15/99, Vol. 20 Issue 18, p3533-3548. 16p. 2 Diagrams, 3 Charts, 16 Graphs.
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  Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Ocean+temperature%22">Ocean temperature</searchLink><br /><searchLink fieldCode="DE" term="%22Measurement%22">Measurement</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22North+Sea%22">North Sea</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: Night-time radiometric sea surface temperature (SST) observations were carried out on a research platform in the North Sea during the second campaign of the ASGAMAGE experiment. An extensive series of atmospheric measurements was also made, allowing a comparison between measurements of the bulk-skin temperature difference, Delta T, and several current theoretical models. An artificial neural network (ANN) was empirically designed and trained on a subset of the net heat flux and wind speed parameters. The remaining dataset was then applied to the output of the ANN. The neural network-based model reproduced the observed Delta T values with a higher level of accuracy than any of the other current models. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Remote Sensing is the property of Taylor & Francis Ltd 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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    Identifiers:
      – Type: doi
        Value: 10.1080/014311699211183
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 16
        StartPage: 3533
    Subjects:
      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Ocean temperature
        Type: general
      – SubjectFull: Measurement
        Type: general
      – SubjectFull: North Sea
        Type: general
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      – TitleFull: A neural network model for predicting the bulk-skin temperature difference at the sea surface.
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            NameFull: Ward, Brian
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            NameFull: Redfern, Sam
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            – D: 15
              M: 12
              Text: 12/15/99
              Type: published
              Y: 1999
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            – TitleFull: International Journal of Remote Sensing
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