It is well acknowledged that there are large uncertainties associated with radar-based
estimates of rainfall. Numerous sources of these errors are due to parameter estimation, the …

Hydrology requires accurate and reliable rainfall input. Because of the strong spatial and
temporal variability of precipitation, estimation of spatially distributed rain rates is …

Pysteps is an open-source and community-driven Python library for probabilistic
precipitation nowcasting, that is, very-short-range forecasting (0–6 h). The aim of pysteps is …

Uncertainty is a fundamental characteristic of weather, seasonal climate, and hydrological
prediction, and no forecast is complete without a description of its uncertainty. Effective …

Although it is broadly acknowledged that the radar-rainfall (RR) estimates based on the US
national network of Weather Surveillance Radar-1988 Doppler (WSR-88D) stations contain …

The uncertainty structure of radar quantitative precipitation estimation (QPE) is largely
unknown at fine spatiotemporal scales near the radar measurement scale. By using the …

There are significant uncertainties inherent in precipitation forecasts and these uncertainties
can be communicated to users via large ensembles that are generated using stochastic …

The Short-Term Ensemble Prediction System (STEPS) is implemented in real-time at the
Royal Meteorological Institute (RMI) of Belgium. The main idea behind STEPS is to quantify …

The recent intensification of extreme rainfall events has the potential to aggravate the
frequency and intensity of floods in urban areas and signals the urgent need for accurate …

Quantification of rainfall and its spatial and temporal variability is extremely important for
reliable hydrological and meteorological modeling. While rain gauge measurements do not …