Applied Factor Analysis in the Natural Sciences

Reyment, Richard A.; Jvreskog, K. G.

doi:10.1017/cbo9780511524882

Cited by 348 publications

(128 citation statements)

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“…The period used for the whole study will span the years 1979-2002 as it is the common period of precipitation observations and ERA40 availability. The main modes of interannual variability affecting the SPCZ are extracted by applying a rotated Empirical Orthogonal Function analysis (using the varimax method described in Reyment and Jörekog 1993) to the austral summer precipitation in the South Pacific region (155°E-80°W/0-30°S). 1 Results are shown for GPCP in Fig.…”

Section: Choice Of the Reanalysismentioning

confidence: 99%

Interannual variability of the South Pacific Convergence Zone and implications for tropical cyclone genesis

et al. 2009

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The interannual variability of the South Pacific Convergence Zone (SPCZ) and its influence on tropical cyclone (TC) genesis in the South Pacific are investigated using observations and ERA40 reanalysis over the 1979-2002 period. In austral summer, the SPCZ displays four typical structures at interannual timescales. The first three are characterized by a diagonal orientation of the SPCZ and account for 85% of the summer seasons. One is close to climatology and the other two exhibit a 3°northward or southward departure from the SPCZ climatological position. In contrast, the fourth one, that only encompasses three austral summer seasons (the extreme 1982/1983 and 1997/1998 El Niño events and the moderate 1991/1992 El Niño event), displays very peculiar behaviour where the SPCZ largely departs from its climatological position and is zonally oriented. Variability of the western/central Pacific equatorial sea surface temperature (SST) is shown to modulate moisture transport south of the equator, thereby strongly constraining the location of the SPCZ. The SPCZ location is also shown to strongly modulate the atmospheric circulation variability in the South Pacific with specific patterns for each class. However, independently of its wide year-to-year excursions, the SPCZ is always collocated with the zero relative vorticity at low levels while the maximum vorticity axis lies 6°to the south of the SPCZ position. This coherent atmospheric organisation in the SPCZ region is shown to constrain tropical cyclogenesis to occur preferentially within 10°south of the SPCZ location as this region combines all the large-scale atmospheric conditions that favour the breeding of TCs. This analysis also reveals that cyclogenesis in the central Pacific (in the vicinity of French Polynesia) only occurs when the SPCZ displays a zonal orientation while this observation was previously attributed to El Niño years in general. Different characteristics of El Niño Southern Oscillation (ENSO)-related Pacific equatorial warming are shown to impact differently on the SPCZ position, suggesting that for regional scales, such as the South Pacific, the SPCZ classification is more appropriate than a simple ENSO index to characterize TC interannual variability. These findings suggest that forecasting the strength of El Niño through SST variations in the eastern Pacific may not be sufficient to accurately predict cyclogenesis in the South Pacific, especially east of the dateline.

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Section: Choice Of the Reanalysismentioning

confidence: 99%

Interannual variability of the South Pacific Convergence Zone and implications for tropical cyclone genesis

et al. 2009

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“…A sampling of standard works [12,14,18] on these topics, together with more recent reviews [19,20] that focus on factor rotation methods, is included in the references. Over the past few decades, factor analysis has proved to be a powerful data analysis tool in the physical sciences as well, with broad application in, for instance, climatology, [21] geosciences, [22] and chemistry. [23] Interestingly, even a cursory review of the relevant literature shows great variation in terminology and its usage as practitioners in different fields try to tailor factor analysis to meet their specific needs.…”

Section: Pca Svd and Factor Rotationmentioning

confidence: 99%

Exploiting spatial‐domain simplicity in spectral image analysis

Keenan

2009

Surface & Interface Analysis

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Full-spectrum imaging is fast becoming a tool of choice for characterizing heterogeneous materials. Spectral images, which consist of a complete spectrum at each point in a spatial array, can be acquired from a wide variety of surface and microanalytical spectroscopic techniques. It is not uncommon that such spectral image data sets comprise tens of thousands of individual spectra, or more. Given the vast quantities of raw spectral data, factor analysis methods have proved indispensable for extracting the chemical information from these high-dimensional data sets into a limited number of factors that represent the spectral and spatial characteristics of the sample's composition. It is well known that factor models suffer a 'rotational ambiguity', that is, there are an infinite number of factor models that will fit the data equally well. Thus, physically inspired constraints are often employed to derive relatively unique models that make the individual factors more easily interpreted by the practicing analyst. In the present work, we note that many samples undergoing spectral image analysis are 'simple' in the sense that only one or a few of the sample's constituents are present at any particular location. When this situation prevails, simplicity in the spatial domain can be exploited to make the resulting factor models more realistic. In particular, orthogonal rotation of the spatial-domain vectors arising from singular value decomposition (SVD) of the spectral data matrix will be shown to be an effective method for making physically acceptable and easily interpretable estimates of the pure-component spectra and abundances.

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“…There are no statistical constraints on PCs, but CFs must be the Gaussian in a PFA model (Reyment and Jöreskog, 1993). The CFs could be rotated (Richman, 1985), for example, by the varimax method (Kaiser, 1959) for simplifying evaluation of the PC patterns easily.…”

Section: Application Of the Kruskal-wallis Test To Homogeneity Assessmentioning

confidence: 99%

Spatiotemporal variability of precipitation total series over Turkey

Türkeş

Koç

Sarış

2008

Intl Journal of Climatology

162

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Long-term changes and trends in the series of monthly, seasonal and annual precipitation totals of 97 stations in Turkey were analysed by considering their spatial and temporal characteristics. Secular trends in precipitation series were examined with the Mann-Kendall rank correlation test for the general period 1930-2002, whereas spatial variabilities of, and relationships between, the precipitation series at 86 of these stations were investigated by the principal component analysis (PCA) for the period 1953-2002 when the length of data is at its best for the stations subjected to the PCA. Major findings of the paper can be summarized as follows: (1) First principal component (PC1) generally describes climatology of the precipitation totals in Turkey that is closely governed by the large-scale and/or synoptic scale atmospheric features (i.e. surface and upper air pressure and wind systems). (2) In winter, it is very likely that the greater PC1 loadings over the western and south-western parts of Turkey characterized mainly with the Mediterranean rainfall regimes indicate influence of the large-scale atmospheric circulation and associated weather patterns. However, smaller PC1 loadings over the north-eastern and eastern parts of Turkey are very likely related to the influence of the northerly mid-latitude cyclones, and the northerly and easterly circulations linked with the Eastern Europe and the Siberian originated high pressures on spatial variations of winter precipitation. (3) As for the long-term temporal variability, it was detected that there is an apparent decreasing trend in the winter precipitation totals of Turkey, whereas a general increasing trend is dominant in the precipitation totals of spring, summer and autumn seasons. (4) Observed decreasing trends are the strongest over the Mediterranean and the Mediterranean Transition rainfall regime regions. (5) Strong decreasing trends are also mostly found in winter months of the year, while apparent increasing trends show up at some stations in the months of April, August and October.

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Applied Factor Analysis in the Natural Sciences

Cited by 348 publications

References 0 publications

Interannual variability of the South Pacific Convergence Zone and implications for tropical cyclone genesis

Interannual variability of the South Pacific Convergence Zone and implications for tropical cyclone genesis

Exploiting spatial‐domain simplicity in spectral image analysis

Spatiotemporal variability of precipitation total series over Turkey

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