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A rescued dataset of sub-daily meteorological observations for Europe and the southern Mediterranean region, 1877–2012

机译:欧洲和地中海南部地区次日气象观测资料的获救数据集,1877-2012年

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Sub-daily meteorological observations are needed for input to and assessment of high-resolution reanalysis products to improve understanding of weather and climate variability. While there are millions of such weather observations that have been collected by various organisations, many are yet to be transcribed into a useable format. Under the auspices of the Uncertainties in Ensembles of Regional ReAnalyses (UERRA) project, we describe the compilation and development of a digital dataset of 8.8?million meteorological observations of essential climate variables (ECVs) rescued across the European and southern Mediterranean region. By presenting the entire chain of data preparation, from the identification of regions lacking in digitised sub-daily data and the location of original sources, through the digitisation of the observations to the quality control procedures applied, we provide a rescued dataset that is as traceable as possible for use by the research community. Data from 127 stations and of 15 climate variables in the northern African and European sectors have been prepared for the period 1877 to 2012. Quality control of the data using a two-step semi-automatic statistical approach identified 3.5 % of observations that required correction or removal, on par with previous data rescue efforts. In addition to providing a new sub-daily meteorological dataset for the research community, our experience in the development of this sub-daily dataset gives us an opportunity to share some suggestions for future data rescue projects.
机译:需要每天进行次气象观测,以输入和评估高分辨率的再分析产品,以增进对天气和气候多变性的了解。尽管各个组织已经收集了数百万种这样的天气观测资料,但许多观测资料尚未被转录成可用的格式。在区域再分析总体不确定性(UERRA)项目的主持下,我们描述了在欧洲和地中海南部地区救出的880万气象基本气候变量(ECV)气象观测数字数据集的编制和开发。通过呈现整个数据准备链,从识别缺少数字化次日数据的区域和原始来源的位置,到将观测值数字化到所应用的质量控制程序,我们提供了一个可追溯的救援数据集尽可能供研究团体使用。已准备了1877年至2012年期间来自北非和欧洲地区127个站点的数据以及15个气候变量。使用两步半自动统计方法对数据进行质量控制,发现需要校正或校正的观测值占3.5%。与以前的数据救援工作相当。除了为研究社区提供新的次日气象数据集外,我们在开发此次日数据集方面的经验使我们有机会分享一些有关未来数据救援项目的建议。

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