Average Predictability Time. Part II: Seamless Diagnoses of Predictability on Multiple Time Scales
Average Predictability Time. Part II: Seamless Diagnoses of Predictability on Multiple Time Scales
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DOI：
10.1175/2008jas2869.1
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发表时间：
2009-05
影响因子：
3.1
通讯作者：
T. DelSole;M. Tippett
中科院分区：
文献类型：
--
作者：
T. DelSole;M. Tippett
摘要本文提出了一种无需时间平均的多时间尺度可预报性诊断新方法。该方法找到最大化系统的平均可预测性时间（APT）的组件，其中APT被定义为所有提前期的平均可预测性的积分。基于Mahalanobis度量的可预测性度量导致一个完整的，不相关的组件集，可以通过它们对APT的贡献进行排序，类似于主成分分解方差的方式。的组件和相关的APT是不变的非奇异线性变换，允许变量与不同的单位和自然变异被认为是在一个单一的状态向量，而无需归一化。对于从线性回归导出的预测模型，最大化APT等效于最大化分量与时滞状态向量之间的平方多个相关性之和。该方法用于诊断1000 hPa纬向速度的可预报性。
Abstract This paper proposes a new method for diagnosing predictability on multiple time scales without time averaging. The method finds components that maximize the average predictability time (APT) of a system, where APT is defined as the integral of the average predictability over all lead times. Basing the predictability measure on the Mahalanobis metric leads to a complete, uncorrelated set of components that can be ordered by their contribution to APT, analogous to the way principal components decompose variance. The components and associated APTs are invariant to nonsingular linear transformations, allowing variables with different units and natural variability to be considered in a single state vector without normalization. For prediction models derived from linear regression, maximizing APT is equivalent to maximizing the sum of squared multiple correlations between the component and the time-lagged state vector. The new method is used to diagnose predictability of 1000-hPa zonal velocity on time...