19 citations as recorded by crossref.

1. Ensemble Radar-Based Rainfall Forecasts for Urban Hydrological Applications M. Codo & M. Rico-Ramirez https://doi.org/10.3390/geosciences8080297
2. Characterizing the spatial variations and correlations of large rainstorms for landslide study L. Gao et al. https://doi.org/10.5194/hess-21-4573-2017
3. Rad-cGAN v1.0: Radar-based precipitation nowcasting model with conditional generative adversarial networks for multiple dam domains S. Choi & Y. Kim https://doi.org/10.5194/gmd-15-5967-2022
4. Limits of precipitation nowcasting by extrapolation of radar reflectivity for warm season in Central Europe J. Mejsnar et al. https://doi.org/10.1016/j.atmosres.2018.06.005
5. Relationship between Rainfall Variability and the Predictability of Radar Rainfall Nowcasting Models Z. Liu et al. https://doi.org/10.3390/atmos10080458
6. A non-stationary stochastic ensemble generator for radar rainfall fields based on the short-space Fourier transform D. Nerini et al. https://doi.org/10.5194/hess-21-2777-2017
7. A Strategy to set up Test Dataset and Evaluation Benchmark for Radar Nowcasting of Precipitation in Italy C. Annella et al. https://doi.org/10.1109/JSTARS.2025.3568185
8. Development and verification of a real-time stochastic precipitation nowcasting system for urban hydrology in Belgium L. Foresti et al. https://doi.org/10.5194/hess-20-505-2016
9. Adaptive Blending of Probabilistic Precipitation Forecasts with Emphasis on Calibration and Temporal Forecast Consistency M. Rempel et al. https://doi.org/10.1175/AIES-D-22-0020.1
10. A Reduced-Space Ensemble Kalman Filter Approach for Flow-Dependent Integration of Radar Extrapolation Nowcasts and NWP Precipitation Ensembles D. Nerini et al. https://doi.org/10.1175/MWR-D-18-0258.1
11. Enhancement of radar rainfall estimates for urban hydrology through optical flow temporal interpolation and Bayesian gauge-based adjustment L. Wang et al. https://doi.org/10.1016/j.jhydrol.2015.05.049
12. Nationwide Radar-Based Precipitation Nowcasting—A Localization Filtering Approach and its Application for Germany R. Reinoso-Rondinel et al. https://doi.org/10.1109/JSTARS.2022.3144342
13. Exploring the use of 3D radar measurements in predicting the evolution of single-core convective cells Y. Cheng et al. https://doi.org/10.1016/j.atmosres.2024.107380
14. Precipitation Nowcasting with Orographic Enhanced Stacked Generalization: Improving Deep Learning Predictions on Extreme Events G. Franch et al. https://doi.org/10.3390/atmos11030267
15. Application of optical flow technique to short-term rainfall forecast for some synoptic patterns in Vietnam N. Thu et al. https://doi.org/10.1007/s00704-024-05277-y
16. Joint Intensity and Spatio-Temporal Representation Learning for Extreme Precipitation Nowcasting Z. Pan et al. https://doi.org/10.1109/JSTARS.2025.3590059
17. Nowcasting of Precipitation in the High-Resolution Dallas–Fort Worth (DFW) Urban Radar Remote Sensing Network S. Pulkkinen et al. https://doi.org/10.1109/JSTARS.2018.2840491
18. A Calibrated and Consistent Combination of Probabilistic Forecasts for the Exceedance of Several Precipitation Thresholds Using Neural Networks P. Schaumann et al. https://doi.org/10.1175/WAF-D-20-0188.1
19. Pysteps: an open-source Python library for probabilistic precipitation nowcasting (v1.0) S. Pulkkinen et al. https://doi.org/10.5194/gmd-12-4185-2019