Comparison of Ensemble Kalman Filters under Non-Gaussianity
Comparison of Ensemble Kalman Filters under Non-Gaussianity
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DOI：
10.1175/2009mwr3133.1
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发表时间：
2010-04-01
影响因子：
3.2
通讯作者：
Snyder, Chris
中科院分区：
文献类型：
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作者：
Lei, Jing;Bickel, Peter;Snyder, Chris
近年来，各种版本的集成卡尔曼滤波器（EnKFs）被提出和研究。这项工作关注的是，在数学上严格的方式，当预测集合是非高斯时，两个主要版本的EnKF的相对性能。该方法基于过滤方法对小模型违规的稳定性，使用期望的平方L(2)距离作为更新分布之间偏差的度量。分析和实验结果表明，随机和确定性的EnKFs对高斯假设的违反都很敏感，而随机滤波器在某些情况下，特别是在存在野异常值的情况下，比确定性滤波器相对更稳定。这些结果不仅与以往的实证研究相一致，而且表明了平方根卡尔曼滤波算法中自由参数的自然选择。
Recently various versions of ensemble Kalman filters (EnKFs) have been proposed and studied. This work concerns, in a mathematically rigorous manner, the relative performance of two major versions of EnKF when the forecast ensemble is non-Gaussian. The approach is based on the stability of the filtering methods against small model violations, using the expected squared L(2) distance as a measure of the deviation between the updated distributions. Analytical and experimental results suggest that both stochastic and deterministic EnKFs are sensitive to the violation of the Gaussian assumption, while the stochastic filter is relatively more stable than the deterministic filter under certain circumstances, especially when there are wild outliers. These results not only agree with previous empirical studies, but also suggest a natural choice of a free parameter in the square root Kalman filter algorithm.