An empirical orthogonal function analysis of remotely sensed sea surface temperature variability and its relation to interior oceanic processes off Baja California

Gallaudet, Timothy C.; Simpson, Joseph

doi:10.1016/0034-4257(94)90105-8

Cited by 44 publications

(16 citation statements)

References 34 publications

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“…It is clear from the table that the fraction of variance explained by the first eigenfunction varies significantly from study to study, with the value (80%) obtained in this study near the middle of the range. Except for the Gallaudet and Simpson (1994) results, the fraction of the variance explained by the first EOF tends to decrease with the increase in the number of temporal degrees of freedom as one would expect. Also as one might expect, the fraction of variance explained by the first EOF is much higher for semienclosed regions: the Gulf of California (Paden et al 1991) and the Santa Barbara Channel (Lagerloef and Bernstein 1988) as compared with open ocean areas.…”

Section: Results: Spline Interpolated Datamentioning

confidence: 78%

“…This is because a strong seasonal thermocline forms in the shelf water concentrating the heating to a relatively shallow surface layer when compared with the Sargasso Sea (Cornillon et al 1987b). [Rapid warming of shelf waters compared to adjacent deep water has been observed by others (Kelly 1985;Lagerloef 1992;Gallaudet and Simpson 1994;Fang and Hsieh 1993) (although in the second mode); and (Paden et al 1991). ] The rate of warming observed here, approximately 15ЊC over the 4-month period, is similar to the climatological warming of 15ЊC observed VOLUME 27 by Bunker (1976) for 1 April through 30 June.…”

Section: A Empirical Eigenfunctionmentioning

confidence: 99%

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Empirical eigenfunction analysis of sea surface temperatures in the Western North Atlantic

Everson

Cornillon

Sirovich

et al. 1996

AIP Conference Proceedings

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The empirical orthogonal function decomposition is used to analyze time records of AVHRR sea surface temperature observations of the western North Atlantic from 32.9Њ to 43.6ЊN, 62.7Њ to 76.3ЊW. A manually declouded dataset covering the spring of 1985 is analyzed. The majority (80%) of the variance about the mean is accounted for by an empirical eigenfunction, which is identified with seasonal warming. This eigenfunction shows that the shelf water, excluding Georges Bank, warms the most rapidly; the surface water of the Gulf of Maine warms a little less rapidly and the Gulf Stream and Sargasso Sea surface water warm the least rapidly. The SST of the Gulf Stream is also shown to behave more like that at 30ЊN than like Sargasso Sea water immediately to its south (ϳ35ЊN). The second EOF is found to be a small correction to the general warming rate described by the first EOF. The third and fourth EOFs are determined primarily by meander propagation. Observations with partial cloud cover from the period 1985 to 1991 are also analyzed. Again, the dominant effect is identified as seasonal warming.

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Section: Results: Spline Interpolated Datamentioning

confidence: 78%

Section: A Empirical Eigenfunctionmentioning

confidence: 99%

Empirical eigenfunction analysis of sea surface temperatures in the Western North Atlantic

Everson

Cornillon

Sirovich

et al. 1996

AIP Conference Proceedings

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“…Usually in the analysis of SST the first dominant mode in the decomposition after the temporal mean has been removed is associated with the seasonal heating signal (Gallaude and Simpson, 1994). However, because the data sets used in this study are only for the spring season, little seasonal heating appears there.…”

Section: Temporal Eofsmentioning

confidence: 99%

“…Among the several available methods of satellite data analysis, EOF analysis is a particularly useful tool in studying large quantities of multivariate data such as SST images. It is used to decompose a time series into its orthogonal component modes, the first few of which can be used to describe the dominant patterns of variance in the various time-series (Gallaude and Simpson, 1994;Tanimoto et al, 1997). It can be also used on any multivariate data set; in oceanography it is routinely used to analyze measured time-series of some parameters, such as beach changes (Winant et al, 1975).…”

Section: Introductionmentioning

confidence: 99%

Atmospheric and Oceanic Forcings on the Spring Coastal Thermal Environment in the Kuroshio/Oyashio Frontal Region

Yang

2000

Coastal Engineering Journal

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Satellite infrared images and coastal meteorological and oceanographic data are used to investigate the influence of weather and offshore conditions on spring thermal environment around the Sendai Bay in the Kuroshio/Oyashio frontal region. Sea surface temperature (SST) anomaly represents a high annual variation and a high correlation with those of the duration of sunshine and air temperature. The multiple regression analysis of SST with five-explanatory variables (weather variation components) provides a good correlation between the predicted value and in situ SST. Temporal behaviors of coastal SST and salinity show that water mass of the bay is closely connected with the advection of the Kuroshio/Oyashio. Analyses of SST images represent well the properties and durability of the Kuroshio/Oyashio interaction in the same year, and the low SST fluctuation at the inner area of the Sendai Bay and coastal area of the Oyashio flowing path. Empirical orthogonal function (EOF) analyses of monthly-averaged infrared images describe a spatial and temporal variability, which is consistent with in situ measurements. The first modes of the temporal variance, 91.6% (April) and 95.7% (May), show spatially varying offshore water-mass in the presence of the Kuroshio/Oyashio current.

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“…Lagerloef and Bernstein (1988) used temporal and spatial EOF analyses of a series of AVHRR data to study the SST variability in the Santa Barbara Channel and Gallaudet and Simpson (1994) carried out similar analyses off the coast of Baja California. Eslinger et al (1989) considered spatial and temporal EOF analysis of cloud containing Coastal Zone Color Scanner (CZCS) images to study the variability of pigment concentration in north-eastern North American coastal waters.…”

Section: Introductionmentioning

confidence: 99%

Technical Note: On masking land pixels in empirical orthogonal function analysis of sequences of sea surface images

Borzelli¹,

Ciappa

Ligi

et al. 2004

International Journal of Remote Sensing

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Implications of masking land pixels in series of images of the sea surface are investigated. The study is carried out by means of analytical and numerical arguments. An algorithm that performs empirical orthogonal function (EOF) analysis by selecting from original images only the subset of unmasked pixels, has been set up and used on an exemplar dataset constituted by 206 images of the sea level anomaly (SLA) provided by the Topex/Poseidon mission over the Mediterranean Sea. It is shown that, as far as singular value decomposition (SVD) and spatial EOF analyses are involved, different masking values of land pixels modify the relative importance of empirical functions (EOFs) and the corresponding temporal coefficient, while results obtained with temporal EOF analysis remain unchanged.

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An empirical orthogonal function analysis of remotely sensed sea surface temperature variability and its relation to interior oceanic processes off Baja California

Cited by 44 publications

References 34 publications

Empirical eigenfunction analysis of sea surface temperatures in the Western North Atlantic

Empirical eigenfunction analysis of sea surface temperatures in the Western North Atlantic

Atmospheric and Oceanic Forcings on the Spring Coastal Thermal Environment in the Kuroshio/Oyashio Frontal Region

Technical Note: On masking land pixels in empirical orthogonal function analysis of sequences of sea surface images

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