PROJECT TITLE :

Reconstruction of Satellite-Derived Sea Surface Temperature Data Based on an Improved DINEOF Algorithm

ABSTRACT:

An improved knowledge interpolating empirical orthogonal operate (I-DINEOF) algorithm was proposed during this study. Compared with the standard DINEOF algorithm, within the I-DINEOF algorithm, the present information are not necessary to be selected for cross-validation and also the initial matrix is directly used for reconstruction. Rather than using single EOF to reconstruct the full spatio-temporal matrix, the initial matrix is split into many subareas and every subarea is reconstructed by the most appropriate EOF. To validate the accuracy of the I-DINEOF algorithm, a real sea surface temperature (SST) data set and three artificial data sets with different missing data percentage are reconstructed by using the DINEOF and i-DINEOF algorithms. Four parameters (Pearson correlation coefficient, signal-to-noise ratio, root-mean-sq. error, and mean absolute distinction) are used as a live of reconstructed accuracy. Compared with the DINEOF algorithm, the I-DINEOF algorithm is less stricken by the missing information and will considerably enhance the accuracy of reconstruction.


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