A cross‐checked global monthly weather station database for precipitation covering the period 1901–2010,Geoscience Data Journal

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A cross‐checked global monthly weather station database for precipitation covering the period 1901–2010
Geoscience Data Journal ( IF 3.3 ) Pub Date : 2020-01-26 , DOI: 10.1002/gdj3.88
Dante Castellanos‐Acuna ₁ , Andreas Hamann ₁

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Comprehensive monthly weather station databases are the foundation for many gridded climate data products, and they are widely used to characterize regional climate conditions, track climate change and research the impact of climate on natural and managed ecosystems. However, weather station databases are often regional in coverage, and they can have extensive gaps in station coverage over time. They may also contain errors in climate records, station coordinates or elevation. Here, we assemble a comprehensive monthly weather station database for precipitation from multiple reputable data sources. We use digital elevation models and nearby stations to search for inconsistencies in reported station locations and recorded precipitation values. We also estimated missing values in weather station time series using a linear model approach based on interpolated anomaly surfaces. The resulting station records were ranked into ten classes, according to the completeness of records, the reliability of missing value estimations and other criteria. We corrected incomplete or erroneous location and elevation information for 12% of all available station records. A total of 23% of monthly records that had missing values could be estimated with high or moderate confidence. We sub‐sampled our global database of more than 80,000 stations with various spatial filters, so that only the highest quality station for a given area was retained. Our contribution significantly enhances global data coverage compared to individual databases currently available. Even when accepting only the stations within the top two quality ranks in our combined database, and applying the coarsest spatial filter of one station per approximately 1,600 km², the remaining station count of more than 20,000 stations exceeds the largest alternative database (without a spatial filter applied) by more than 50%.

中文翻译：

一项经过相互核对的全球每月气象站数据库，涵盖了1901–2010年的降水量

全面的每月气象站数据库是许多栅格化气候数据产品的基础，它们被广泛用于表征区域气候条件，跟踪气候变化以及研究气候对自然和可管理生态系统的影响。但是，气象站数据库的覆盖范围通常是区域性的，随着时间的推移，它们在覆盖范围方面可能存在很大的差距。它们还可能包含气候记录，站位坐标或高程中的错误。在这里，我们建立了一个全面的每月气象站数据库，以收集来自多个知名数据源的降水。我们使用数字高程模型和附近的站点来搜索报告的站点位置和记录的降水值之间的不一致。我们还使用基于插值异常面的线性模型方法来估计气象站时间序列中的缺失值。根据记录的完整性，缺失值估计的可靠性和其他标准，将所得的站点记录分为十类。我们已对所有可用台站记录中的12％纠正了不完整或错误的位置和海拔信息。具有缺失值的月度记录总数中有23％可以以高或中等置信度进行估计。我们使用各种空间过滤器对全球80,000多个站点进行了二次抽样，从而仅保留了给定区域中最高质量的站点。与当前可用的单个数据库相比，我们的贡献大大提高了全球数据覆盖率。^{如图2所示}，超过20,000个站点的剩余站点数比最大的替代数据库（未应用空间过滤器）超出50％以上。

更新日期：2020-01-26

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