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.

地面气象雷达可提供覆盖范围广、时空分辨率高的降水估算结果，但通常需结合雨量计数据进行校准，方可获得合理的估算精度。官方机构（尤其是国家气象水文部门）运营的雨量站网络，其近实时可用性与站点密度往往相对较低。众包雨量站网络的站点密度通常远高于官方机构的网络。本研究获取了Netatmo品牌的个人气象站（Personal Weather Station, PWS）数据。针对2019年9月1日至2020年8月31日期间，从天气雷达信息交换业务计划（Operational Program on the Exchange of weather RAdar information, OPERA）获取的泛欧洲逐小时雷达降水累积量，本研究采用逐小时的个人气象站（PWS）逐小时雨量累积数据对其进行逐小时校准。研究团队对OPERA雷达数据应用了两种统计方法与卫星云型掩码，以进一步去除非气象回波。尽管部分方法无法在近实时场景下实施，但OPERA数据集仍可代表近实时数据，因为这些方法仅针对非气象回波去除，而非降水估算核心流程本身。Netatmo品牌的PWS数据在与雷达降水累积数据融合前，已通过相邻PWS观测数据与未校准雷达数据完成质量控制。随后本研究采用空间校准（融合）方法完成数据整合。该数据集覆盖了欧洲78%的地理区域。本数据集旨在展示众包雨量站数据在近实时场景下改善雷达降水数据的应用潜力。