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| Titel |
Factors affecting multiscaling analysis of rainfall time series |
| VerfasserIn |
D. Harris, A. Seed, M. Menabde, G. Austin |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1023-5809
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 4, no. 3 ; Nr. 4, no. 3, S.137-156 |
| Datensatznummer |
250001682
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| Publikation (Nr.) |
 copernicus.org/npg-4-137-1997.pdf |
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| Zusammenfassung |
| Simulations based on random multiplicative cascade models are used to
investigate the uncertainty in estimates of parameters characterizing the multiscaling
nature of rainfall time series. The principal parameters used and discussed are the
spectral exponent, β, and the K(q) function which characterizes the scaling of the
moments. By simulating a large number of series, the sampling variability of parameter
estimates in relation to the length of the time series is assessed and found to be in
excess of 10%-20% for fields less than ~104 points in length. The issue of long
time series which may consist of physically distinct processes with different statistics is
addressed and it is shown that highly variable data mixed with an equal amount of less
variable data of similar strength is dominated entirely by the statistics of the highly
variable data. The effects on the estimates of β and K(q) with the addition of white noise
or the tipping bucket effect (quantization) can also be significant, particularly
following gradient transformations. Some high resolution rainfall data are also analyzed
to illustrate how a single instrumental glitch can strongly bias results and how mixing
physically different processes together can lead to incorrect conclusions. |
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