Access the full text.
Sign up today, get DeepDyve free for 14 days.
J. Mcguirk (1982)
A century of precipitation variability along the pacific coast of North America and its impactClimatic Change, 4
E. LeDrew (1980)
Eigenvector Analysis of the Vertical Velocity Field Over the Eastern Canadian ArcticMonthly Weather Review, 108
M. Uddstrom, D. Wark (1985)
A Classification Scheme for Satellite Temperature Retrievals, 24
(1979)
On the physical interpretation of empirical orthogonal functions’, Preprints Sixth Conf
R. Johnston (1981)
Regarding the delimitation of regions according to climatic fluctuationsArchives for meteorology, geophysics, and bioclimatology, Series B, 29
(1983)
Rotation of principal components in climatological research
H. Diaz, D. Fulbright (1981)
Eigenvector Analysis of Seasonal Temperature, Precipitation and Synoptic-Scale System Frequency over the Contiguous United States. Part I: WinterMonthly Weather Review, 109
J. Neuhaus, Charles Wrigley (1954)
THE QUARTIMAX METHODBritish Journal of Statistical Psychology, 7
R. Davis (1976)
Predictability of Sea Surface Temperature and Sea Level Pressure Anomalies over the North Pacific OceanJournal of Physical Oceanography, 6
C. Harris, H. Kaiser (1964)
Oblique factor analytic solutions by orthogonal transformationsPsychometrika, 29
J. Craddock, C. Flood (1969)
Eigenvectors for representing the 500 mb geopotential surface over the Northern HemisphereQuarterly Journal of the Royal Meteorological Society, 95
D. Resio, B. Hayden (1975)
Recent Secular Variations in Mid-Atlantic Winter Extratropical Storm ClimateJournal of Applied Meteorology, 14
T. Wigley, J. Lough, P. Jones (1984)
Spatial patterns of precipitation in England and Wales and a revised
(1983)
Regionalization of central United States for short-period summer rainfall
D. Legates, C. Willmott (1983)
A comparative evaluation of principal components-based and information theory methods of precipitation regionalizationArchives for meteorology, geophysics, and bioclimatology, Series B, 32
W. Zwick, W. Velicer (1982)
Factors Influencing Four Rules For Determining The Number Of Components To Retain.Multivariate behavioral research, 17 2
(1986)
On the modes of variation of growing season rainfall in the central United States’, to be Le Drew, E
H. Kaiser (1958)
The varimax criterion for analytic rotation in factor analysisPsychometrika, 23
Stewart Cohen (1983)
Classification of 500 mb Height Anomalies Using Obliquely Rotated Principal Components, 22
H. Bedi, M. Bindra (1980)
Principal components of monsoon rainfallTellus A, 32
S. Hastenrath, W. Wendland (1979)
On the secular variation of storms in the tropical North Atlantic and Eastern PacificTellus A, 31
(1940)
A rotational method based upon the mean principal axis of a subgroup of tests’, Psych
H. Glahn (1965)
Objective Weather Forecasting by Statistical MethodsThe Statistician, 15
L. Tucker, Carl Finkbeiner (1981)
TRANSFORMATION OF FACTORS BY ARTIFICIAL PERSONAL PROBABILITY FUNCTIONSETS Research Report Series, 1981
H. Kaiser (1974)
A NOTE ON THE EQUAMAX CRITERION.Multivariate behavioral research, 9 4
L. Humphreys, Richard Montanelli (1975)
An Investigation of the Parallel Analysis Criterion for Determining the Number of Common FactorsMultivariate Behavioral Research, 10
L. Tucker (1944)
A semi-analytical method of factorial rotation to simple structurePsychometrika, 9
(1972)
The Foundations of Factor Analysis, McGraw Hill, New York, NY
J. Craddock (1965)
A Meteorological Application of Principal Component AnalysisThe Statistician, 15
G. Thurston, J. Spengler (1985)
A Multivariate Assessment of Meteorological Influences on Inhalable Particle Source Impacts., 24
H. Hotelling (1933)
Analysis of a complex of statistical variables into principal components.Journal of Educational Psychology, 24
(1985)
Hemispheric pattern analysis of weekly 500mb data’, Preprints Ninth Conf
G. North (1984)
Empirical Orthogonal Functions and Normal ModesJournal of the Atmospheric Sciences, 41
G. Brier, Gayle Meltesen (1976)
Eigenvector Analysis for Prediction of Time SeriesJournal of Applied Meteorology, 15
G. North, T. Bell, Robert Cahalan, F. Moeng (1982)
Sampling Errors in the Estimation of Empirical Orthogonal FunctionsMonthly Weather Review, 110
(1983)
Methodological aspects of principal component analysis in meteorological fields’, Preprints Second International Conf
R. Cattell (1966)
The Scree Test For The Number Of Factors.Multivariate behavioral research, 1 2
J. Carroll (1957)
Biquartimin Criterion for Rotation to Oblique Simple Structure in Factor Analysis.Science, 126 3283
J. Walsh, A. Mostek (1980)
A Quantitative Analysis of Meteorological Anomaly Patterns Over the United States, 1900–1977Monthly Weather Review, 108
(1957)
A statistical-dynamic approach to numerical weather prediction’, M.1
N. Cliff, C. Hamburger (1967)
The study of sampling errors in factor analysis by means of artificial experiments.Psychological bulletin, 68 6
(1983)
An analysis of the space and time variation of growing season rainfall in the central
(1976)
Principal Components Analysis’, Geo Abstracts Ltd, Norwich, Great Britain
