Articles | Volume 18, issue 11
https://doi.org/10.5194/hess-18-4671-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue:
https://doi.org/10.5194/hess-18-4671-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
27 Nov 2014
Research article |
| 27 Nov 2014
The effect of flow and orography on the spatial distribution of the very short-term predictability of rainfall from composite radar images
L. Foresti
CORRESPONDING AUTHOR
Royal Meteorological Institute of Belgium, Brussels, Belgium
Bureau of Meteorology, Centre for Australian Weather and Climate Research, Melbourne, Australia
now at: Royal Meteorological Institute of Belgium, Brussels, Belgium
A. Seed
Bureau of Meteorology, Centre for Australian Weather and Climate Research, Melbourne, Australia
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Cited
18 citations as recorded by crossref.
- Ensemble Radar-Based Rainfall Forecasts for Urban Hydrological Applications M. Codo & M. Rico-Ramirez 10.3390/geosciences8080297
- Characterizing the spatial variations and correlations of large rainstorms for landslide study L. Gao et al. 10.5194/hess-21-4573-2017
- Rad-cGAN v1.0: Radar-based precipitation nowcasting model with conditional generative adversarial networks for multiple dam domains S. Choi & Y. Kim 10.5194/gmd-15-5967-2022
- Limits of precipitation nowcasting by extrapolation of radar reflectivity for warm season in Central Europe J. Mejsnar et al. 10.1016/j.atmosres.2018.06.005
- Relationship between Rainfall Variability and the Predictability of Radar Rainfall Nowcasting Models Z. Liu et al. 10.3390/atmos10080458
- A non-stationary stochastic ensemble generator for radar rainfall fields based on the short-space Fourier transform D. Nerini et al. 10.5194/hess-21-2777-2017
- Development and verification of a real-time stochastic precipitation nowcasting system for urban hydrology in Belgium L. Foresti et al. 10.5194/hess-20-505-2016
- Adaptive Blending of Probabilistic Precipitation Forecasts with Emphasis on Calibration and Temporal Forecast Consistency M. Rempel et al. 10.1175/AIES-D-22-0020.1
- A Reduced-Space Ensemble Kalman Filter Approach for Flow-Dependent Integration of Radar Extrapolation Nowcasts and NWP Precipitation Ensembles D. Nerini et al. 10.1175/MWR-D-18-0258.1
- Enhancement of radar rainfall estimates for urban hydrology through optical flow temporal interpolation and Bayesian gauge-based adjustment L. Wang et al. 10.1016/j.jhydrol.2015.05.049
- Nationwide Radar-Based Precipitation Nowcasting—A Localization Filtering Approach and its Application for Germany R. Reinoso-Rondinel et al. 10.1109/JSTARS.2022.3144342
- Exploring the use of 3D radar measurements in predicting the evolution of single-core convective cells Y. Cheng et al. 10.1016/j.atmosres.2024.107380
- Precipitation Nowcasting with Orographic Enhanced Stacked Generalization: Improving Deep Learning Predictions on Extreme Events G. Franch et al. 10.3390/atmos11030267
- Application of optical flow technique to short-term rainfall forecast for some synoptic patterns in Vietnam N. Thu et al. 10.1007/s00704-024-05277-y
- Joint Intensity and Spatio-Temporal Representation Learning for Extreme Precipitation Nowcasting Z. Pan et al. 10.1109/JSTARS.2025.3590059
- Nowcasting of Precipitation in the High-Resolution Dallas–Fort Worth (DFW) Urban Radar Remote Sensing Network S. Pulkkinen et al. 10.1109/JSTARS.2018.2840491
- A Calibrated and Consistent Combination of Probabilistic Forecasts for the Exceedance of Several Precipitation Thresholds Using Neural Networks P. Schaumann et al. 10.1175/WAF-D-20-0188.1
- Pysteps: an open-source Python library for probabilistic precipitation nowcasting (v1.0) S. Pulkkinen et al. 10.5194/gmd-12-4185-2019
18 citations as recorded by crossref.
- Ensemble Radar-Based Rainfall Forecasts for Urban Hydrological Applications M. Codo & M. Rico-Ramirez 10.3390/geosciences8080297
- Characterizing the spatial variations and correlations of large rainstorms for landslide study L. Gao et al. 10.5194/hess-21-4573-2017
- Rad-cGAN v1.0: Radar-based precipitation nowcasting model with conditional generative adversarial networks for multiple dam domains S. Choi & Y. Kim 10.5194/gmd-15-5967-2022
- Limits of precipitation nowcasting by extrapolation of radar reflectivity for warm season in Central Europe J. Mejsnar et al. 10.1016/j.atmosres.2018.06.005
- Relationship between Rainfall Variability and the Predictability of Radar Rainfall Nowcasting Models Z. Liu et al. 10.3390/atmos10080458
- A non-stationary stochastic ensemble generator for radar rainfall fields based on the short-space Fourier transform D. Nerini et al. 10.5194/hess-21-2777-2017
- Development and verification of a real-time stochastic precipitation nowcasting system for urban hydrology in Belgium L. Foresti et al. 10.5194/hess-20-505-2016
- Adaptive Blending of Probabilistic Precipitation Forecasts with Emphasis on Calibration and Temporal Forecast Consistency M. Rempel et al. 10.1175/AIES-D-22-0020.1
- A Reduced-Space Ensemble Kalman Filter Approach for Flow-Dependent Integration of Radar Extrapolation Nowcasts and NWP Precipitation Ensembles D. Nerini et al. 10.1175/MWR-D-18-0258.1
- Enhancement of radar rainfall estimates for urban hydrology through optical flow temporal interpolation and Bayesian gauge-based adjustment L. Wang et al. 10.1016/j.jhydrol.2015.05.049
- Nationwide Radar-Based Precipitation Nowcasting—A Localization Filtering Approach and its Application for Germany R. Reinoso-Rondinel et al. 10.1109/JSTARS.2022.3144342
- Exploring the use of 3D radar measurements in predicting the evolution of single-core convective cells Y. Cheng et al. 10.1016/j.atmosres.2024.107380
- Precipitation Nowcasting with Orographic Enhanced Stacked Generalization: Improving Deep Learning Predictions on Extreme Events G. Franch et al. 10.3390/atmos11030267
- Application of optical flow technique to short-term rainfall forecast for some synoptic patterns in Vietnam N. Thu et al. 10.1007/s00704-024-05277-y
- Joint Intensity and Spatio-Temporal Representation Learning for Extreme Precipitation Nowcasting Z. Pan et al. 10.1109/JSTARS.2025.3590059
- Nowcasting of Precipitation in the High-Resolution Dallas–Fort Worth (DFW) Urban Radar Remote Sensing Network S. Pulkkinen et al. 10.1109/JSTARS.2018.2840491
- A Calibrated and Consistent Combination of Probabilistic Forecasts for the Exceedance of Several Precipitation Thresholds Using Neural Networks P. Schaumann et al. 10.1175/WAF-D-20-0188.1
- Pysteps: an open-source Python library for probabilistic precipitation nowcasting (v1.0) S. Pulkkinen et al. 10.5194/gmd-12-4185-2019
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