# Ensemble Radar-Based Rainfall Forecasts for Urban Hydrological Applications
## 5 Conclusions  
This work assessed how radar rainfall uncertainties propagate from the radar rainfall measurements into radar rainfall forecasts, and further on into urban sewer forecasting. The work also compared the accuracy of flow forecast prediction using two different rainfall ensemble generators. A stochastic ensemble generator, which adds spatially correlated noise to the deterministic forecast, was used as reference (STEPS ensembles). An ensemble generator that adds spatially-correlated noise based on the residual radar error between radar rainfall and rain gauge measurements was used to assess the radar rainfall uncertainties (radar ensembles) and how these uncertainties propagate into the simulated sewer flows of an urban area. Results from the rainfall forecasts show that both ensembles can produce accurate forecasts for lead-times up to 3 h for all rainfall intensities larger or equal to 0.1 mm/h. With intensities larger than 1 mm/h the results vary depending on the event. Skilful forecasts could be produced up to 1 h lead-time in most cases and in some events, it was possible to produce a skilled forecast even when the lead-time was 3 h. For a high rainfall threshold (larger than 3 mm/h) reliable forecasts were produced for at least 1 h lead-time. The radar ensembles produced slightly better results than the STEPS ensembles in the first hour of the forecasts. After this, the radar ensembles lost accuracy more rapidly than the STEPS ensembles. The flow forecasts in the urban areas were generated using an Infoworks CS model. The radar ensembles produced better results than the STEPS ensembles in the first hour of the forecasts and were able to better reproduce the flow peaks. The RMSE mean for the radar ensembles was lower than the RMSE mean for the STEPS ensembles for most of the events in the first hour forecasted. This was true even in cases where the simulated flow using the radar rainfall is overestimated or underestimated, thus being able to better reproduce the flow hydrograph. With lead-times longer than 1 h, all the forecasts lose predictability independently of which ensemble generator is used. The radar ensembles produced reproducible forecasts with improved accuracy at predicting the flow peaks when compared to the STEPS ensembles. However, there were events where neither of the ensembles could adequately forecast the flow peaks. In general, this happened in cases where both, radar and rain gauge simulated flows were very different from the observed flow. The results show a time-lag of some minutes between the measured and forecasted peak flows. However, even with this limitation, it is possible to improve the forecast of peak flows in urban areas using the method proposed in this study, and it can be used in real time to enhance existing warning systems in urban areas with up to one hour lead-time. The nowcast skill can be potentially improved by blending radar nowcasts with NWP forecasts, especially as the forecasting lead time increases. With up to one hour lead-time, the nowcast has a major impact on the forecast and this improvement enhances the flow forecasts during this period. Future works can incorporate uncertainties caused by growth and decay of precipitation using for instance the method described by [PERSON] et al. [PERSON] et al. (2017). [PERSON] conceived the methodology, performed the calculations and wrote the original draft. [PERSON] provided supervision and guidance in every aspect of the research and participate in the editing and review of the paper. The first author acknowledges the funding from Science without Borders Programme and CAPES foundation to carry out this project. The authors would like to thank the UK Met Office for providing the STEPS model and the radar data. We also acknowledge the BADC (British Atmospheric Data Centre) and the UK Environment Agency for providing the radar and rain gauge datasets respectively. We would like to thank Yorkshire Water Services Ltd. for providing the Infoworks CS model and data of the sewer system for the urban area.