论文标题
非线性，非高斯联合国家参数数据同化的基于替代物的方法
A surrogate-based approach to nonlinear, non-Gaussian joint state-parameter data assimilation
论文作者
论文摘要
将数据顺序同化为非线性高维模型的许多最新进展是对粒子过滤器的修改，这些粒子过滤器采用了高维状态空间的有效搜索。在这项工作中，我们提出了一种结合统计模拟器和粒子过滤器的互补策略。模拟器用于学习和提供对正向动态映射的计算廉价近似值。这种模拟器粒子滤波器（EMU-PF）方法需要适度的前向模型运行，但即使在非高斯病例中，也可以产生精确的后验分布。我们探索了对EMU-PF的几种修改，该修改利用机制降低了尺寸以有效拟合统计模拟器，并在非典型的Lorenz-96系统上提供了一系列的仿真实验来证明其性能。最后，我们讨论了如何将EMU-PF与现代粒子过滤算法配对。
Many recent advances in sequential assimilation of data into nonlinear high-dimensional models are modifications to particle filters which employ efficient searches of a high-dimensional state space. In this work, we present a complementary strategy that combines statistical emulators and particle filters. The emulators are used to learn and offer a computationally cheap approximation to the forward dynamic mapping. This emulator-particle filter (Emu-PF) approach requires a modest number of forward-model runs, but yields well-resolved posterior distributions even in non-Gaussian cases. We explore several modifications to the Emu-PF that utilize mechanisms for dimension reduction to efficiently fit the statistical emulator, and present a series of simulation experiments on an atypical Lorenz-96 system to demonstrate their performance. We conclude with a discussion on how the Emu-PF can be paired with modern particle filtering algorithms.
