English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this paper we present a non-stationary stochastic generator for radar rainfall fields based\udon the short-space Fourier transform (SSFT). The statistical properties of\udrainfall fields often exhibit significant spatial heterogeneity due to\udvariability in the involved physical processes and influence of orographic\udforcing. The traditional approach to simulate stochastic rainfall fields\udbased on the Fourier filtering of white noise is only able to reproduce the\udglobal power spectrum and spatial autocorrelation of the precipitation\udfields. Conceptually similar to wavelet analysis, the SSFT is a simple and\udeffective extension of the Fourier transform developed for space–frequency\udlocalisation, which allows for using windows to better capture the local\udstatistical structure of rainfall. The SSFT is used to generate stochastic\udnoise and precipitation fields that replicate the local spatial correlation\udstructure, i.e. anisotropy and correlation range, of the observed radar\udrainfall fields. The potential of the stochastic generator is demonstrated\udusing four precipitation cases observed by the fourth generation of Swiss\udweather radars that display significant non-stationarity due to the\udcoexistence of stratiform and convective precipitation, differential rotation\udof the weather system and locally varying anisotropy. The generator is\udverified in its ability to reproduce both the global and the local Fourier\udpower spectra of the precipitation field. The SSFT-based stochastic generator\udcan be applied and extended to improve the probabilistic nowcasting of\udprecipitation, design storm simulation,\udstochastic numerical weather prediction (NWP)\uddownscaling, and also for other geophysical\udapplications involving the simulation of complex non-stationary fields

A non-stationary stochastic ensemble generator for radar rainfall fields based on the short-space Fourier transform