Examples of Extended Empirical Orthogonal Function Analyses

Weare, Bryan C.; Nasstrom, John S.

doi:10.1175/1520-0493(1982)110<0481:eoeeof>2.0.co;2

Cited by 356 publications

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“…In multivariate SSA (M-SSA: Weare and Nasstrom, 1982;Lau and Chan, 1985;Kimoto et al, 1991), the EOFs are normalized eigenvectors of a block [M x G'-by-M × C] lag-cross-correlation matrix between C time series -or channels, such as the leading S-PCs of the preceding S-EOF analysis -with lags ranging from 1 to M times At, the sampling interval. The EOFs in M-SSA contain elements of both spatial and temporal variability and are called space-time EOFs (ST-EOFs: Plaut and Vautard, 1994).…”

Section: Singular-spectrum Analysis (Ssa)mentioning

confidence: 99%

Interannual and interdecadal variability in United States surface-air temperatures, 1910-87

1995

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Abstract. Monthly mean surface-air temperatures at 870 sites in the contiguous United States were analyzed for interannual and interdecadal variability over the time interval 1910-87. The temperatures were analyzed spatially by empirical-orthogonal-function analysis and temporally by singularspectrum analysis (SSA). The dominant modes of spatio-temporal variability are trends and nonperiodic variations with time scales longer than 15 years, decadal-scale oscillations with periods of roughly 7 and 10 years, and interannual oscillations of 2.2 and 3.3 years. Together, these modes contribute about 18% of the slower-than-annual United States temperature variance. Two leading components roughly capture the mean hemispheric temperature trend and represent a long-term warming, largest in the southwest, accompanied by cooling of the domain's southeastern quadrant. The extremes of the 2.2-year interannual oscillation characterize temperature differences between the Northeastern and Southwestern States, whereas the 3.3-year cycle is present mostly in the Western States. The 7-to 10-year oscillations are rauch less regular and persistent than the interannual oscillations and characterize temperature differences between the western and interior sectors of the United States. These continental-or regionat-scale temperature variations may be related to climatic variations with similar periodicities, either global or centered in other regions; such variations include quasi-biennial oscillations over the tropical Pacific or North Atlantic and quasi-triennial oscillations of North Pacific sea-surface temperatures.

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Section: Singular-spectrum Analysis (Ssa)mentioning

confidence: 99%

Interannual and interdecadal variability in United States surface-air temperatures, 1910-87

1995

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“…The original 2.5°gridded data were smoothed into 5°latitude by 5°longitude. The data matrix consisting of 689 consecutive maps each containing 352 grid values for three variables (Ts, SLP, and U) was subjected to PCA to explore the essential relationships among the monthly time-series, using methods consistent with earlier studies (Wallace and Dickinson 1972;Weare and Nasstrom, 1982;Horel, 1984;Barnston and Livezey, 1987;Kim et al, 1996a;Kim and Wu, 1999). We made separate analyses for the raw time-series with seasonal cycle retained (SEAS) and with the seasonal cycle removed (anomaly, ANOM) by subtracting the monthly averages at each grid point over the study period.…”

Section: Methodsmentioning

confidence: 99%

Joint modes of climate variability across the inter‐Americas

Jury

Malmgren

2011

Intl Journal of Climatology

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Surface temperature (Ts), sea-level pressure (SLP), and zonal wind (U) fields from NCEP/NCAR reanalysis spanning the period 1949-2006 have been jointly analysed by means of principal component analysis to assess the dominant modes of climate variability in the inter-American region, including the eastern Pacific and Atlantic Oceans. Two aspects of these time-series were analysed: the annual cycle and its residual. Seasonal modes 1 and 2 take the form of north-south dipoles of Ts over oceanic and terrestrial environments. Seasonal mode 3 derives from SLP changes over the Amazon with a Caribbean wind response. Temporal fluctuations in the residual (ANOM) fields are resolved by six modes. ANOM1-3 is dominated by SLP and Ts changes across the equator in the Atlantic and east Pacific. The ANOM4 pattern is an isolated wind mode in the NE Atlantic. ANOM5 and 6 are dominated by sea temperature patterns in the South Atlantic and in the path of African easterly waves crossing the tropical Atlantic. The residual time scores are marked by significant warming (Ts) trends and co-located lower pressure. Cycles in the anomaly time scores tend to occur at 2-to 5-year periods. Modes dominated by atmospheric variables exhibit greater high-frequency 'noise' than oceanic modes. Predictability is assessed through development of multivariate regression models trained on Caribbean rainfall and related target time-series. The seasonal dipole between North and South America is found to modulate Pacific El Niño-Southern Oscillation and tropical Atlantic influence on Caribbean rainfall.

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“…Before proceeding further, it must be noted that the same input matrix structure (i.e. a sequence of spatial fields at k successive times in columns) have been used in EEOF analysis (Weare and Nasstrom, 1982, from now on WN). In WN, the eigenvalues are obtained from the covariance matrix which corresponds to covariance between rows (i.e.…”

Section: Methodsmentioning

confidence: 99%

“…Previously, Weare and Nasstrom, (1982) had proposed the Extended Empirical Orthogonal Functions (EEOF) analysis as an extension of the common utilized EOFs in the traditional S-mode. Interesting results have been obtained with this technique, e.g.…”

Section: Introductionmentioning

confidence: 99%

Principal sequence pattern analysis: a new approach to classifying the evolution of atmospheric systems

Compagnucci

Araneo

Canziani

2001

Intl Journal of Climatology

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A new eigentechnique approach, Principal Sequence Pattern Analysis (PSPA), is introduced for the analysis of spatial pattern sequence, as an extension of the traditional Principal Component Analysis set in the T-Mode. In this setting, the variables are sequences of k spatial fields of a given meteorological variable. PSPA is described and applied to a sample of 256 consecutive daily 1000 hPa geopotential height fields. The results of the application of the technique to 5-day sequences demonstrate the advantages of this procedure in identifying field pattern sequences, thereby allowing the determination of the evolution and development of the systems, together with cyclogenesis and anticyclogenesis processes.In order to complete the study, the more traditional Extended Empirical Orthogonal Function (EEOF) analysis, which is the S-mode equivalent of the PSPA, was applied to the same dataset. For EEOF, it was not possible to identify any real sequences that could correspond to the sequences of patterns yielded by the EEOF. Furthermore, the explained variance distribution in the EEOF was significantly different from that obtained with PSPA. Conversely, the PSPA approach allowed for the identification of the sequences corresponding to those sequences observed in the data. Using diagrams of the squares of the component loadings values, as a function of time, the study of the times of occurrence of dominant field characteristics was also possible. In other words, successful determination of periods where the basic flow was dominant and times when strongly perturbed transient events with a significant meridional component occurred, was facilitated by PSPA.

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Examples of Extended Empirical Orthogonal Function Analyses

Cited by 356 publications

References 0 publications

Interannual and interdecadal variability in United States surface-air temperatures, 1910-87

Interannual and interdecadal variability in United States surface-air temperatures, 1910-87

Joint modes of climate variability across the inter‐Americas

Principal sequence pattern analysis: a new approach to classifying the evolution of atmospheric systems

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