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The European climatological high-resolution gauge-adjusted radar precipitation dataset, EURADCLIM, addresses the need for an accurate (sub-)daily precipitation product covering 78% of Europe at high spatial resolution. A climatological dataset of 1-h and 24-h precipitation accumulations on a 2-km grid is derived for the period 2013 through 2020. The starting point is the European Meteorological Network (EUMETNET) Operational Program on the Exchange of weather RAdar Information (OPERA) gridded radar dataset of 15-min instantaneous surface rain rates, which is based on data from, on average, 138 ground-based weather radars. First, methods are applied to further remove non-meteorological echoes from these composites by applying two statistical methods and a satellite-based cloud type mask. Second, the radar composites are merged with the European Climate Assessment & Dataset (ECA&D) with potentially ~7700 rain gauges from National Meteorological and Hydrological Services (NMHS) in order to substantially improve its quality. Characteristics of the radar, rain gauge and satellite datasets are presented, as well as a detailed account of the applied algorithms. The clutter removal algorithms are effective, while removing few precipitation echoes. The usefulness of EURADCLIM for quantitative precipitation estimation (QPE) is confirmed by comparing against rain gauge accumulations employing scatter density plots, statistical metrics, and a spatial verification. These show a strong improvement with respect to the original OPERA product. The potential of EURADCLIM to derive pan-European precipitation climatologies and to evaluate extreme precipitation events is shown. Specific attention is given to remaining artefacts in and limitations of EURADCLIM. Finally, it is recommended to further improve EURADCLIM by applying algorithms to 3D instead of 2D radar data, and by obtaining more rain gauge data for the radar-gauge merging. The EURADCLIM 1-h and 24-h precipitation datasets are publicly available at https://doi.org/10.21944/7ypj-wn68 (Overeem et al., 2022a) and https://doi.org/10.21944/1a54-gg96 (Overeem et al., 2022b). [ABSTRACT FROM AUTHOR]

Large parts of the continents are continuously scanned by terrestrial weather radars to monitor precipitation and wind conditions. These systems also monitor the mass movements of bird, bat, and insect migration, but it is still unknown how many of these systems perform with regard to detection and quantification of migration intensities of the different groups. In this study that was undertaken within five regions across Europe and the Middle East we examined to what extent bird migration intensities derived from different weather radars are comparable between each other and relate to intensities measured by local small‐scaled radars, some of them specifically developed to monitor birds. Good correspondence was found for the relative day‐to‐day pattern in migration intensities among most radar systems that were compared. Absolute intensities varied between different systems and regions. The findings of this study can be used to infer about absolute bird migration intensities measured by different radar systems and consequently help resolving methodological issues regarding the estimation of migrant numbers in the Western‐Palearctic region. It further depicts a scientific basis for the future monitoring of migratory bird populations across a large spatio‐temporal scale, predicting their movements and studying its consequences on ecological systems and human lives. [ABSTRACT FROM AUTHOR]

The Operational Program on the Exchange of Weather Radar Information (OPERA) has co-ordinated radar co-operation among national weather services in Europe for more than 20 years. It has introduced its own, manufacturer-independent data model, runs its own data center, and produces Pan-European radar composites. The applications using this data vary from data assimilation to flood warnings and the monitoring of animal migration. It has used several approaches to provide a homogeneous combination of disparate raw data and to indicate the reliability of its products. In particular, if a pixel shows no precipitation, it is important to know if that pixel is dry or if the measurement was missing. [ABSTRACT FROM AUTHOR]