Articles | Volume 21, issue 6
Research article
09 Jun 2017
Research article |  | 09 Jun 2017

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

Daniele Nerini, Nikola Besic, Ioannis Sideris, Urs Germann, and Loris Foresti

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Cited articles

Atencia, A. and Zawadzki, I.: A comparison of two techniques for generating nowcasting ensembles. Part I: Lagrangian ensemble technique, Mon. Weather Rev., 142, 4036–4052, 2014.
Badas, M. G., Deidda, R., and Piga, E.: Modulation of homogeneous space-time rainfall cascades to account for orographic influences, Nat. Hazards Earth Syst. Sci., 6, 427–437,, 2006.
Bárdossy, A. and Hörning, S.: Gaussian and non-Gaussian inverse modeling of groundwater flow using copulas and random mixing, Water Resour. Res., 52, 4504–4526, 2016.
Berenguer, M., Sempere-Torres, D., and Pegram, G. G. S.: SBMcast – an ensemble nowcasting technique to assess the uncertainty in rainfall forecasts by Lagrangian extrapolation, J. Hydrol., 404, 226–240, 2011.
Boisvert, J. B., Manchuk, J. G., and Deutsch, C. V.: Kriging in the presence of locally varying anisotropy using non-Euclidean distances, Math. Geosci., 41, 585–601,, 2009.
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.