Spatio-Temporal Distribution and Data Quality of Citizen Weather Stations: A Comparative Study of Netatmo and Wunderground in Paris

Citizen weather stations (CWS) have emerged as valuable tools for urban climate research, providing hyperlocal observations that complement traditional meteorological networks. This study evaluates the spatio-temporal distribution and data quality of temperature measurements from two prominent CWS networks—Netatmo and Wunderground—using Paris as a case study. Paris offers a unique research setting due to its high density of CWS and an extensive network of professionally operated weather stations, enabling rigorous data validation.

Temperature data from both networks were analyzed before and after applying quality control techniques using the MetObs Toolkit. The analysis reveals that Netatmo, with a significantly larger number of stations, exhibits higher noise levels and variability, leading to challenges in data reliability. In contrast, Wunderground, despite its smaller network size, provides more consistent and stable temperature data. Key metrics such as data completeness, outlier and gap frequency, and alignment with official meteorological observations were assessed to quantify these differences.

Beyond the Paris case study, the global distributions and densities of Netatmo and Wunderground stations were examined, showcasing their heterogeneous spatial coverage. These insights help researchers identify the most suitable data sources based on specific study requirements, as well as understand their applicability and limitations.

This study underscores the trade-offs between data quantity, quality, and accessibility in CWS networks, providing valuable guidance for integrating these datasets into urban climate research and supporting data-driven climate adaptation strategies.