In this paper, we present a comprehensive review of the data sources and estimation methods of 30 currently available global precipitation data sets, including gauge-based, satellite-related, and reanalysis data sets. We analyzed the discrepancies between the data sets from daily to annual timescales and found large differences in both the magnitude and the variability of precipitation estimates. The magnitude of annual precipitation estimates over global land deviated by as much as 300 mm/yr among the products. Reanalysis data sets had a larger degree of variability than the other types of data sets. The degree of variability in precipitation estimates also varied by region. Large differences in annual and seasonal estimates were found in tropical oceans, complex mountain areas, northern Africa, and some high-latitude regions. Overall, the variability associated with extreme precipitation estimates was slightly greater at lower latitudes than at higher latitudes. The reliability of precipitation data sets is mainly limited by the number and spatial coverage of surface stations, the satellite algorithms, and the data assimilation models. The inconsistencies described limit the capability of the products for climate monitoring, attribution, and model validation.

中文翻译：

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全球降水数据集回顾：数据来源、估计和比对

在本文中，我们全面回顾了 30 个当前可用的全球降水数据集的数据源和估算方法，包括基于仪表、卫星相关和再分析数据集。我们分析了从日到年时间尺度的数据集之间的差异，发现降水估计的量级和可变性存在很大差异。产品中全球陆地年降水量估计值偏差高达 300 毫米/年。再分析数据集比其他类型的数据集具有更大程度的可变性。降水估计的变异程度也因地区而异。在热带海洋、复杂的山区、北非和一些高纬度地区发现年度和季节性估计值存在很大差异。全面的，与极端降水估计相关的可变性在低纬度地区略大于高纬度地区。降水数据集的可靠性主要受地面站的数量和空间覆盖、卫星算法和数据同化模型的限制。所描述的不一致限制了产品用于气候监测、归因和模型验证的能力。