Speakers
Description
Small-scale convective storms are difficult to capture with conventional rain‑gauge networks because of limited spatial coverage. Weather radar provides spatially continuous observations but measures aloft with an intrinsic time lag relative to surface observations and carries uncertainties from the indirect measurement principle. For near‑real‑time rainfall analyses and climatological studies, precise spatio‑temporal localization of convective events is therefore crucial. These storms often occur in association with cold pools.
Cold pools are mesoscale (~30 km) air masses generated by evaporative cooling beneath thunderstorm clouds; they are typically marked by a local pressure rise and a temperature drop. In Germany during 2025, there are roughly 90,000 Netatmo private weather stations (PWS) reporting air pressure and potentially air temperature, with around 35,000 of them also measuring precipitation. Although PWS data do not meet professional‑network standards, their sheer spatial density offers an opportunity to detect cold pools at the surface and to complement radar observations.
This study aims to (1) detect characteristic cold‑pool signatures (local pressure increase and temperature decrease) in Netatmo PWS data, (2) validate these detections against radar datasets (Radklim, CatRaRE) and KONRAD3D thunderstorm tracks, and (3) discuss the potential of PWS‑based cold‑pool detection to support subsequent hydrometeorological applications. We apply automated quality control (QC) to PWS measurements (e.g., using the open‑source tool pypwsqc) before detection and validation, thus evaluating the performance of QC methods developed for PWS rainfall observation on other variables.