Quality control and correction method for air temperature data from a citizen science weather station network in Leuven, Belgium

Abstract. The growing urbanization trend and increasingly frequent extreme weather events urge further monitoring and understanding of weather in cities. In order to gain information on these intra urban weather patterns, dense high quality atmospheric measurements are needed. Crowdsourced weather stations (CSW) could be a promising solution to reach such monitoring networks in a cost-efficient way. Because of their non-traditional measuring equipment and installation settings, the quality of these datasets remains however an issue of concern. This paper presents crowdsourced data from the Leuven.cool network, a citizen science network of around 100 low-cost weather stations (Fine Offset WH2600) distributed across Leuven, Belgium.  The dataset is accompanied by a newly developed station specific temperature quality control (QC) and correction procedure. The procedure consists of three levels removing implausible measurements, while also correcting for inter (in between stations) and intra (station-specific) station temperature biases by means of a random-forest approach. The evaluation of the QC is performed using data from four WH2600 stations installed next to official weather stations belonging to the Royal Meteorological Institute of Belgium (RMIB). A positive temperature bias with strong relation to the incoming solar radiation was found between the CSW data and official data. The QC method is able to reduce this bias from 0.15 ± 0.56 °C to 0.00 ± 0.22 °C. After evaluation, the QC method is applied to the data of the Leuven.cool network, making it a very suitable data set to study in detail local weather phenomena such as the urban heat island (UHI) effect.