Articles | Volume 21, issue 6
https://doi.org/10.5194/hess-21-2777-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-21-2777-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
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
CORRESPONDING AUTHOR
Radar, Satellite and Nowcasting Division, MeteoSwiss,
Locarno-Monti, Switzerland
Institute for Atmospheric and Climate Science, ETH, Zurich,
Switzerland
Nikola Besic
Radar, Satellite and Nowcasting Division, MeteoSwiss,
Locarno-Monti, Switzerland
Environmental Remote Sensing Laboratory, EPFL, Lausanne,
Switzerland
Ioannis Sideris
Radar, Satellite and Nowcasting Division, MeteoSwiss,
Locarno-Monti, Switzerland
Urs Germann
Radar, Satellite and Nowcasting Division, MeteoSwiss,
Locarno-Monti, Switzerland
Loris Foresti
Radar, Satellite and Nowcasting Division, MeteoSwiss,
Locarno-Monti, Switzerland
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- NowPrecip: localized precipitation nowcasting in the complex terrain of Switzerland I. Sideris et al. https://doi.org/10.1002/qj.3766
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- Radar-based characterisation of heavy precipitation in the eastern Mediterranean and its representation in a convection-permitting model M. Armon et al. https://doi.org/10.5194/hess-24-1227-2020
- Nonstationary stochastic rain type generation: accounting for climate drivers L. Benoit et al. https://doi.org/10.5194/hess-24-2841-2020
- Scale‐dependent blending of ensemble rainfall nowcasts and numerical weather prediction in the open‐source pysteps library R. Imhoff et al. https://doi.org/10.1002/qj.4461
- A quest for precipitation attractors in weather radar archives L. Foresti et al. https://doi.org/10.5194/npg-31-259-2024
- A triple collocation-based 2D soil moisture merging methodology considering spatial and temporal non-stationary errors J. Zhou et al. https://doi.org/10.1016/j.rse.2021.112509
- Intraday probabilistic forecasts of surface solar radiation with cloud scale-dependent autoregressive advection A. Carpentieri et al. https://doi.org/10.1016/j.apenergy.2023.121775
- NowPrecip 2: Precipitation nowcasting in the complex terrain of Switzerland. Part I: Methods I. Sideris et al. https://doi.org/10.1002/qj.70171
- An Event-Based Stochastic Parametric Rainfall Simulator (ESPRS) for Urban Stormwater Simulation and Performance in a Sponge City Y. Yang et al. https://doi.org/10.3390/w15081561
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- Designing an inundation monitoring and real-time urban flood forecasting system: a synthetic study V. Tran et al. https://doi.org/10.1016/j.jhydrol.2026.135520
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- A localisation strategy for scalable and efficient multi-source precipitation blending at high resolutions over large domains Y. Zhang et al. https://doi.org/10.1016/j.jhydrol.2026.135831
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- Optimal Temporal Resolution of Rainfall for Urban Applications and Uncertainty Propagation F. Cecinati et al. https://doi.org/10.3390/w9100762
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- Autocorrelation structure of convective rainfall in semiarid-arid climate derived from high-resolution X-Band radar estimates F. Marra & E. Morin https://doi.org/10.1016/j.atmosres.2017.09.020
- SAD: Verifying the scale, anisotropy and direction of precipitation forecasts S. Buschow & P. Friederichs https://doi.org/10.1002/qj.3964
- A framework for space–time modelling of rainfall events for hydrological applications of weather radar A. Green et al. https://doi.org/10.1016/j.jhydrol.2024.130630
- A Hybrid Model for Fast and Probabilistic Urban Pluvial Flood Prediction X. Li & P. Willems https://doi.org/10.1029/2019WR025128
- Kriging‐variance‐based multi‐member ensembles of radar–rain‐gauge precipitation estimates: Application in Switzerland I. Sideris et al. https://doi.org/10.1002/qj.4990
- Characterisation of the melting layer variability in an Alpine valley based on polarimetric X-band radar scans F. van den Heuvel et al. https://doi.org/10.5194/amt-11-5181-2018
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Saved (final revised paper)
Latest update: 24 Jul 2026
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...
