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| Titel |
Scaling statistics in a critical, nonlinear physical model of tropical oceanic rainfall |
| VerfasserIn |
K. M. Nordstrom, V. K. Gupta |
| 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 ; 10, no. 6 ; Nr. 10, no. 6, S.531-543 |
| Datensatznummer |
250008209
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| Publikation (Nr.) |
 copernicus.org/npg-10-531-2003.pdf |
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| Zusammenfassung |
| Over the last two decades, concepts of scale
invariance have come to the fore in both modeling and data analysis in
hydrological precipitation research. With the advent of the use of the
multiplicative random cascade model, these concepts have become
increasingly more important. However, unifying this statistical view of
the phenomenon with the physics of rainfall has proven to be a rather
nontrivial task. In this paper, we present a simple model, developed
entirely from qualitative physical arguments, without invoking any
statistical assumptions, to represent tropical atmospheric convection over
the ocean. The model is analyzed numerically. It shows that the data from
the model rainfall look very spiky, as if generated from a random field
model. They look qualitatively similar to real rainfall data sets from
Global Atmospheric Research Program (GARP) Atlantic Tropical Experiment
(GATE). A critical point is found in a model parameter
corresponding to the Convective Inhibition (CIN), at which rainfall
changes abruptly from non-zero to a uniform zero value over the entire
domain. Near the critical value of this parameter, the model rainfall
field exhibits multifractal scaling determined from a fractional wetted
area analysis and a moment scaling analysis. It therefore must exhibit
long-range spatial correlations at this point, a situation qualitatively
similar to that shown by multiplicative random cascade models and GATE
rainfall data sets analyzed previously (Over and Gupta, 1994; Over, 1995).
However, the scaling exponents associated with the model data are
different from those estimated with real data. This comparison identifies
a new theoretical framework for testing diverse physical hypotheses
governing rainfall based in empirically observed scaling statistics. |
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