Regularization and tempering for a moment‐matching localized particle filter
Regularization and tempering for a moment‐matching localized particle filter
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暂时正则化和回火——匹配局部粒子过滤器
DOI：
10.1002/qj.4328
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发表时间：
2022
影响因子：
8.9
通讯作者：
Poterjoy, Jonathan
中科院分区：
文献类型：
--
作者：
Poterjoy, Jonathan
迭代集合滤波器和平滑器现在普遍用于地球物理模型。其中一些方法依赖于观察似然函数的因式分解，从后验密度通过一组“调质”过渡到集合成员。对于基于高斯的数据同化方法，非线性算子的切线性版本可以在迭代之间再线性化，从而导致比单步方法偏差更小的解决方案。本研究对局部粒子滤波器（PF）采用了类似的迭代策略，该算法依赖于矩的估计来调整基于重要性权重的未观测变量。这种方法建立在局部PF的“正则化”基础上，通过启发式方法强制权重更加统一。然后，正则化导致自适应回火，它也可以与参数方法（如集合卡尔曼滤波器）的滤波器更新相结合。通过推导当前局部PF公式假设的局部后验概率密度，然后检查单步和回火PF如何从该密度中采样，分析了迭代的作用。从对低维非线性系统进行的实验来看，迭代和混合策略在观测稀疏状态下显示出最大的优势，其中只有少数粒子包含高可能性并且先验误差是非高斯的。这种模式模拟了数值天气预报中的特定应用，其中较小的集合大小、未解决的模式误差和高度非线性动力学导致先验不确定性大于测量不确定性。
Iterative ensemble filters and smoothers are now commonly used for geophysical models. Some of these methods rely on a factorization of the observation likelihood function to sample from a posterior density through a set of “tempered” transitions to ensemble members. For Gaussian‐based data assimilation methods, tangent linear versions of nonlinear operators can be relinearized between iterations, thus leading to a solution that is less biased than a single‐step approach. This study adopts similar iterative strategies for a localized particle filter (PF) that relies on the estimation of moments to adjust unobserved variables based on importance weights. This approach builds off a “regularization” of the local PF, which forces weights to be more uniform through heuristic means. The regularization then leads to an adaptive tempering, which can also be combined with filter updates from parametric methods, such as ensemble Kalman filters. The role of iterations is analyzed by deriving the localized posterior probability density assumed by current local PF formulations and then examining how single‐step and tempered PFs sample from this density. From experiments performed with a low‐dimensional nonlinear system, the iterative and hybrid strategies show the largest benefits in observation‐sparse regimes, where only a few particles contain high likelihoods and prior errors are non‐Gaussian. This regime mimics specific applications in numerical weather prediction, where small ensemble sizes, unresolved model error, and highly nonlinear dynamics lead to prior uncertainty that is larger than measurement uncertainty.
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用于数据同化的滤波器和平滑器中的高斯近似
DOI：
10.1080/16000870.2019.1600344
发表时间：
2019
期刊：
Tellus A: Dynamic Meteorology and Oceanography
影响因子：
--
作者：
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通讯作者：
Hodyss, Daniel
影响因子：
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作者：
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通讯作者：
Robinson G
DOI：
10.1080/16000870.2017.1283809
发表时间：
2017-01-01
影响因子：
2
作者：
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通讯作者：
Snyder, Chris
影响因子：
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作者：
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通讯作者：
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