TY - JOUR T1 - Spatio-Temporal Interpolation of Cloudy SST Fields Using Conditional Analog Data Assimilation A1 - Fablet,Ronan A1 - Huynh Viet,Phi A1 - Lguensat,Redouane A1 - Horrein,Pierre-Henri A1 - Chapron,Bertrand AD - UBL, IMT Atlantique, Lab STICC, F-29238 Brest, France. AD - IFREMER, LOPS, F-29200 Brest, France. UR - https://archimer.ifremer.fr/doc/00426/53806/ DO - 10.3390/rs10020310 KW - ocean remote sensing data KW - data assimilation KW - optimal interpolation KW - analog models KW - multi-scale decomposition KW - patch-based representation N2 - 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. Y1 - 2018/02 PB - Mdpi JF - Remote Sensing SN - 2072-4292 VL - 10 IS - 2 ID - 53806 ER -