EOF Calculations and Data Filling from Incomplete Oceanographic Datasets*

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Abstract
The paper presents a new self-consistent method to infer missing data from oceanographic data series and to extract the relevant empirical orthogonal functions. As a by-product, the new method allows for the detection of the number of statistically significant EOFs by a cross-validation procedure for a complete or incomplete dataset, as well as the noise level and interpolation error. Since the proposed filling and analysis method does not need a priori information about the error covariance structure, the method is self-consistent and parameter free. Abstract The paper presents a new self-consistent method to infer missing data from oceanographic data series and to extract the relevant empirical orthogonal functions. As a by-product, the new method allows for the detection of the number of statistically significant EOFs by a cross-validation procedure for a complete or incomplete dataset, as well as the noise level and interpolation error. Since the proposed filling and analysis method does not need a priori information about the error covariance structure, the method is self-consistent and parameter free.