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| Titel |
The effect of flow and orography on the spatial distribution of the very short-term predictability of rainfall from composite radar images |
| VerfasserIn |
L. Foresti, A. Seed |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 18, no. 11 ; Nr. 18, no. 11 (2014-11-27), S.4671-4686 |
| Datensatznummer |
250120534
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| Publikation (Nr.) |
 copernicus.org/hess-18-4671-2014.pdf |
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| Zusammenfassung |
| The spatial distribution and scale dependence of the very short-term
predictability of precipitation by Lagrangian persistence of composite radar
images is studied under different flow regimes in connection with the
presence of orographic features. Data from the weather radar composite of
eastern Victoria, Australia, a 500 × 500 km2 domain at 10 min
temporal and 2 × 2 km2 spatial resolutions, covering the period
from February 2011 to October 2012, were used for the analyses. The
scale dependence of the predictability of precipitation is considered by
decomposing the radar rainfall field into an eight-level multiplicative cascade
using a fast Fourier transform. The rate of temporal development of
precipitation in Lagrangian coordinates is estimated at each level of the
cascade under different flow regimes, which are stratified by applying a
k-means clustering algorithm on the diagnosed velocity fields. The
predictability of precipitation is measured by its lifetime, which is
derived by integrating the Lagrangian auto-correlation function. The
lifetimes were found to depend on the scale of the feature as a power law,
which is known as dynamic scaling, and to vary as a function of flow regime.
The lifetimes also exhibit significant spatial variability and are
approximately a factor of 2 longer on the upwind compared with the downwind
slopes of terrain features. The scaling exponent of the spatial power
spectrum also shows interesting geographical differences. These findings
provide opportunities to perform spatially inhomogeneous stochastic
simulations of space–time precipitation to account for the presence of
orography, which may be integrated into design storm simulations and
stochastic precipitation nowcasting systems. |
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