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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- Nonstationary stochastic rain type generation: accounting for climate drivers L. Benoit et al. 10.5194/hess-24-2841-2020
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- Intraday probabilistic forecasts of surface solar radiation with cloud scale-dependent autoregressive advection A. Carpentieri et al. 10.1016/j.apenergy.2023.121775
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- An Improved Precipitation Nowcasting Algorithm Based on COTREC Method Z. Yang et al. 10.1109/TGRS.2024.3446826
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- Applications of directional wavelets, Universal Multifractals and anisotropic scaling in ensemble nowcasting; a review of methods with case‐studies R. Scovell 10.1002/qj.3780
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Latest update: 20 Nov 2024
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...