F. Molteni, P. Bonelli, P. Bacci (1983)
Precipitation over Northern Italy: a description by means of principal component analysis, 22
D. Gatz (1978)
Identification of Aerosol Sources in the St. Louis Area Using Factor AnalysisJournal of Applied Meteorology, 17
J. Carroll (1953)
An analytical solution for approximating simple structure in factor analysisPsychometrika, 18
K. Weickmann (1983)
Intraseasonal Circulation and Outgoing Longwave Radiation Modes During Northern Hemisphere WinterMonthly Weather Review, 111
L. Ashbaugh, L. Myrup, R. Flocchini (1984)
A principal component analysis of sulfur concentrations in the western United StatesAtmospheric Environment, 18
L. Tucker, R. Koopman, R. Linn (1969)
Evaluation of factor analytic research procedures by means of simulated correlation matricesPsychometrika, 34
T. Dyer (1975)
The assignment of Rainfall stations into homogeneous groups: An application of principal component analysisQuarterly Journal of the Royal Meteorological Society, 101
H. Kaiser, B. Cerny (1978)
Casey's Method For Fitting Hyperplanes From An Intermediate Orthomax Solution.Multivariate behavioral research, 13 4
(1985)
The eigentechniques, pseudo-random distributions and the interpretation of spurious signal’, Preprints Ninth Con$ on Prob
(1962)
Trans-Varimax: Some properties of the Ratiomax and Equamax criteria for blind orthogonal rotation
S. Gregory (1975)
ON THE DELIMITATION OF REGIONAL PATTERNS OF RECENT CLIMATIC FLUCTUATIONSWeather, 30
J. Kutzbach (1970)
LARGE-SCALE FEATURES OF MONTHLY MEAN NORTHERN HEMISPHERE ANOMALY MAPS OF SEA-LEVEL PRESSUREMonthly Weather Review, 98
H. Harman (1961)
Modern factor analysis
J. Rogers (1976)
Sea Surface Temperature Anomalies in the Eastern North Pacific and Associated Wintertime Atmospheric Fluctuations over North America, 1960–73Monthly Weather Review, 104
T. Dyer (1979)
Rainfall along the east coast of southern Africa, the southern oscillation, and the latitude of the subtropical high pressure beltQuarterly Journal of the Royal Meteorological Society, 105
C. Willmott (1976)
A component analytic approach to precipitation regionalization in CaliforniaArchiv für Meteorologie, Geophysik und Bioklimatologie, Serie B, 24
T. Karl, A. Koscielny, H. Diaz (1982)
Potential Errors in the Application of Principal Component (Eigenvector) Analysis to Geophysical DataJournal of Applied Meteorology, 21
M. Richman, P. Lamb (1985)
Climatic Pattern Analysis of Three- and Seven-Day Summer Rainfall in the Central United States: Some Methodological Considerations and a RegionalizationJournal of Applied Meteorology and Climatology, 24
J. Kutzbach (1967)
Empirical Eigenvectors of Sea-Level Pressure, Surface Temperature and Precipitation Complexes over North AmericaJournal of Applied Meteorology, 6
T. Karl, A. Koscielny (1982)
Drought in the United States: 1895–1981International Journal of Climatology, 2
H. Diaz (1981)
Eigenvector Analysis of Seasonal Temperature, Precipitation and Synoptic-Scale System Frequency over the Contiguous United States. Part II: Spring, Summer, Fall and AnnualMonthly Weather Review, 109
R. Cattell, J. Muerle (1960)
The "Maxplane" Program for Factor Rotation to Oblique Simple StructureEducational and Psychological Measurement, 20
H. Storch, Gerhard Hannoschöck (1985)
Statistical Aspects of Estimated Principal Vectors (EOFs) Based on small Sample Sizes, 24
A. Hakstian (1974)
THE DEVELOPMENT OF A CLASS OF OBLIQUE FACTOR SOLUTIONSBritish Journal of Mathematical and Statistical Psychology, 27
M. Richman (1981)
Obliquely Rotated Principal Components: An Improved Meteorological Map Typing Technique?Journal of Applied Meteorology, 20
W. Christensen, R. Bryson (1966)
AN INVESTIGATION OF THE POTENTIAL OF COMPONENT ANALYSIS FOR WEATHER CLASSIFICATIONMonthly Weather Review, 94
J. Horn (1965)
A rationale and test for the number of factors in factor analysisPsychometrika, 30
B. Korth, L. Tucker (1975)
The distribution of chance congruence coefficients from simulated dataPsychometrika, 40
J. Horel (1984)
Complex Principal Component Analysis: Theory and Examples, 23
J. Horel (1981)
A Rotated Principal Component Analysis of the Interannual Variability of the Northern Hemisphere 500 mb Height FieldMonthly Weather Review, 109
R. Balling, M. Lawson (1982)
Twentieth century changes in winter climatic regionsClimatic Change, 4
D. Saunders (1961)
The rationale for an “oblimax” method of transformation in factor analysisPsychometrika, 26
J. Walsh, M. Richman, D. Allen (1982)
Spatial Coherence of Monthly Precipitation in the United StatesMonthly Weather Review, 110
L. Veitch (1965)
The description of Australian pressure fields by principal componentsQuarterly Journal of the Royal Meteorological Society, 93
P. Englehart, A. Douglas (1985)
A Statistical Analysis of Precipitation Frequency in the Conterminous United States, Including Comparisons with Precipitation Totals, 24
Hakstian Ar, Rogers Wt, Cattell Rb (1982)
The Behavior Of Number-Of-Factors Rules With Simulated Data.Multivariate behavioral research, 17 2
H. Kaiser, K. Dickman (1977)
Some properties of binormaminPsychometrika, 42
R. Durfee (1967)
MULTIPLE FACTOR ANALYSIS.
