资料同化中的自洽随机模型误差
Self-Consistent Stochastic Model Errors in Data Assimilation
摘要：
在使用数据同化从观测中导入信息以估计模型的参数和状态变量时，必须假设测量值和模型误差中的噪声的分布。利用资料同化的路径积分公式，引入随机模式误差分布自洽的思想：由观测资料得到的路径积分的模式误差分布应与公式化路径积分时所作的假设一致。讨论了资料同化的路径积分设置，为一致性检验提供设置。使用两个例子从1996年的洛伦兹模型，为$D = 100$和$D = 20$，我们展示了如何可以测试这种不一致的基本没有额外的努力比，在提取答案，有趣的问题，从数据同化本身。\end{abstract}
英文摘要：
In using data assimilation to import information from observations to estimate parameters and state variables of a model, one must assume a distribution for the noise in the measurements and in the model errors. Using the path integral formulation of data assimilation~ cite{abar2009}, we introduce the idea of self consistency of the distribution of stochastic model errors: the distribution of model errors from the path integral with observed data should be consistent with the assumption made in formulating the the path integral. The path integral setting for data assimilation is discussed to provide the setting for the consistency test. Using two examples drawn from the 1996 Lorenz model, for $D = 100$ and for $D = 20$ we show how one can test for this inconsistency with essential no additional effort than that expended in extracting answers to interesting questions from data assimilation itself. \end{abstract}
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