Comparison of Information-Based Measures of Forecast Uncertainty in Ensemble ENSO Prediction
Comparison of Information-Based Measures of Forecast Uncertainty in Ensemble ENSO Prediction
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DOI：
10.1175/2007jcli1719.1
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发表时间：
2008-01
影响因子：
4.9
通讯作者：
Youmin Tang;R. Kleeman;A. Moore
中科院分区：
文献类型：
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作者：
Youmin Tang;R. Kleeman;A. Moore
本文利用两个混合耦合模式对1981 - 1998年厄尔尼诺-南方涛动（ENSO）进行了整体预测。最近提出的几种基于信息的可预测性度量，包括相对熵(R)、预测信息（PI）、预测能力（PP）和互信息（MI），探讨了它们对ENSO集合预测的先验预测能力的估计能力。重点放在检查不使用观测的可预测性措施与使用观测的相关性和均方根误差（RMSE）的模型预测技能之间的关系。这里确定的关系提供了一种实用的方法来估计潜在的可预测性和个体预测的置信水平。研究发现，MI是一个很好的综合技能指标。当MI较大时，预测系统的预测能力较高，而较小的MI往往对应较低的预测能力。
Abstract In this study, ensemble predictions of the El Nino–Southern Oscillation (ENSO) were conducted for the period 1981–98 using two hybrid coupled models. Several recently proposed information-based measures of predictability, including relative entropy (R), predictive information (PI), predictive power (PP), and mutual information (MI), were explored in terms of their ability of estimating a priori the predictive skill of the ENSO ensemble predictions. The emphasis was put on examining the relationship between the measures of predictability that do not use observations, and the model prediction skills of correlation and root-mean-square error (RMSE) that make use of observations. The relationship identified here offers a practical means of estimating the potential predictability and the confidence level of an individual prediction. It was found that the MI is a good indicator of overall skill. When it is large, the prediction system has high prediction skill, whereas small MI often corresponds to a l...