Automated quality-control filters for undetected rainfall in citizen rain gauge data

Ministry of Infrastructure and Water Management, Royal Netherlands Meteorological Institute, R.K. Hutten, Technische Universiteit Delft (TU Delft)
De Bilt : KNMI

Flooding in cities, known as Urban Pluvial Flooding (UPF), causes disruption of society, damage to cities and inconvenience for people (Douglas et al., 2010; Spekkers et al., 2015). 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 (Ashley et al., 2005; Hartmann et al., 2013). 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, webplatform processes , set-up of the weather station, stability of data transfer and cleaning of the CWS (de Vos et al., 2017). 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 aimof 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.

94 p.
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(Internal report ; 2018-04)
With ref.