Precipitation is a central quantity of hydrometeorological research and applications. Especially in complex terrain, such as in High Mountain Asia (HMA), surface precipitation observations are scarce. Gridded precipitation products are one way to overcome the limitations of ground truth observations. They can provide datasets continuous in both space and time. However, there are many products available, which use various methods for data generation and lead to different precipitation values. In our study we compare nine different gridded precipitation products from different origins (ERA5, ERA5-Land, ERA-interim, HAR v2 10 km, HAR v2 2 km, JRA-55, MERRA-2, GPCC and PRETIP) over a subregion of the Central Himalaya and the Southwest Tibetan Plateau, from May to September 2017. Total spatially averaged precipitation over the study period ranged from 411 mm (GPCC) to 781 mm (ERA-Interim) with a mean value of 623 mm and a standard deviation of 132 mm. We found that the gridded products and the few observations, with few exceptions, are consistent among each other regarding precipitation variability and rough amount within the study area. It became obvious that higher grid resolution can resolve extreme precipitation much better, leading to overall lower mean precipitation spatially, but higher extreme precipitation events. We also found that generally high terrain complexity leads to larger differences in the amount of precipitation between products. Due to the considerable differences between products in space and time, we suggest carefully selecting the product used as input for any research application based on the type of application and specific research question. While coarse products such as ERA-Interim or ERA5 that cover long periods but have coarse grid resolution have previously shown to be able to capture long-term trends and help with identifying climate change features, this study suggests that more regional applications, such as glacier mass-balance modeling, require higher spatial resolution, as is reproduced, for example, in HAR v2 10 km.

中文翻译：

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喜马拉雅中部和青藏高原西南部一个分区网格降水数据的比对

降水是水文气象研究和应用的核心量。特别是在地形复杂的地区，例如亚洲高山（HMA），地表降水观测很少。网格降水产品是克服地面实况观测局限性的一种方法。它们可以提供在空间和时间上连续的数据集。但是，有许多可用的产品，它们使用各种方法生成数据并导致不同的降水值。在我们的研究中，我们比较了来自不同来源（ERA5、ERA5-Land、ERA-interim、HAR v2 10 km、HAR v2 2 km、JRA-55、MERRA-2、GPCC 和 PRETIP）在喜马拉雅中部和青藏高原西南部，2017 年 5 月至 9 月。研究期间的空间平均降水总量从 411 毫米（GPCC）到 781 毫米（ERA-Interim），平均值为 623 毫米，标准偏差为 132 毫米。我们发现网格化产品和少数观察结果（除少数例外）在研究区域内的降水变异性和粗略数量方面相互一致。很明显，更高的网格分辨率可以更好地解决极端降水，导致空间上总体平均降水量较低，但极端降水事件发生率较高。我们还发现，通常较高的地形复杂性导致产品之间的降水量差异较大。由于产品在空间和时间上的巨大差异，我们建议根据应用程序类型和特定研究问题仔细选择用作任何研究应用程序输入的产品。虽然 ERA-Interim 或 ERA5 等覆盖时间长但具有粗网格分辨率的粗产品先前已证明能够捕捉长期趋势并有助于识别气候变化特征，但本研究表明，更多区域应用，如冰川质量平衡建模需要更高的空间分辨率，例如在 HAR v2 10 km 中重现。