# Ensemble Radar-Based Rainfall Forecasts for Urban Hydrological Applications
## 4 Discussion
### Forecasting Sewer Flows  
In the event shown in Figure 11, the radar probabilistic forecast is able to simulate the peaks in the first hour of the forecast and also accurately simulate the second flow peak. The STEPS probabilistic forecasts underestimated both flow peaks. For this event, it is clear that using rain gauge data to produce the radar rainfall ensembles adds valuable information to improve the forecasts. This allows the flow peak and flows patterns to be forecasted better using the radar ensembles. For radar ensembles, the measured flow (0.509 m\({}^{3}\)/s) is very close to the 75 th percentile flow (0.418 m\({}^{3}\)/s). For the STEPSensembles, the measured flow is only captured by the more extreme ensembles, as can be seen on the boxplot. The flow simulations using radar data and the deterministic forecast show similar results to the STEPS ensembles peak flows, both underestimating the flow peaks. The simulation using rain gauge data replicate the first flow peak more accurately but underestimate the second one. The radar ensembles have the advantage of combining information from both the radar and the rain gauges and are able to predict both flow peaks. The second large flow peak occurred around 2 h lead-time, and cannot be forecasted by any of the probabilistic or deterministic forecasts. This indicates that at lead-time longer than 1 h the flow forecast loses its forecasting skill. For this event, the radar ensembles perform better in analysing the RMSE mean. For the first hour, the mean is approximately 15% smaller for radar ensembles when compared to the STEPS ensembles. The simulated flow peak in Figure 12 is overestimated using radar rainfall. Flow simulations using rain gauge data are much closer to the measured peak flow, but there is a delay in time of a few minutes. In this example, both radar and STEPS forecasts can capture the flow peak, however, the radar ensembles produce a flow forecast with a higher spread than the STEPS forecast. The measured peak flow (0.563 m\({}^{3}\)/s) falls between the 25 th and 75 th percentile for the radar ensembles (Figure 12b) in the first forecasted hour. In the second hour of the forecast, the STEPS ensembles forecast the flow intensity more accurately than the radar ensembles. The radar ensembles produce a much larger spread and therefore there is an overestimation of the flow by a large part of the ensembles. This leads to an increased RMSE (0.287 m\({}^{3}\)/s) for the first hour of the forecasts and a higher value than using the STEPS ensembles (0.0169 m\({}^{3}\)/s). The STEPS ensembles produce a smaller RMSE mean for the whole duration of the event. In Figure 13, the nowcasting model's efficiency is rapidly lost with increased lead-time. This figure shows the results of forecasting the same rainfall event but at different starting times (30 min apart). The forecasts for both ensembles initiated at 02:30 on 1 August 2008 (Figure 13a) fail to predict the flow peak correctly, and the time lag between the forecasted flow and the measured flow is higher than for shorter lead-times. In the forecasts initiated at 03:00 (Figure 13b), the peak flow falls into the first forecasted hour and the forecast produced replicates the peak flow better. In this case both ensembles were able to capture the peak flow, however, only some of the radar ensembles can reproduce the peak flow correctly. Because the STEPS has a higher spread, the peak flow can be forecasted by more ensemble members under these circumstances. The event presented in Figure 13 presents the highest measured flow among all the selected events. Accurately forecasting high rainfall intensities has proved to be more challenging and both forecasts were able to predict the peaks at a short forecasting lead-time. The RMSE mean for the forecast initiated at 02:30 is higher (0.405 m\({}^{3}\)/s for radar ensembles and 0.430 m\({}^{3}\)/s for STEPS ensembles), and in contrast to the other events, the flows were forecasted with a delay of around 20 min.