Statistical characteristics of daily precipitation: Comparisons of gridded and point datasets
Statistical characteristics of daily precipitation: Comparisons of gridded and point datasets
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DOI：
10.1175/2008jamc1757.1
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发表时间：
2008-09-01
影响因子：
3
通讯作者：
Robeson, Scott M.
中科院分区：
文献类型：
--
作者：
Ensor, Leslie A.;Robeson, Scott M.
每日降水量数据的网格化消除了从点观测获得的数据的许多局限性，例如与数据缺失和缺乏空间覆盖相关的问题。因此，网格化的降水数据对于应用气候学研究和监测可能很有价值，但也有局限性。为了更全面地了解网格化数据的局限性（特别是当它们被用作台站数据的替代品时），计算并比较了来自气候预测中心统一雨量计数据集（URD）及其最近（雨量计）台站的5个中西部网格点的年降水总量、雨日频率和年最大值。为了进一步检验两个数据集之间的差异，计算了伊利诺伊州和印第安纳州的日降水重现期。这些分析表明，用于创建URD的网格化过程产生了与雨量计数据几乎相同的年度总量;然而，网格化显著增加了低降水事件的频率，同时大大降低了强降水事件的频率。在网格化降水数据中，极端降水值也大大减少。虽然平滑几乎总是发生在数据网格化时，但离散变量（如日降水量）的网格化可能会产生与原始观测数据统计特征截然不同的数据集。
Gridding of daily precipitation data alleviates many of the limitations of data that are derived from point observations, such as problems associated with missing data and the lack of spatial coverage. As a result, gridded precipitation data can be valuable for applied climatological research and monitoring, but they too have limitations. To understand the limitations of gridded data more fully (especially when they are used as surrogates for station data), annual precipitation total, rain-day frequency, and annual maxima are calculated and compared for five Midwestern grid points from the Climate Prediction Center's Unified Rain Gauge Dataset (URD) and those of its nearest (rain gauge) station. To further examine differences between the two datasets, return periods of daily precipitation were calculated over a region encompassing Illinois and Indiana. These analyses reveal that the gridding process used to create the URD produced nearly the same annual totals as the rain gauge data; however, the gridding significantly increased the frequency of low-precipitation events while greatly reducing the frequency of heavy-precipitation events. Extreme precipitation values also were greatly reduced in the gridded precipitation data. While smoothing nearly always occurs when data are gridded, the gridding of discrete variables such as daily precipitation can produce datasets with statistical characteristics that are very different from those of the original observations.