Enhancing Rainfall Spatial Representation through Quality-Controlled Personal Weather Stations

The high spatial and temporal variability of precipitation, especially during short, high-intensity events, is typically not captured by rain gauge networks. Furthermore, the actual precipitation maxima do not necessarily occur at the locations of the rain gauges. This consequently leads to a systematic underestimation of interpolated precipitation amounts (Bárdossy and Anwar, 2023). Since this phenomenon depends on the sample size, i.e., the number of rain gauges, a way to increase the sample size is to use additional data of so-called opportunistic precipitation sensors. A suitable data source is provided by personal weather stations (PWS) equipped with rain gauges, which have exceeded the number of stations operated by national weather services and other authorities. They therefore offer the potential to improve quantitative precipitation estimates (Bárdossy et al. 2021, Graf et al. 2021). 

In this study, we investigate the behaviour of precipitation extremes from interpolations  in the Lazio region in Italy using different rainfall data sets. The Lazio region is characterized by a dense network of approximately 230 professionally maintained rain gauges and more than 300 Netatmo Personal Weather Stations, both providing data in  high temporal resolution Although these stations offer a valuable opportunity to enhance the spatial coverage of rainfall observations, they do not generally comply with professional standards in terms of installation, maintenance, and data reliability, and therefore require a rigorous quality control (QC) procedure. In this study, the most recent QC filters and bias correction methodologies are applied to the PWS dataset. Following the QC process, the performance of the corrected PWS observations is assessed through comparison with co-located professional rain gauges. Furthermore, the potential added value of incorporating PWS data is investigated by analyzing their contribution to the representation of rainfall spatial variability, with particular emphasis on extreme precipitation events, as well as their impact on precipitation interpolation results. The outcomes of this study aim to provide insights into the effective integration of crowdsourced weather observations into operational and research-oriented hydrometeorological applications.

References:

Bárdossy, A., Seidel, J., El Hachem, A.: The use of personal weather station observations to improve precipitation estimation and interpolation, Hydrology and Earth System Sciences, 25, 583-601, 2021. https://doi.org/10.5194/hess-25-583-2021

Bárdossy, A., Anwar, F.: Why do our rainfall–runoff models keep underestimating the peak flows? Hydrology and Earth System Sciences, 27, 1987–2000, 2023. https://doi.org/10.5194/hess-27-1987-2023

Graf, M.,  El Hachem, A., Eisele, M., Seidel, J., Chwala, C., Kunstmann, H., Bárdossy, A.: Rainfall estimates from opportunistic sensors in Germany across spatio-temporal scales. Journal of Hydrology: Regional Studies, 37. https://doi.org/10.1016/j.ejrh.2021.100883