FN Archimer Export Format
PT J
TI Spatio-Temporal Interpolation of Cloudy SST Fields Using Conditional Analog Data Assimilation
BT 
AF FABLET, Ronan
   HUYNH VIET, Phi
   LGUENSAT, Redouane
   HORREIN, Pierre-Henri
   CHAPRON, Bertrand
AS 1:1;2:1;3:1;4:1;5:2;
FF 1:;2:;3:;4:;5:PDG-ODE-LOPS-SIAM;
C1 UBL, IMT Atlantique, Lab STICC, F-29238 Brest, France.
   IFREMER, LOPS, F-29200 Brest, France.
C2 IMT ATLANTIQUE, FRANCE
   IFREMER, FRANCE
SI BREST
SE PDG-ODE-LOPS-SIAM
UM LOPS
IN WOS Ifremer jusqu'en 2018
   DOAJ
   copubli-france
IF 4.118
TC 11
UR https://archimer.ifremer.fr/doc/00426/53806/54741.pdf
LA English
DT Article
DE ;ocean remote sensing data;data assimilation;optimal interpolation;analog models;multi-scale decomposition;patch-based representation
AB 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. 
PY 2018
PD FEB
SO Remote Sensing
SN 2072-4292
PU Mdpi
VL 10
IS 2
UT 000427542100154
DI 10.3390/rs10020310
ID 53806
ER

EF