A Debiased Ranked Probability Skill Score to Evaluate Probabilistic Ensemble Forecasts with Small Ensemble Sizes
A Debiased Ranked Probability Skill Score to Evaluate Probabilistic Ensemble Forecasts with Small Ensemble Sizes
复制标题
用于评估小集合规模的概率集合预测的去偏概率技能分数
DOI：
10.1175/jcli3361.1
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发表时间：
2005
影响因子：
4.9
通讯作者：
M. Liniger
中科院分区：
文献类型：
--
作者：
W. Müller;C. Appenzeller;F. Doblas;M. Liniger
摘要等级概率技能得分（RPSS）是一种广泛使用的集合预报技能量化指标。基础分数由二次范数定义，与均方误差（mse）相当，但它适用于概率空间。它对预测概率分布的形状和偏移很敏感。然而，RPSS表现出负的偏见，合奏系统与小合奏大小，最近显示。在这里，两种策略探讨，以解决这个缺陷的RPSS。首先，RPSS检查不同的范数L（RPSSL）。它表明，RPSSL=1的基础上的绝对值，而不是预测和观察到的累积概率分布之间的平方差是无偏的，RPSSL定义与高阶规范显示负偏差。然而，RPSSL=1在统计意义上并不是严格正确的。第二种方法，然后调查，这是基于二次范数，但与气候概率的抽样误差conside…
Abstract The ranked probability skill score (RPSS) is a widely used measure to quantify the skill of ensemble forecasts. The underlying score is defined by the quadratic norm and is comparable to the mean squared error (mse) but it is applied in probability space. It is sensitive to the shape and the shift of the predicted probability distributions. However, the RPSS shows a negative bias for ensemble systems with small ensemble size, as recently shown. Here, two strategies are explored to tackle this flaw of the RPSS. First, the RPSS is examined for different norms L (RPSSL). It is shown that the RPSSL=1 based on the absolute rather than the squared difference between forecasted and observed cumulative probability distribution is unbiased; RPSSL defined with higher-order norms show a negative bias. However, the RPSSL=1 is not strictly proper in a statistical sense. A second approach is then investigated, which is based on the quadratic norm but with sampling errors in climatological probabilities conside...