MATLAB Software Package for EOF Analysis

EOF analysis serves as a powerful methodological framework for investigating climate data variability and extracting dominant spatial-temporal patterns. This MATLAB implementation requires users to provide anomaly sequences as input data, which can be directly processed by invoking the rest.m function. The core algorithm employs singular value decomposition (SVD) to decompose the covariance matrix of normalized anomalies, generating orthogonal empirical modes that capture maximum variance. Key implementation features include automated anomaly calculation through mean removal, eigenvalue sorting for mode significance ranking, and variance fraction computation for pattern interpretation. The rest.m wrapper function handles data dimension validation, missing value filtration, and output normalization. Users should note that proper anomaly selection critically influences pattern separation quality - detrending and seasonal adjustment should precede analysis for non-stationary data. For optimal results, ensure input matrices follow (time × space) dimensionality with consistent sampling intervals. The package automatically calculates North's significance test for eigenvalue separation and provides scree plot visualization. Recommended pre-processing steps include data standardization and covariance matrix regularization for high-dimensional datasets. Always validate results against physical consistency and consult domain-specific literature for interpretation guidelines.