21 citations as recorded by crossref.

1. Forecasting High-Flow Discharges in a Flashy Catchment Using Multiple Precipitation Estimates as Predictors in Machine Learning Models A. Zanchetta et al. https://doi.org/10.3390/hydrology9120216
2. Analysis of Rainfall Distribution in Malaysia through the Employment of Hydro-Estimator Data N. Aminudin et al. https://doi.org/10.48084/etasr.7601
3. Characterizing Rainfall Discrepancies Between Landslide Sites and the Nearest Rain Gauges Using Radar Estimates: A Case Study from Italy C. Vennari et al. https://doi.org/10.3390/rs18091435
4. Assessment of GPM and SM2RAIN-ASCAT rainfall products over complex terrain in southern Italy F. Chiaravalloti et al. https://doi.org/10.1016/j.atmosres.2018.02.019
5. Long‐term hydrological assessment of remote sensing precipitation from multiple sources over the lower Yangtze River basin, China D. Zhu et al. https://doi.org/10.1002/met.1991
6. Runoff generation in a semiarid environment: The role of rainstorm intra-event temporal variability and antecedent soil moisture S. Assouline et al. https://doi.org/10.1016/j.advwatres.2024.104715
7. Prediction of river temperature surges is dependent on precipitation method D. Croghan et al. https://doi.org/10.1002/hyp.13317
8. Learning ensembles of deep neural networks for extreme rainfall event detection G. Folino et al. https://doi.org/10.1007/s00521-023-08238-0
9. A GIS-Based Methodology to Combine Rain Gauge and Radar Rainfall Estimates of Precipitation Using the Conditional Merging Technique for High-Resolution Quantitative Precipitation Forecasts in Țibleș and Rodnei Mountains I. Kocsis et al. https://doi.org/10.3390/atmos13071106
10. A Machine Learning Approach for Rainfall Estimation Integrating Heterogeneous Data Sources M. Guarascio et al. https://doi.org/10.1109/TGRS.2020.3037776
11. Integrated Rainfall Estimation Using Rain Gauges and Weather Radar: Implications for Rainfall-Induced Landslides M. De Biase et al. https://doi.org/10.3390/rs17213629
12. Multi-parameter Doppler Weather Radar analysis of an extreme rain event in central India: Precursors of convective intensification, sectoral variability and storm structure T. Sreekanth et al. https://doi.org/10.1016/j.atmosres.2026.108952
13. The Role of Weather Radar in Rainfall Estimation and Its Application in Meteorological and Hydrological Modelling—A Review Z. Sokol et al. https://doi.org/10.3390/rs13030351
14. Forecasting heavy precipitation in Portugal: AROME numerical prediction model performance J. Cruz et al. https://doi.org/10.1007/s11069-026-08008-w
15. Flood Simulation in South Carolina Watersheds Using Different Precipitation Inputs P. Gao et al. https://doi.org/10.1155/2018/4085463
16. Application of Radar-Based Precipitation Data Improves the Effectiveness of Urban Inundation Forecasting D. Tri et al. https://doi.org/10.3390/su16093736
17. Geomorphic effects caused by heavy rainfall in the Corigliano-Rossano area (NE Calabria, Italy) on 12 August 2015 V. Rago et al. https://doi.org/10.1080/17445647.2020.1867248
18. Improving Mean Annual Precipitation Prediction Incorporating Elevation and Taking into Account Support Size G. Buttafuoco & M. Conforti https://doi.org/10.3390/w13060830
19. Performance of ECMWF forecasts for convective events using radar-based and multi-source observations J. Cruz et al. https://doi.org/10.1016/j.atmosres.2026.109051
20. A Comparative Evaluation of Using Rain Gauge and NEXRAD Radar-Estimated Rainfall Data for Simulating Streamflow S. Ahmed et al. https://doi.org/10.3390/hydrology9080133
21. Urban flooding analysis for event reconstruction in historical cities: the case study of Matera (Italy) S. Dal Sasso et al. https://doi.org/10.3389/fbuil.2026.1729071