Spatial and temporal variability patterns of sea surface temperature in the Equatorial Pacific
DOI:
https://doi.org/10.15381/rif.v27i3.27369Keywords:
Covariance, modes, SST, eigenvalues, eigenvectorsAbstract
In this research, Empirical Orthogonal Function (EOF) analysis is applied to reduce the number of variables in a sea surface temperature (SST) dataset to a second dataset containing a much smaller number of variables. The condition is that these new variables retain the maximum possible fraction of information from the original dataset. This second dataset is derived by finding the eigenvalues and eigenvectors of the covariance matrix. The objective is to identify the most significant spatial and temporal patterns (principal components) of SST variability in the Equatorial Pacific Ocean (Latitude: 30°N - 30°S, Longitude: 140°E - 70°O) and subsequently associate these patterns (or modes) with phenomena such as the El Niño-Southern Oscillation (ENSO). Finally, to validate the estimated patterns, they are compared with those obtained by national and international institutions.
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Copyright (c) 2024 Edward Alburqueque, Joel Rojas
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