Bivariate Downscaling With Asynchronous Measurements
Bivariate Downscaling With Asynchronous Measurements
复制标题
通过异步测量进行双变量降尺度
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
Jingfei Zhang
中科院分区：
文献类型：
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作者：
Xuming He;Yunwen Yang;Jingfei Zhang
统计缩小尺度是将全球或区域气候模型预测本地化以评估气候变化潜在影响的一项有用技术。它需要量化气候模型输出和过去当地观测之间的关系，但这两组测量不一定是同时进行的，因此通常的回归技术不适用。在单变量向下尺度的情况下，O‘Brien，Sornette和McPherron的统计异步回归方法(地球物理研究杂志，106,13247-13259,2001年)提供了一种简单的分位数匹配方法和异步测量。在这篇文章中，我们提出了一种基于双变量等级和位置概念的异步测量的双变量降尺度方法。该方法优于单变量降尺度，因为它能够在统计降尺度中保留温度和降水等两个变量之间的一般关联形式。通过对模拟数据和真实数据的应用，证明了双变量降尺度方法的这一理想性质。
Statistical downscaling is a useful technique to localize global or regional climate model projections to assess the potential impact of climate changes. It requires quantifying a relationship between climate model output and local observations from the past, but the two sets of measurements are not necessarily taken simultaneously, so the usual regression techniques are not applicable. In the case of univariate downscaling, the Statistical Asynchronous Regression (SAR) method of O’Brien, Sornette, and McPherron (Journal of Geophysical Research, 106, 13247–13259, 2001) provides a simple quantile-matching approach with asynchronous measurements. In this paper, we propose a bivariate downscaling method for asynchronous measurements based on a notion of bivariate ranks and positions. The proposed method is preferable to univariate downscaling, because it is able to preserve general forms of association between two variables, such as temperature and precipitation, in statistical downscaling. This desirable property of the bivariate downscaling method is demonstrated through applications to simulated and real data.