1976
DOI: 10.1175/1520-0485(1976)006<0671:eoaops>2.0.co;2
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Empirical Orthogonal Analysis of Pacific Sea Surface Temperatures

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Cited by 216 publications
(89 citation statements)
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“…Several investigators (e.g. Newell, 1983 andWeare et al, 1976) refer to EOFl as the El Niño mode, however, it is important to point out that EOFl actually represents a global mode because it is not confined to the Pacific Ocean alone nor does it represent only El Niño time-and space-scale SST anomalies; nevertheless, the spatial pattern of EOFl largely corresponds to the El Niño-type of SST anomaly structure because the pattern depicts warm water over the eastern equatorial Pacific and cold water to the west and polarward of this region, (Fig. 2a).…”
Section: Resultsmentioning
confidence: 99%
“…Several investigators (e.g. Newell, 1983 andWeare et al, 1976) refer to EOFl as the El Niño mode, however, it is important to point out that EOFl actually represents a global mode because it is not confined to the Pacific Ocean alone nor does it represent only El Niño time-and space-scale SST anomalies; nevertheless, the spatial pattern of EOFl largely corresponds to the El Niño-type of SST anomaly structure because the pattern depicts warm water over the eastern equatorial Pacific and cold water to the west and polarward of this region, (Fig. 2a).…”
Section: Resultsmentioning
confidence: 99%
“…2(b). Weare et al (1976) attempted a similar analysis on the monthly mean SST data during the period from 1949 to 1973. Compared with the results of their analysis on the non-seasonal SST variations, in the first component, its spatial pattern is slightly different from their result in the area of the eastern Equatorial Pacific.…”
Section: Dominant Sst Anomaly Patternsmentioning
confidence: 96%
“…It is also referred to as Principal Component Analysis (PCA), and this technique has a long history of application to meteorological data (Lorenz, 1956). EOF analysis enables fields of highly correlated data to be represented adequately by small number of orthogonal functions and a corresponding time-series of coefficients (Weare et al, 1976). This method provides an effective means to identify the dominant spatial and temporal patterns from series of remote sensing data (Nezlin and McWilliams, 2003;Emilie and Francis, 2009).…”
Section: Empirical Orthogonal Function Analysismentioning
confidence: 99%