Exploratory analysis of hourly observations of temperature measured by citizen observations over Scandinavia

Today, the volume of data monitoring atmospheric conditions near the Earth’s surface is vast and continually expanding. Among the most rapidly developing sources are observational networks composed of stations managed by citizens. From the perspective of national meteorological services, this citizen-generated data presents an opportunity to enhance the existing networks of traditional weather stations operated by public institutions. For variables like temperature and precipitation, the presence of a dense network that delivers data at hourly or finer sampling rates enables the detailed reconstruction of weather phenomena occurring between the microscale and the mesoscale (i.e. from hundreds of kilometres down to 1-2 kilometres or less). The specific applications of the work we present are: i) development of quality control tests; ii) development of post-processing of observational and gridded datasets, aiming at providing enhanced gridded datasets reconstructing atmospheric variables near the surface.

In previous studies, we analyzed hourly precipitation data collected by a network of citizen-operated stations in Finland, Norway, and Sweden from September 2019 to October 2022. These observations were gathered using Netatmo weather stations, which are commercially available and installed in private homes for various purposes, including home automation. We compared this crowdsourced data with reference observations from WMO-compliant stations managed by the national weather services of the three countries. Our findings indicate that while reference observations consistently fall within the empirical distribution of the crowdsourced data, there are signs that intense precipitation events may be underestimated by crowdsourced data. Further investigation into the spatial variability of crowdsourced precipitation revealed significant deviations between measurements from locations as close as 1 to 5 km apart, with differences reaching up to 50% of the mean hourly precipitation in the area. This variation is partially attributed to the suboptimal siting and exposure of some Netatmo stations. However, it also provides an estimate of the inherent variability of precipitation over short distances.

In this study, we expand our research to include hourly temperature data collected by the same network of crowdsourced stations. We aim to address several research questions concerning the representativeness errors of citizen observations compared to WMO-compliant observations of hourly temperature. Specifically, we investigate whether systematic deviations exist between the two data sources, whether these deviations vary by season, and how accurately a normal distribution can model these deviations. Additionally, we will explore the spread of this normal distribution. Our research also seeks to quantify the typical variability in space of the deviations between crowdsourced data and reference observations, aiming to provide a more comprehensive understanding of representativeness errors.