Polynomial Chaos Quantification of the Growth of Uncertainty Investigated with a Lorenz Model
Polynomial Chaos Quantification of the Growth of Uncertainty Investigated with a Lorenz Model
复制标题
用洛伦兹模型研究不确定性增长的多项式混沌量化
DOI：
--
复制
发表时间：
2010
期刊：
影响因子：
--
通讯作者：
S. Finette
中科院分区：
文献类型：
--
作者：
C. Shen;T. Evans;S. Finette
摘要初始条件只是近似已知的依赖时间的物理模型只能将演化的物理状态预测到一定的误差范围内。在天气预报以及海洋预报中，量化这种不确定性或可预测性至关重要，因为需要这种定量知识来限制预报的准确性。蒙特卡罗模拟是公认的确定不确定度的标准，但由于这些模型的高度自由度和计算要求，不适用于大气和海洋模型，特别是在业务环境中。相反，文献中开发的方法依赖于有限的模拟集合，这些模拟是从可能在预测时间增长最多的初始误差中挑选出来的。在本文中，作者提出了另一种量化不确定性增长的方法--多项式混沌方法。该方法试图表达首字母...
Abstract A time-dependent physical model whose initial condition is only approximately known can predict the evolving physical state to only within certain error bounds. In the prediction of weather, as well as its ocean counterpart, quantifying this uncertainty or the predictability is of critical importance because such quantitative knowledge is needed to provide limits on the forecast accuracy. Monte Carlo simulation, the accepted standard for uncertainty determination, is impractical to apply to the atmospheric and ocean models, particularly in an operational setting, because of these models’ high degrees of freedom and computational demands. Instead, methods developed in the literature have relied on a limited ensemble of simulations, selected from initial errors that are likely to have grown the most at the forecast time. In this paper, the authors present an alternative approach, the polynomial chaos method, to the quantification of the growth of uncertainty. The method seeks to express the initial...