# Ensemble Radar-Based Rainfall Forecasts for Urban Hydrological Applications
## 2 Materials and Methods  
Composite radar data from the UK Met Office, available through the British Atmospheric Data Centre (BADC) [39] were used in this study. The radar data has a temporal resolution of 5 min and a spatial resolution of 1 km \(\times\) 1 km. The dataset used in this study is from the year 2008 and can be correlated with the available flow observations. The UK Met Office radar network consists of C-band weather radars capable of producing high-resolution precipitation data for the UK [39]. Ground clutter is identified using ground clutter maps. The \(Z\)-\(R\) equation is constant for all types of rainfall (\(a=200\) and \(b=1.6\)). The radar data processing includes an algorithm to correct for rain attenuation, which can be significant at C-band frequencies. The algorithm can be unstable in cases of severe attenuation and further uncertainties can occur with attenuation correction in cases where the weather radars are not properly calibrated [17]. Currently, there are 18 C-band weather radars in the UK [37]. Most of these radars have been upgraded with dual-polarisation technology in the last few years. This will result in significant improvements in terms of data quality (e.g., better identification of non-meteorological echoes), attenuation correction and rainfall estimation. Images from adjacent radars and previous images from the same radar are analysed in order to discard corrupted images. Anomalous propagation is removed from the radar data using a combination of the radar data and Meteosat images. In order to take into account the variation in the vertical reflective profile, an idealized vertical profile is identified at each radar pixel and this is defined by the background reflectivity factor and incorporates simple parameterizations. A radar horizon is used to correct for occultation of the radar beam [17]. The Met Office radar data processing system also includes correction algorithms for uncertainties due to noise filtering, antenna pointing, mean field bias and conversion from Cartesian to polar coordinates [40]. Rain gauge data was provided by the UK Environmental Agency. The rain gauge data is freely available upon request ([EMAIL_ADDRESS]) under the Open Government License. A network of 229 tipping bucket rain gauges (TBRs) with 15 min temporal resolution were available for the study area in the north of England. Although rain gauges are used as a reference and are known to be reliable precipitation instruments, they are also subject to measurement errors. Uncertainties are related to gauge calibration, wind effect, wetting-evaporation losses, timing-errors, hydrodynamic water flow instabilities, blockages, malfunction and underestimation during heavy rainfall [37; 41]. In order to minimise inaccuracies in measurements different rain gauges were compared, and only reliable rain gauge data was used. Rain gauge data that presented significant deviation when compared to the surrounding rain gauges or showed an anomalous behaviour (e.g., blockages) were discarded (22 rain gauges in our case). It is also necessary to deal with sampling errors when comparing weather radar and rain gauge data. It is also worth recognising that rain gauge measurements are point measurements at ground level, whereas weather radar measurements occur at a higher altitude and with a larger sampling volume in space. Therefore, part of the discrepancies between both measurements are due to differences in the sampling volumes [42]. Providing the rain gauge data is quality-controlled, they can be considered as ground true measurements to validate the radar rainfall observations. A sequence of three radar images with a time-step of 5 min were used to produce forecasts every 5 min with a 3-h forecasting lead-time and 1 km spatial resolution. The STEPS model provided by the UK Met Office was used in this analysis. STEPS is a rainfall-forecasting model that blends rainfall extrapolation nowcasts with NWP rainfall forecasts.