Dataset of pan-European 1-h OPERA radar precipitation accumulations adjusted with rain gauge accumulations from Netatmo personal weather stations

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.地基天气雷达（ground-based weather radars）可提供覆盖范围广、时空分辨率优异的降水估算产品，但通常需结合雨量计（rain gauge）数据进行校准，方可获得合理的精度水平。官方机构（尤其是国家气象与水文服务部门）运营的雨量计网络，其（近）实时可用性与布设密度往往相对较低。众包式雨量计网络的布设密度通常远高于官方机构的同类网络。本研究获取了Netatmo品牌旗下个人气象站（Personal Weather Stations, PWS）的观测数据。针对每个整点时刻，本数据集采用个人气象站的逐小时雨量累计数据，对泛欧洲范围的逐小时雷达降水累计产品开展校准。本次研究的雷达数据来源于天气雷达信息交换运营计划（Operational Program on the Exchange of weather RAdar information, OPERA），采集时段为2019年9月1日至2020年8月31日。研究针对OPERA数据集采用了两种统计方法与卫星云型掩膜（satellite cloud type mask）技术，以进一步剔除非气象回波。尽管并非所有上述方法均可实现（近）实时应用，但OPERA数据集仍具备（近）实时数据的代表性，因为这些方法仅针对非气象回波剔除，而非降水估算流程本身。在与雷达降水累计数据融合前，研究结合邻近个人气象站数据与未校准雷达数据，对Netatmo个人气象站数据开展了质量控制。本次研究采用了空间校准（融合）方法。该数据集覆盖了欧洲大陆78%的区域。本数据集旨在验证众包雨量计数据在（近）实时场景下提升雷达降水数据精度的应用潜力。