信息传播和定位在集合卡尔曼滤波中的应用
On the propagation of information and the use of localization in ensemble Kalman filtering
摘要：
已经提出了几个本地化版本的集合卡尔曼滤波器。虽然应用这些方案的测试证明它们非常有前途，但目前缺乏对本地化的基本原理和局限性的全面基本理解。这是本文的目标之一，有助于解决这个问题。第二个目标是阐明混沌波动力学在信息传播中所起的作用以及对预报的影响。为了实现这些目标，这里使用的主要工具将是分析和解释的玩具大气模型在2005年介绍的数值实验。该模型的波包的传播。结果发现，当采用集合卡尔曼滤波方案时，在每个预报周期对背景集合成员进行平均得到的空间关联函数是短程的，这与对模式自由演化状态进行平均得到的长程关联函数形成强烈对比。研究了在一个地区观测对其他地区预报影响的传播。比较了有定位和无定位分析的误差协方差矩阵。从这项研究中，本地化过程和信息传播的主要特点进行了提取和总结。
英文摘要：
Several localized versions of the ensemble Kalman filter have been proposed. Although tests applying such schemes have proven them to be extremely promising, a full basic understanding of the rationale and limitations of localization is currently lacking. It is one of the goals of this paper to contribute toward addressing this issue. The second goal is to elucidate the role played by chaotic wave dynamics in the propagation of information and the resulting impact on forecasts. To accomplish these goals, the principal tool used here will be analysis and interpretation of numerical experiments on a toy atmospheric model introduced by Lorenz in 2005. Propagation of the wave packets of this model is shown. It is found that, when an ensemble Kalman filter scheme is employed, the spatial correlation function obtained at each forecast cycle by averaging over the background ensemble members is short ranged, and this is in strong contrast to the much longer range correlation function obtained by averaging over states from free evolution of the model. Propagation of the effects of observations made in one region on forecasts in other regions is studied. The error covariance matrices from the analyses with localization and without localization are compared. From this study, major characteristics of the localization process and information propagation are extracted and summarized.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
