Flooding in cities, known as Urban Pluvial Flooding (UPF), causes disruption of society, damage to cities and inconvenience for people. Cities are expected to become more vulnerable to UPF due to more frequent and more intense extreme rainfall events with high spatial variability as result of climate change. Additionally increasing urbanisation results in more impervious surfaces, inducing shorter response times of the drainage systems. A high spatial and temporal resolution of the urban rainfall network is required to forecast UPF. Professional rain gauge networks do not provide this necessary resolution. A strategy to increase this density is to use rainfall data obtained from Citizen Weather Stations (CWSs). The downside of CWS networks is the low quality of measurements compared to professional measurements. This is caused by the use of lower quality sensor, web-platform processes , set-up of the weather station, stability of data transfer and cleaning of the CWS. Undetected Rainfall (UR) is one of the errors found in rain gauge data of a CWS network. An UR error is an incorrect zero rainfall value in the rainfall data, meaning that rainfall did occur at the gauge but it was not detected. A quality-control system that creates a more reliable CWS network is required to facilitate UPF forecasting. The aim of this research is to take a first step towards a quality-control system by developing and testing different automatic quality-control filters that flag for UR errors in rainfall data of a CWS network.

For this research 753 CWSs in the Rotterdam-The Hague region are used. They were obtained from Netatmo weathermap and give a density of 1 CWSs per 5.93 km2. This is not a uniform density because more CWSs are located in densely populated areas. The availability of the CWSs grew from 369 to 668, measuring at least once per day between October 2015 and October 2017. Two filters are developed to identify UR errors in CWS rainfall data. The first filter is the CWS filter which uses neighbouring CWSs within a radius of 8 kilometre to find the UR errors. The second filter is the radar filter, which identifies UR errors using the overlying radar pixel. The performances of these filters are tested by artificially placing zeros at 10% of the rainfall sequences of 10-min rainfall data from four automatic KNMI gauge datasets within the study area. Both filters are applied on these datasets and evaluated using confusion matrices. Finally, the two filters are applied on the 753 CWS rainfall datasets for a period between October 2016 and October 2017.