J. Walsh, M. Richman (1981)
Seasonality in the Associations between Surface Temperatures over the United States and the North Pacific OceanMonthly Weather Review, 109
(1959)
Analytic determination of comnion factors’, Amer
Jeffrey Katz, F. Rohlf (1974)
Functionplane—A new approach to simple structure rotationPsychometrika, 39
H. Lins (1985)
Streamflow Variability in the United States: 1931–78, 24
A. Hakstian, Robert Abell (1974)
A further comparison of oblique factor transformation methodsPsychometrika, 39
(1985)
Climate regions derived from Chinese long-term precipitation records
Ashbaugh (1985)
Authors' reply to discussion on A principal component analysis of sulfer concentrations in the western United StatesAtmos. Environ., 19
C. Willmott (1978)
P-mode principal components analysis, grouping and precipitation regions in CaliforniaArchiv für Meteorologie, Geophysik und Bioklimatologie, Serie B, 26
D. Klaus (1978)
Spatial distribution and periodicity of mean annual precipitation south of the SaharaArchiv für Meteorologie, Geophysik und Bioklimatologie, Serie B, 26
P. Schönemann (1966)
A generalized solution of the orthogonal procrustes problemPsychometrika, 31
B. Gray (1981)
On the stability of temperature eigenvector patternsInternational Journal of Climatology, 1
W. Brinkmann (1981)
Sea Level Pressure Patterns over Eastern North America, 1899–1976Monthly Weather Review, 109
A. Hendrickson, P. White (1964)
PROMAX: A QUICK METHOD FOR ROTATION TO OBLIQUE SIMPLE STRUCTUREBritish Journal of Statistical Psychology, 17
(1982)
SAS Institute Inc
M. Salinger (1980)
New Zealand Climate: I. Precipitation PatternsMonthly Weather Review, 108
M. Hirose, J. Kutzbach (1969)
An Alternate Method for Eigenvector Computations.Journal of Applied Meteorology, 8
G. Morin, J. Fortin, W. Sochanska, Jean-Pierre Lardeau, Raymond Charbonneu (1979)
Use of principal component analysis to identify homogeneous precipitation stations for optimal interpolationWater Resources Research, 15
R. Jennrich, P. Sampson (1966)
Rotation for simple loadingsPsychometrika, 31
C. Eckart, G. Young (1936)
The approximation of one matrix by another of lower rankPsychometrika, 1
H. Kaiser (1959)
Computer Program for Varimax Rotation in Factor AnalysisEducational and Psychological Measurement, 19
A. Fukuoka (1951)
The Central Meteorological Observatory,A study on 10-day forecast(A synthetic report)Geophysical magazine, 22
L. Tucker (1955)
The objective definition of simple structure in linear factor analysisPsychometrika, 20
J. Overland, R. Preisendorfer (1982)
A Significance Test for Principal Components Applied to a Cyclone ClimatologyMonthly Weather Review, 110
R. Tabony (1981)
A principal component and spectral analysis of European rainfallInternational Journal of Climatology, 1
Recent research has pointed to a number of inherent disadvantages of unrotated principal components and empirical orthogonal functions when these techniques are used to depict individual modes of variation of data matrices in exploratory analyses. The various pitfalls are outlined and illustrated with an alternative method introduced to minimize these problems via available linear transformations known as simple structure rotations. The rationale and theory behind simple structure rotation and Procrustes target rotation is examined in the context of meteorological/climatological applications. This includes a discussion of the six unique ways to decompose a rotated data set in order to maximize the physical interpretability of the rotated results.
International Journal of Climatology – Wiley
Published: Jan 1, 1986
Keywords: ; ; ; ; ; ; ; ;
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.