Hostname: page-component-77c89778f8-rkxrd Total loading time: 0 Render date: 2024-07-19T23:19:47.606Z Has data issue: false hasContentIssue false

Estimating interannual variability arising from weather events

Published online by Cambridge University Press:  14 July 2016

Xiaogu Zheng*
Affiliation:
National Institute of Water and Atmospheric Research
James Renwick*
Affiliation:
National Institute of Water and Atmospheric Research
*
1Postal address: National Institute of Water and Atmospheric Research, PO Box 14–901 Kilbirnie, Wellington, New Zealand. Email: x.zheng@niwa.cri.nz
1Postal address: National Institute of Water and Atmospheric Research, PO Box 14–901 Kilbirnie, Wellington, New Zealand. Email: x.zheng@niwa.cri.nz

Abstract

The advantages and limitations of frequency domain and time domain methods for estimating the interannual variability arising from day-to-day weather events are summarized. A modification of the time domain method is developed and its application in examining a precondition for the frequency domain method is demonstrated. A combined estimation procedure is proposed: it takes advantage of the strengths of both methods. The estimation procedures are tested with sets of synthetic data and are applied to long time series of three meteorological parameters. The impacts of the different methods on tests of potential long-range predictability for seasonal means are also discussed.

Type
Other stochastic models
Copyright
Copyright © Applied Probability Trust 2001 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Brockwell, P. J. and Davis, R. A. (1987). Time Series Theory and Methods. Springer, New York.Google Scholar
Epstein, E. S. (1991). Determining the optimum number of harmonics to represent normals based on multiyear data. J. Climate 4, 10471051.Google Scholar
Hannan, E. J. and Deistler, M. (1988). The Statistical Theory of Linear Systems. Wiley, New York.Google Scholar
Jones, R. H. (1975). Estimating the variance of time averages. J. Appl. Meteor. 14, 159163.Google Scholar
Jones, R. H. (1993). Longitudinal Data with Serial Correlation: A State-Space Approach. Chapman and Hall, London.Google Scholar
Jones, R. H., Madden, R. A. and Shea, D. J. (1995). A new methodology for investigating long range predictability. In Proceedings of Sixth International Meeting on Statistical Climatology , Galway, Ireland, 531534.Google Scholar
Katz, W. R. (1982). Statistical evaluation of climate experiments with general circulation models: a parametric time series modeling approach. J. Atmos. Sci. 39, 14461455.Google Scholar
Leith, C. E. (1973). The standard error of time-average estimates of climatic means. J. Appl. Meteor. 12, 10661069.Google Scholar
Lorenz, E. N. (1970). Climate change as a mathematical problem. J. Appl. Meteor. 9, 325329.2.0.CO;2>CrossRefGoogle Scholar
Madden, R. A. (1976). Estimates of the natural variability of time averaged sea level pressure. Mon. Wea. Rev. 104, 942952.Google Scholar
Madden, R. A. (1981). A quantitative approach to long-range prediction. J. Geophys. Res. 86, 98179825.Google Scholar
Madden, R. A. (1983). Comments on ‘Natural variability and predictability’, reply. Mon. Wea. Rev. 111, 586589.Google Scholar
Richardson, C. W. (1981). Stochastic simulation of daily precipitation, temperature, and solar radiation. Water Resources Research 17, 182190.Google Scholar
Trenberth, K. E. (1976). Fluctuations and trends in indices of the Southern Hemisphere circulation. Quart. J. Roy. Meteor. Soc. 102, 6575.Google Scholar
Trenberth, K. E. (1984a). Some effects of finite sample size and persistence on meteorological statistics. Part I: Autocorrelations. Mon. Wea. Rev. 112, 23592368.Google Scholar
Trenberth, K. E. (1984b). Some effects of finite sample size and persistence on meteorological statistics. Part II: Potential predictability. Mon. Wea. Rev. 112, 23692379.Google Scholar
Trenberth, K. E. (1985). Potential predictability of geopotential heights over the Southern Hemisphere. Mon. Wea. Rev. 113, 5464.Google Scholar
Trenberth, K. E. and Shea, D. J. (1987). On the evolution of the southern oscillation. Mon. Wea. Rev. 115, 30783095.Google Scholar
Venables, W. N. and Ripley, B. D. (1995). Modern Applied Statistics with S-Plus. Springer, New York.Google Scholar
Zheng, X. (1996). Unbiased estimation of autocorrelations of daily meteorological variables. J. Climate 9, 21972203.2.0.CO;2>CrossRefGoogle Scholar
Zwiers, F. W. (1987). A potential predictability study conducted with an atmospheric general circulation model. Mon. Wea. Rev. 115, 29572974.Google Scholar