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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
doi:10.5194/hess-2016-668
© Author(s) 2017. This work is distributed
under the Creative Commons Attribution 3.0 License.
Research article
01 Feb 2017
Review status
A revision of this discussion paper is under review for the journal Hydrology and Earth System Sciences (HESS).
A non-stationary stochastic ensemble generator for radar rainfall fields based on the Short-Space Fourier Transform
Daniele Nerini1,2, Nikola Besic1,3, Ioannis Sideris1, Urs Germann1, and Loris Foresti1 1Radar, Satellite and Nowcasting Division, MeteoSwiss, Locarno-Monti, Switzerland
2Institute for Atmospheric and Climate Science, ETH, Zurich, Switzerland
3Environmental Remote Sensing Laboratory, EPFL, Lausanne, Switzerland
Abstract. In this paper we present a non-stationary stochastic generator for radar rainfall fields based on the Short-Space Fourier Transform (SSFT). The statistical properties of rainfall fields often exhibit significant spatial heterogeneity due to differences in the involved physical processes and influence of orographic forcing. The traditional approach to simulate stochastic rainfall fields based on the Fourier filtering of white noise, also known as fractional Brownian noise integration, is only able to reproduce the global power spectrum and spatial autocorrelation of the precipitation fields. Conceptually similar to wavelet analysis, the SSFT is a simple and effective extension of the Fourier transform developed for space-frequency localisation, which allows using windows to better capture the local statistical structure of rainfall. The SSFT is used to generate stochastic noise and precipitation fields that replicate the local spatial correlation structure, i.e. anisotropy and correlation range, of the observed radar rainfall fields. The potential of the stochastic generator is demonstrated using four precipitation cases observed by the 4th generation of Swiss weather radars that display significant non-stationarity due to the coexistence of stratiform and convective precipitation, differential rotation of the weather system and locally varying anisotropy. The generator is verified in its ability to reproduce both the global and the local Fourier power spectra of the precipitation field. The SSFT-based stochastic generator can be applied and extended to improve the probabilistic nowcasting of precipitation, design storm simulation, stochastic NWP downscaling and also for other geophysical applications involving the simulation of complex non-stationary fields.

Citation: Nerini, D., Besic, N., Sideris, I., Germann, U., and Foresti, L.: A non-stationary stochastic ensemble generator for radar rainfall fields based on the Short-Space Fourier Transform, Hydrol. Earth Syst. Sci. Discuss., doi:10.5194/hess-2016-668, in review, 2017.
Daniele Nerini et al.
Daniele Nerini et al.
Daniele Nerini et al.

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Short summary
Stochastic generators are effective tools for the quantification of uncertainty in a number of applications with weather radar data, including quantitative precipitation estimation and very short-term forecasting. However, most of the current stochastic rainfall field generators cannot handle spatial non-stationarity. We propose an approach based on the Short-space Fourier transform which aims to reproduce the local spatial structure of the observed rainfall fields.
Stochastic generators are effective tools for the quantification of uncertainty in a number of...
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