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首页> 外文期刊>Nonlinear processes in geophysics >A local particle filter for high-dimensional geophysical systems
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A local particle filter for high-dimensional geophysical systems

机译：高维地球物理系统的局部粒子滤波器

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摘要

A local particle filter (LPF) is introduced that outperforms traditional ensemble Kalman filters in highly nonlinear/non-Gaussian scenarios, both in accuracy and computational cost. The standard sampling importance resampling (SIR) particle filter is augmented with an observation-space localization approach, for which an independent analysis is computed locally at each grid point. The deterministic resampling approach of Kitagawa is adapted for application locally and combined with interpolation of the analysis weights to smooth the transition between neighboring points. Gaussian noise is applied with magnitude equal to the local analysis spread to prevent particle degeneracy while maintaining the estimate of the growing dynamical instabilities. The approach is validated against the local ensemble transform Kalman filter (LETKF) using the 40-variable Lorenz-96 (L96) model. The results show that (1) the accuracy of LPF surpasses LETKF as the forecast length increases (thus increasing the degree of nonlinearity), (2) the cost of LPF is significantly lower than LETKF as the ensemble size increases, and (3) LPF prevents filter divergence experienced by LETKF in cases with non-Gaussian observation error distributions.
机译：介绍了局部粒子滤波器（LPF），以精度和计算成本，高度非线性/非高斯场景中的传统集合Kalman滤波器优于传统的集合Kalman滤波器。标准采样重要性重采样（SIR）粒子滤波器通过观察空间定位方法增强，在其本地在每个网格点本地计算独立分析。基塔川的确定性重采样方法适用于本地应用，并与分析权重的插值组合，以平滑相邻点之间的转变。高斯噪声的幅度等于局部分析，以防止粒子退化，同时保持对越来越多的动态稳定性的估计。使用40变量LORENZ-96（L96）模型，对本地集合变换卡尔曼滤波器（LetkF）进行验证。结果表明，（1）LPF的精度超过LetkF随着预测长度的增加（因此增加非线性程度），（2）LPF的成本显着低于Letkf，因为合奏尺寸增加，并且（3）LPF在非高斯观察误差分布的情况下，防止Letkf经验的过滤次数。

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