Ground observation of precipitation over complex terrain is subject to large uncertainties due to inadequate sampling. This study explores a method that combines limited gauge data and a high-resolution numerical simulation to quantify the precipitation uncertainties in central Himalaya. Specifically, the Coefficient of spatial Variability (CV) of precipitation and the minimum Number of Required Stations (NRS) to obtain areal-mean precipitation ground truth values within a 0.25° area are investigated using fine-scale meteorological simulation at 1.5 km grid spacing. Evaluation over a densely-gauged area demonstrates comparable CV and NRS values between station observations and simulations. The simulated CV and NRS values in a larger area show a strong and positive dependence on each other and an expected positive (negative) correlation with topographic complexity (temporal scale). The proposed method sheds lights on evaluating precipitation products and holds promise for informing the layout of rain gauge networks in complex terrain.

中文翻译：

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通过高分辨率数值模拟表征复杂地形中地面“真相”的不确定性

由于采样不足，在地面上对降水进行地面观测存在很大的不确定性。这项研究探索了一种将有限的轨距数据和高分辨率数值模拟相结合的方法，以量化喜马拉雅山中部的降水不确定性。具体来说，使用1.5 km网格间距的细尺度气象模拟研究了降水的空间变异性（CV）系数和获得0.25°区域内平均降水地面真实值所需的最小台数（NRS）。在一个人口稠密的地区进行的评估表明，观测站和模拟站之间的CV和NRS值相当。在较大区域中模拟的CV和NRS值显示出彼此的强正相关性，以及与地形复杂性（时间尺度）的预期正（负）相关性。所提出的方法为评估降水产物提供了依据，并有望为复杂地形中雨量计网络的布局提供信息。