Quality Control for Crowdsourced Personal Weather Stations to Enable Operational Rainfall Monitoring

Abstract

Automatic personal weather stations owned and maintained by weather enthusiasts provide spatially dense in situ measurements that are often collected and visualized in real time on online weather platforms. While the spatial and temporal resolution of this data source is high, its rainfall observations are prone to typical errors, currently preventing its large-scale, real-time application. This study proposes a quality control methodology consisting of four modules targeting these errors, applicable in real time without requiring auxiliary measurements. The quality control improves the overall accuracy of a year of hourly rainfall depths in Amsterdam to a bias of −11.3% (0.2% when a proxy for overall rainfall underestimation by personal weather stations is used), a Pearson correlation coefficient of 0.82, and a coefficient of variation of 2.70, while maintaining 88% of the original data set. Application on a national scale (average 1 station per ∼10 km2) yields high-resolution nationwide rainfall maps, hence showing the great potential of personal weather stations for complementing existing often sparse traditional rain gauge networks.