References

Table of Contents

General

Chatfield, C., 1989: The Analysis of Time Series. Chapman & Hall, London.

Karl, J.H., 1989: An Introduction to Digital Signal Processing. John Wiley & Sons, New York.

Press, W.H., S.A. Teukolsky, W.T. Vetterling, and B.P. Flannery, 1992: Numerical Recipes in Fortran: The Art of Scientific Computing, 2nd ed.. Cambridge University Press.
[hereafter Numerical Recipes]

Statistics

Abraham, B. and J. Ledolter, 1983: Statistical Methods for Forecasting. John Wiley & Sons, New York.

Spiegel, M.R., 1975: Schaum's Outline of Theory and Problems of Probability and Statistics. McGraw-Hill, New York.

Correlation and Regression

Munk, W.H., 1960: Smoothing and persistence. J. Meteor., 17, 92-93.

Fits

Potter, K.W., 1981: Illustration of a new test for detecting a shift in mean in precipitation series. Mon. Wea. Rev., 109, 2040-2045.

Shuman, F.G., 1957: Numerical methods in weather prediction: II. Smoothing and filtering. Mon. Wea. Rev., 85, 357-361.

Spectrum

Båth, M., 1974: Spectral Analysis in Geophysics. Elsevier Pub. Co., Amsterdam.

Gilman, D.L., F.J. Fuglister, and J.M. Mitchell, Jr., 1963: On the power spectrum of "red noise". J. Atmos. Sci., 20, 182-184.

Jenkins, G.M. and D.G. Watts, 1968: Spectral Analysis and its Applications. Holden-Day, San Francisco.

Lomb Periodogram

Horne, J.H. and S.L. Baliunas, 1986: A prescription for period analysis of unevenly sampled time series. Astrophys. J., 302, 757-763.

Numerical Recipes, pp. 569-577.

Wavelets

Farge, M., 1992: Wavelet transforms and their applications to turbulence. Ann. Rev. Fluid Mech., 24, 395-457.

Lau, K.-M. and H.-Y. Weng, 1995: Climate signal detection using wavelet transform: How to make a time series sing. Bull. Am. Meteor. Soc, 76, 2391-2402.

Meyers, S.D., B.G. Kelly, and J.J. O'Brien, 1993: An introduction to wavelet analysis in oceanography and meteorology: With application to the dispersion of Yanai waves. Mon. Wea. Rev., 121, 2858-2866.

Weng, H. and K.-M. Lau, 1994: Wavelets, period doubling, and time-frequency localization with application to organization of convection over the tropical western Pacific. J. Atmos. Sci., 51, 2523-2541.

Data Void Regions

Barnes, S.L., 1964: A technique for maximizing details in numerical weather map analysis. J. Appl. Meteor., 3, 396-409.

Madden, R.A., D.J. Shea, G.W. Branstator, J.J. Tribbia, and R.O. Weber, 1993: The effects of imperfect spatial and temporal sampling on estimates of the global mean temperature: Experiments with model data. J. Climate, 6, 1057-1066.

Reynolds, R.W. and T.M. Smith, 1994: Improved global sea surface temperature analyses using optimum interpolation. J. Climate, 7, 929-948.

Schaefer, J.T. and C.A. Doswell III, 1979: On the interpolation of a vector field. Mon. Wea. Rev., 107, 458-476.

Shuman, F.G., 1957: Numerical methods in weather prediction: II. Smoothing and filtering. Mon. Wea. Rev., 85, 357-361.

2D Correlation & Regression Maps

Livezey, R.E. and W.Y. Chen, 1983: Statistical Field Significance and its Determination by Monte Carlo Techniques. Mon. Wea. Rev., 111, 46-59.

Empirical Orthogonal Functions

Mitchum, G.T., 1993: Principal component analysis: Basic methods and extensions. Müller, P. and D. Henderson, editors, Statistical Methods in Physical Oceanography, Proceedings of the 'Aha Huliko'a Hawaiian Winter Workshop, 185-199.

Preisendorfer, R.W., 1988: Principal Component Analysis in Meteorology and Oceanography. Elsevier Pub. Co., New York, 425 pp.