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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 18, issue 11
Hydrol. Earth Syst. Sci., 18, 4671–4686, 2014
https://doi.org/10.5194/hess-18-4671-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Precipitation: measurement and space time variability

Hydrol. Earth Syst. Sci., 18, 4671–4686, 2014
https://doi.org/10.5194/hess-18-4671-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 27 Nov 2014

Research article | 27 Nov 2014

The effect of flow and orography on the spatial distribution of the very short-term predictability of rainfall from composite radar images

L. Foresti1,2,* and A. Seed2 L. Foresti and A. Seed
  • 1Royal Meteorological Institute of Belgium, Brussels, Belgium
  • 2Bureau of Meteorology, Centre for Australian Weather and Climate Research, Melbourne, Australia
  • *now at: Royal Meteorological Institute of Belgium, Brussels, Belgium

Abstract. 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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