Speaker
Description
Merged weather radar and rain‑gauge products provide the state‑of‑the‑art quantitative precipitation estimation (QPE) from national meteorological services for many hydrometeorological applications. They combine the high spatio‑temporal coverage of weather radar with the point‑scale accuracy of rain gauges to mitigate each instrument’s specific uncertainties, namely the indirect measurement high above ground from radars and limited spatial representativeness from rain gauges. Nevertheless, these products are far from perfect. They inherit observational errors from both sources and add uncertainty through the merging procedure.
One straightforward approach to reduce uncertainty is to increase the number of adjustment sensors. Private weather stations (PWS) are a promising source. We use rainfall observations from Netatmo devices in Germany from 2020 and 2025. Depending on the year, there are roughly 10–30 times more PWS reporting rainfall than the professional rain gauges operated by Deutscher Wetterdienst and partner networks in Germany.
In this contribution we (1) evaluate quality‑control (QC) methods to improve PWS data quality, (2) compare rainfall statistics of PWS observations against collocated reference rain gauges, and (3) quantify the impact of including PWS in the radar–gauge merging process. As a reference dataset we use operational DWD products. The main result we present, is an evaluation of radar products adjusted with PWS data, as well as products that use both PWS and rain gauges, against operational radar–gauge products. We demonstrate how adding a large number of PWS changes radar‑adjusted QPE and discuss the advantages and limitations of using them for real-time and climatological products.