# Ensemble Radar-Based Rainfall Forecasts for Urban Hydrological Applications
## 1 Introduction
This is because NWP models have been shown to have better forecasting skill after several hours lead-time and can improve the ability to forecast growths and decays of precipitation when blended with a nowcasting model [7]. NWP models require high computing power, and although the forecast resolutions are increasing [34; 35], they are still more computationally demanding than nowcasting models. Blending nowcasting with NWP forecasts has been shown to improve the forecasting skill [9], and it has been successfully used in real-time applications [7; 36]. The uncertainties in the radar rainfall analysis and the temporal evolution of precipitation were accounted for by using a stochastic perturbation system, in which probabilistic forecasts are produced by adding spatially correlated stochastic noise to the deterministic forecast. Although the STEPS performance is higher than GANDOLF's, results for moderate and heavy rain still do not match up accurately with the observed precipitation. A newer version of STEPS has been developed to take into account radar errors [36], using a statistical model to generate ensembles proposed by [PERSON] et al. [14]. Even with extensive research to correct radar errors and improve rainfall estimation, residual errors are present in processed radar images, and they are an important source of errors in the first hours of the forecasts [14; 31; 37]. Ensemble forecasts consist of a number of different forecasts, all of them equally likely, that might occur in the same space and time. Ensemble forecasts can be obtained by adding spatially correlated stochastic noise as described above to the initial radar analyses, in order to produce a series of equally likely forecasts. An advantage of producing ensemble radar-based forecasts is that it provides additional information about the uncertainties in the forecasts, either from the nowcasting model or from the radar analyses [5; 9; 34; 38]. [PERSON] et al. [15] proposed a radar rainfall ensemble scheme that uses rain gauge data as a reference to estimate the residual errors in radar rainfall. This approach was coupled with a semi-distributed rainfall-runoff model to demonstrate the propagation of the radar rainfall uncertainty in the simulation of river flows [14]. [PERSON] et al. [37] implemented the model proposed by [PERSON] et al. [14] and assessed how uncertainties in radar rainfall propagate into urban drainage flows. The results showed that this model is able to capture the temporal and spatial correlations of the radar rainfall errors. The results also showed that the uncertainties in radar rainfall could explain some of the uncertainties observed in the simulated flow volumes, but additional uncertainties in the sewer flow model structure play an important role. As shown in this section, the majority of past papers attempt to model the uncertainties in radar rainfall, but this leaves a research gap with regards to assessing and quantifying the value of radar rainfall ensembles in short-term rainfall forecasting (i.e., nowcasting), as well as in urban sewer flow forecasting. This study aims to model radar rainfall uncertainties in radar nowcasting to assess how these uncertainties propagate in the prediction of sewer flows of a small urban area. Section 2 of this paper describes the methodology and the data sets used. The results from the rainfall probabilistic forecasts and the results of applying these ensembles in the hydraulic model are shown in Section 3. A discussion of the results is presented in Section 4 and, Section 5 summarises the conclusions of this work.