Biennial Variations in Surface Temperature over the United States as Revealed by Singular Decomposition

Rasmusson, Eugene M.; Arkin, Phillip A.; Chen, Wenyuan; Jalickee, John B.

doi:10.1175/1520-0493(1981)109<0587:bvisto>2.0.co;2

Cited by 89 publications

(37 citation statements)

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“…An elegant alternative to FDEOFs is the complex EOFs in the time domain introduced into atmospheric science by Rasmusson et al (1981) using Hilbert singular decomposition. The method was refined later by Barnett (1983) and applied to the monsoon (Barnett, 1984a,b), atmospheric angular momentum (Anderson and Rosen, 1983), the QBO in northern hemispheric SLP (Trenberth and Shin, 1984), and coastal ocean currents (Merrifield and Winant, 1989).…”

Section: Complex Hilbertmentioning

confidence: 99%

“…FDEOFs generalise conventional EOFs in the sense that the covariance matrix used for EOFs is only related to the real part of the cross-spectrum matrix and hence does not use the whole information from the complex cross-spectrum matrix. The HEOF method (Rasmusson et al, 1981;Barnett, 1983;Horel, 1984;von Storch and Zwiers, 1999) is an alternative to FDEOFs and uses the Hilbert, or quadrature transform of the field. This transform allows HEOFs to deal with propagating structures/waves in the time domain using complexified fields.…”

Section: Introductionmentioning

confidence: 99%

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Empirical orthogonal functions and related techniques in atmospheric science: A review

Hannachi

Jolliffe

Stephenson

2007

Intl Journal of Climatology

886

616

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Abstract:Climate and weather constitute a typical example where high dimensional and complex phenomena meet. The atmospheric system is the result of highly complex interactions between many degrees of freedom or modes. In order to gain insight in understanding the dynamical/physical behaviour involved it is useful to attempt to understand their interactions in terms of a much smaller number of prominent modes of variability. This has led to the development by atmospheric researchers of methods that give a space display and a time display of large space-time atmospheric data.Empirical orthogonal functions (EOFs) were first used in meteorology in the late 1940s. The method, which decomposes a space-time field into spatial patterns and associated time indices, contributed much in advancing our knowledge of the atmosphere. However, since the atmosphere contains all sorts of features, e.g. stationary and propagating, EOFs are unable to provide a full picture. For example, EOFs tend, in general, to be difficult to interpret because of their geometric properties, such as their global feature, and their orthogonality in space and time. To obtain more localised features, modifications, e.g. rotated EOFs (REOFs), have been introduced. At the same time, because these methods cannot deal with propagating features, since they only use spatial correlation of the field, it was necessary to use both spatial and time information in order to identify such features. Extended and complex EOFs were introduced to serve that purpose.Because of the importance of EOFs and closely related methods in atmospheric science, and because the existing reviews of the subject are slightly out of date, there seems to be a need to update our knowledge by including new developments that could not be presented in previous reviews. This review proposes to achieve precisely this goal. The basic theory of the main types of EOFs is reviewed, and a wide range of applications using various data sets are also provided.

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Section: Complex Hilbertmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Empirical orthogonal functions and related techniques in atmospheric science: A review

Hannachi

Jolliffe

Stephenson

2007

Intl Journal of Climatology

886

616

View full text Add to dashboard Cite

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“…Yasunail [ 1985,1989] debated whether the TBO should be considered part of the ENSO, demonstrating that it had its own unique character as well as different period scale and eastward phase speed. This view was shared by Trenberth [ 1980] but not by Rasmusson et al [1981]• who considered the TBO to be orle of the higherfrequency components of the ENSO. Indeed, Allan et al [1996] and Allan [2000] found the TBO and ENSO projecting similarly onto global standing modes of SST and SLP variability, Recently, however, subtle but significant differences have been observed in the Pacific standing modes of TBO and ENSO signals [Tourre et al, 2001], with SST anomalies of the TBO more confined to the equator than those of ENSO and displaced more to the east in the equatorial Pacific Ocean.…”

Section: Paper Number 2000jc000407mentioning

confidence: 99%

A global quasi‐biennial wave in surface temperature and pressure and its decadal modulation from 1900 to 1994

White¹,

Allan²

2001

J. Geophys. Res.

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“…CEOF (e.g., Rasmusson et al 1981) Surv Geophys (2018) 39:435-465 439 variability due to the dominant influence of the ENSO. Therefore, through a careful synthetic study, we will justify the application of CICA for extracting ENSO and non-ENSO modes from GRACE TWS data similar to Eicker et al (2016).…”

Section: Introductionmentioning

confidence: 99%

“…As a result, the ordinary PCA approach has been modified to better deal with nonstationary information, which yielded methods such as the extended empirical orthogonal function (EEOF, Weare and Nasstrom 1982) and the complex empirical orthogonal function (CEOF, Rasmusson et al 1981). EEOF is also called multi-channel singular spectrum analysis (MSSA, Broomhead and King 1986a, b).…”

Section: Introductionmentioning

confidence: 99%

Developing a Complex Independent Component Analysis (CICA) Technique to Extract Non-stationary Patterns from Geophysical Time Series

et al. 2017

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In recent decades, decomposition techniques have enabled increasingly more applications for dimension reduction, as well as extraction of additional information from geophysical time series. Traditionally, the principal component analysis (PCA)/empirical orthogonal function (EOF) method and more recently the independent component analysis (ICA) have been applied to extract, statistical orthogonal (uncorrelated), and independent modes that represent the maximum variance of time series, respectively. PCA and ICA can be classified as stationary signal decomposition techniques since they are based on decomposing the autocovariance matrix and diagonalizing higher (than two) order statistical tensors from centered time series, respectively. However, the stationarity assumption in these techniques is not justified for many geophysical and climate variables even after removing cyclic components, e.g., the commonly removed dominant seasonal cycles. In this paper, we present a novel decomposition method, the complex independent component analysis (CICA), which can be applied to extract non-stationary (changing in space and time) patterns from geophysical time series. Here, CICA is derived as an extension of realvalued ICA, where (a) we first define a new complex dataset that contains the observed time series in its real part, and their Hilbert transformed series as its imaginary part, (b) an

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Biennial Variations in Surface Temperature over the United States as Revealed by Singular Decomposition

Cited by 89 publications

References 0 publications

Empirical orthogonal functions and related techniques in atmospheric science: A review

Empirical orthogonal functions and related techniques in atmospheric science: A review

A global quasi‐biennial wave in surface temperature and pressure and its decadal modulation from 1900 to 1994

Developing a Complex Independent Component Analysis (CICA) Technique to Extract Non-stationary Patterns from Geophysical Time Series

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