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| Titel |
Causal signatures in rainfall cascade: a wavelet approach |
| VerfasserIn |
Gabriel Katul, Annalisa Molini, Amilcare Porporato |
| Konferenz |
EGU General Assembly 2010
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 12 (2010) |
| Datensatznummer |
250043783
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| Zusammenfassung |
| A central topic in rainfall research is to determine whether rainfall variability at a given
space-time scale is caused by dynamics acting at some other scales. Random multiplicative
cascades (RMCs) are standard approaches for describing rainfall variability across a
wide range of time scales. Their popularity stems from their ability to reproduce
rainfall self-similarity and long-range correlations as well as intermittency buildup at
finer scales. However, standard RMCs only predict instantaneous flow of variance
(energy or activity) from large to fine scales and cannot account for scale-wise
causal relationships. Such relationships reveal themselves through non-instantaneous
cascade mechanisms – namely large scale events influencing finer scale events at later
times (i.e. forward causal cascade) or conversely (inverse causal cascade). The
presence of causal cascade signatures within the rainfall process is explored here
using both continuous wavelet decomposition (CWT) and scale-by-scale causality
measures such as cross-scale correlation and linearized transfer entropy. The causality
hypothesis is further tested against results from toy models, surrogate data, and a scalar
turbulence time series (water vapor) to ensure that rainfall causality is not an artifact
of the estimation method or resulting from the redundancy in CWT. The analysis
demonstrates the presence of causal cascades (mainly forward) in rainfall series when
sampled at fine temporal resolutions (seconds). These causal relationships tend
to vanish when rainfall is aggregated at coarser time scales (hours and longer). |
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