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Remote Sens. 2018, 10(2), 310; https://doi.org/10.3390/rs10020310

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

1
IMT Atlantique, Lab-STICC, UBL, Brest 29238, France
2
Ifremer, LOPS, Brest 29200, France
*
Author to whom correspondence should be addressed.
Received: 27 November 2017 / Revised: 29 January 2018 / Accepted: 6 February 2018 / Published: 17 February 2018
(This article belongs to the Collection Sea Surface Temperature Retrievals from Remote Sensing)
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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. View Full-Text
Keywords: ocean remote sensing data; data assimilation; optimal interpolation; analog models; multi-scale decomposition; patch-based representation ocean remote sensing data; data assimilation; optimal interpolation; analog models; multi-scale decomposition; patch-based representation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Fablet, R.; Huynh Viet, P.; Lguensat, R.; Horrein, P.-H.; Chapron, B. Spatio-Temporal Interpolation of Cloudy SST Fields Using Conditional Analog Data Assimilation. Remote Sens. 2018, 10, 310.

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