TY - DATA T1 - Dataset of pan-European 1-h OPERA radar precipitation accumulations adjusted with rain gauge accumulations from Netatmo personal weather stations PY - 2024/02/14 AU - A. (Aart) Overeem AU - H. (Hidde) Leijnse AU - Gerard van der Schrier AU - Else van den Besselaar AU - Irene Garcia-Marti AU - L.W. (Lotte) de Vos UR - DO - 10.4121/675f3f64-04a8-48db-ae3e-4a6c004a0776.v2 KW - precipitation KW - rain gauge KW - rain KW - quantitative precipitation estimation KW - radar KW - crowdsourcing KW - personal weather stations KW - merging N2 -

Ground-based weather radars provide precipitation estimates with wide coverage and high spatiotemporal resolution, but usually need adjustment with rain gauge data to obtain a reasonable accuracy. The (near) real-time availability and density of rain gauge networks operated by official institutes, especially national meteorological and hydrological services, is often relatively low. Crowdsourced rain gauge networks typically have a much higher density than networks from official institutes. Data from PWSs from brand Netatmo were obtained. Here, pan-European 1-h radar precipitation accumulations have been adjusted with 1-h rain gauge accumulations from personal weather stations (PWSs) for each clock-hour. The radar data were obtained from the Operational Program on the Exchange of weather RAdar information (OPERA) over the period 1 September 2019–31 August 31 2020. Two statistical methods and a satellite cloud type mask have been applied to the OPERA data to further remove non-meteorological echoes. Although not all these methods could be applied in (near) real-time, the OPERA dataset is representative of near (real-time) data, because these methods do only concern non-meteorological echo removal and not precipitation estimation itself. The Netatmo PWS data were subjected to quality control employing neighbouring PWSs and unadjusted radar data, before they were merged with the radar accumulations. A spatial adjustment (merging) method has been employed. The dataset covers 78% of geographical Europe. The dataset aims to show the potential of crowdsourced rain gauge data to improve radar data in (near) real-time.

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