Statistical Downscaling of Extreme Precipitation Events Using Censored Quantile Regression
Statistical Downscaling of Extreme Precipitation Events Using Censored Quantile Regression
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DOI：
10.1175/mwr3403.1
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发表时间：
2007-06
影响因子：
3.2
通讯作者：
P. Friederichs;A. Hense
中科院分区：
文献类型：
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作者：
P. Friederichs;A. Hense
摘要提出了一种基于删失分位数回归的极值统计降尺度方法。德国站数据的条件分位数(例如，日降水量)是通过NCEP再分析数据所代表的大尺度环流来估计的。结果表明，日降水量等离散-连续混合响应变量可以用删失变量进行统计建模。此外，还制定了条件分位数技能分数来评估分位数预测相对于参考预测的相对收益。就像期望值的多元回归一样，分位数回归提供了一种工具来建立极值分位数的模型产出统计系统。
Abstract A statistical downscaling approach for extremes using censored quantile regression is presented. Conditional quantiles of station data (e.g., daily precipitation sums) in Germany are estimated by means of the large-scale circulation as represented by the NCEP reanalysis data. It is shown that a mixed discrete–continuous response variable, such as a daily precipitation sum, can be statistically modeled by a censored variable. Furthermore, a conditional quantile skill score is formulated to assess the relative gain of a quantile forecast compared with a reference forecast. Just like multiple regression for expectation values, quantile regression provides a tool to formulate a model output statistics system for extremal quantiles.