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A Precipitation Climatology and Dataset Intercomparison for the Western United States

机译：美国西部的降水气候学和数据集比对

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This paper presents the results of a regionalization study of the precipitation climate of the western United States using principal component analysis. Past eigen-based regionalization studies have relied on rain gauge networks, which is restrictive because rain gauge coverage is sparse, especially over complex terrain that exists in the western United States. Here, the use of alternate data products is examined by conducting a comparative regionalization using nine precipitation datasets used in hydrometeorological research. Five unique precipitation climates are identified within the western United States, which have centers and boundaries that are physically reasonable and that highlight the relationship between the precipitation climatology and local topography. Using the congruence coefficient as the measure of similarity between principal component solutions, the method is found to be generally stable across datasets. The exception is the National Centers for Environmental Prediction -Department of Energy (NCEP-DOE) Reanalysis 2, which frequently demonstrates only borderline agreement with the other datasets. The loading pattern differences among datasets are shown to be primarily a result of data differences in the representation of (i) precipitation over the Rocky Mountains, (ii) the eastward wet-to-dry precipitation gradient that occurs during the cold season, (iii) the magnitude and spatial extent of the North American monsoon signal, and (iv) precipitation in the desert southwest during spring and summer. Sensitivity tests were conducted to determine whether the spatial resolution and temporal domain of the input data would dramatically affect the solution, and these results show the methodology to be stable to differences in spatial/temporal data features. The results suggest that alternate data products can be used in regionalization studies, which has applications for rain gauge installation and planning, climate research, and numerical modeling experiments.
机译：本文介绍了使用主成分分析对美国西部降水气候进行区域化研究的结果。过去基于特征的区域化研究都依赖于雨量计网络，这是有限制的，因为雨量计的覆盖范围很稀疏，尤其是在美国西部存在的复杂地形上。在这里，通过使用水文气象研究中使用的九种降水数据集进行比较区域划分，研究了替代数据产品的使用。美国西部地区确定了五种独特的降水气候，其中心和边界在物理上是合理的，并且突出了降水气候学与当地地形之间的关系。使用同余系数作为主成分解之间相似性的度量，发现该方法在整个数据集中通常是稳定的。国家环境预测中心-能源部（NCEP-DOE）重新分析2是个例外，该分析经常仅表明与其他数据集的边界一致。数据集之间的加载模式差异主要是由于（i）落基山脉上的降水，（ii）寒冷季节期间发生的向东的干湿降水梯度所代表的数据差异所致。 ）北美季风信号的强度和空间范围，以及（iv）春季和夏季西南西南沙漠地区的降水。进行了敏感性测试，以确定输入数据的空间分辨率和时域是否会极大地影响解决方案，这些结果表明该方法对于时空数据特征的差异是稳定的。结果表明，替代数据产品可用于区域化研究，该研究可用于雨量计的安装和规划，气候研究和数值模拟实验。

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