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Spatio-Temporal Interpolation of Cloudy SST Fields Using Conditional Analog Data Assimilation.

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Title: Spatio-Temporal Interpolation of Cloudy SST Fields Using Conditional Analog Data Assimilation.
Authors: Fablet, Ronan1 ronan.fablet@imt-atlantique.fr, Huynh Viet, Phi1, Lguensat, Redouane1, Horrein, Pierre-Henri1, Chapron, Bertrand2 bchapron@ifremer.fr
Source: Remote Sensing. Feb2018, Vol. 10 Issue 2, p310. 14p.
Subjects: Analog data, Ocean temperature, Imaging systems in geophysics, Natural satellites, Numerical analysis, Big data, Remote sensing
Abstract: The ever increasing geophysical data streams pouring from earth observation satellite missions and numerical simulations along with the development of dedicated big data infrastructure advocate for truly exploiting the potential of these datasets, through novel data-driven strategies, to deliver enhanced satellite-derived gapfilled geophysical products from partial satellite observations. We here demonstrate the relevance of the analog data assimilation (AnDA) for an application to the reconstruction of cloud-free level-4 gridded Sea Surface Temperature (SST).We propose novel AnDA models which exploit auxiliary variables such as sea surface currents and significantly reduce the computational complexity of AnDA. Numerical experiments benchmark the proposed models with respect to state-of-the-art interpolation techniques such as optimal interpolation and EOF-based schemes. We report relative improvement up to 40%/50% in terms of RMSE and also show a good parallelization performance, which supports the feasibility of an upscaling on a global scale. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI 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.)
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  Data: <searchLink fieldCode="DE" term="%22Analog+data%22">Analog data</searchLink><br /><searchLink fieldCode="DE" term="%22Ocean+temperature%22">Ocean temperature</searchLink><br /><searchLink fieldCode="DE" term="%22Imaging+systems+in+geophysics%22">Imaging systems in geophysics</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+satellites%22">Natural satellites</searchLink><br /><searchLink fieldCode="DE" term="%22Numerical+analysis%22">Numerical analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Big+data%22">Big data</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink>
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  Data: The ever increasing geophysical data streams pouring from earth observation satellite missions and numerical simulations along with the development of dedicated big data infrastructure advocate for truly exploiting the potential of these datasets, through novel data-driven strategies, to deliver enhanced satellite-derived gapfilled geophysical products from partial satellite observations. We here demonstrate the relevance of the analog data assimilation (AnDA) for an application to the reconstruction of cloud-free level-4 gridded Sea Surface Temperature (SST).We propose novel AnDA models which exploit auxiliary variables such as sea surface currents and significantly reduce the computational complexity of AnDA. Numerical experiments benchmark the proposed models with respect to state-of-the-art interpolation techniques such as optimal interpolation and EOF-based schemes. We report relative improvement up to 40%/50% in terms of RMSE and also show a good parallelization performance, which supports the feasibility of an upscaling on a global scale. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Remote Sensing is the property of MDPI 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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        Value: 10.3390/rs10020310
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        Text: English
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      – SubjectFull: Ocean temperature
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      – SubjectFull: Imaging systems in geophysics
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      – SubjectFull: Natural satellites
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              Text: Feb2018
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