Leveraging Crowdsourced Data for Extreme Heat Monitoring

ABSTRACT

The combined effects of urban microclimate heterogeneity and climate change exacerbate the disproportionate impact of heatwaves on urban areas, a trend expected to intensify. Crowdsourcing is a promising tool to monitor temperatures at a high spatiotemporal scale, which is now deemed critical. However, quality control is essential before the use of such data. Traditional quality control methods often fail to capture short extreme weather events like heatwaves, as they frequently eliminate crucial observations from these intense, brief episodes. Here, a quality control methodology, tailored to short‐term heatwaves built on existing quality control methods, is introduced and tested on crowdsourced monitoring networks for five North American cities and three heatwave episodes. This framework is centred around a systematic comparison with traditional weather stations. The results show that the designed procedure can effectively filter out false data points and corrupt stations whilst preserving observational data points capturing heatwaves. In the worst case, 24.7% of a heatwave episode's records are eliminated, compared to 75% using an existing detailed quality control method. We further show that crowdsourced monitoring could bring more insight into the spatiotemporal variability of temperature and living experiences during heatwaves compared to the sparse traditional weather station networks.