Spatio-Temporal Interpolation of Cloudy SST Fields Using Conditional Analog Data Assimilation
|Author(s)||Fablet Ronan1, Huynh Viet Phi1, Lguensat Redouane1, Horrein Pierre-Henri1, Chapron Bertrand2|
|Affiliation(s)||1 : UBL, IMT Atlantique, Lab STICC, F-29238 Brest, France.
2 : IFREMER, LOPS, F-29200 Brest, France.
|Source||Remote Sensing (2072-4292) (Mdpi), 2018-02 , Vol. 10 , N. 2 , P. 310 (14p.)|
|WOS© Times Cited||10|
|Note||This article belongs to the Collection Sea Surface Temperature Retrievals from Remote Sensing|
|Keyword(s)||ocean remote sensing data, data assimilation, optimal interpolation, analog models, multi-scale decomposition, patch-based representation|
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.