非线性、非高斯联合状态参数资料同化的基于代理的方法
A surrogate-based approach to nonlinear, non-Gaussian joint state-parameter data assimilation
摘要：
最近在非线性高维模型的数据序列同化方面的许多进展是对粒子滤波器的修改，粒子滤波器采用高维状态空间的有效搜索。在这项工作中，我们提出了一个互补的策略，结合统计仿真器和粒子滤波器。仿真器用于学习并提供计算上便宜的近似正向动态映射。这种仿真器粒子滤波器（Emu-PF）方法需要适度数量的前向模型运行，但即使在非高斯情况下也能产生良好的后验分布。我们探讨了几个修改的Emu-PF，利用降维机制，以有效地适应统计仿真器，并提出了一系列的模拟实验上的一个非典型的Lorenz-96系统，以证明其性能。最后，我们讨论如何Emu-PF可以与现代粒子滤波算法配对。
英文摘要：
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.
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