DOI: 10.5194/hess-21-2777-2017
Scopus记录号: 2-s2.0-85020382993
论文题名: A non-stationary stochastic ensemble generator for radar rainfall fields based on the short-space Fourier transform
作者: Nerini D ; , Besic N ; , Sideris I ; , Germann U ; , Foresti L
刊名: Hydrology and Earth System Sciences
ISSN: 10275606
出版年: 2017
卷: 21, 期: 6 起始页码: 2777
结束页码: 2797
语种: 英语
Scopus关键词: Anisotropy
; Fourier transforms
; Meteorological radar
; Power spectrum
; Precipitation (meteorology)
; Radar
; Rain
; Space-based radar
; Wavelet analysis
; Weather forecasting
; White noise
; Convective precipitation
; Geophysical applications
; Numerical weather prediction
; Spatial autocorrelations
; Spatial correlation structures
; Statistical properties
; Statistical structures
; Traditional approaches
; Stochastic systems
; anisotropy
; autocorrelation
; climate prediction
; correlation
; ensemble forecasting
; Fourier transform
; heterogeneity
; orographic effect
; precipitation (climatology)
; radar
; rainfall
; spectrum
; stochasticity
; white noise
英文摘要: 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 variability 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 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 for 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 fourth 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 numerical weather prediction (NWP) downscaling, and also for other geophysical applications involving the simulation of complex non-stationary fields. © Author(s) 2017. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79154
Appears in Collections: 气候变化事实与影响
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作者单位: Radar, Satellite and Nowcasting Division, MeteoSwiss, Locarno-Monti, Switzerland; Institute for Atmospheric and Climate Science, ETH, Zurich, Switzerland; Environmental Remote Sensing Laboratory, EPFL, Lausanne, Switzerland
Recommended Citation:
Nerini D,, Besic N,, Sideris I,et al. A non-stationary stochastic ensemble generator for radar rainfall fields based on the short-space Fourier transform[J]. Hydrology and Earth System Sciences,2017-01-01,21(6)