集合卡尔曼滤波器
卡尔曼滤波器
膨胀(宇宙学)
协方差
计量经济学
参数化复杂度
高斯分布
计算机科学
数学
算法
统计
扩展卡尔曼滤波器
理论物理学
量子力学
物理
作者
Patrick N. Raanes,Marc Bocquet,Alberto Carrassi
摘要
This paper studies multiplicative inflation: the complementary scaling of the state covariance in the ensemble Kalman filter (EnKF). Firstly, error sources in the EnKF are catalogued and discussed in relation to inflation; nonlinearity is given particular attention as a source of sampling error. In response, the "finite-size" refinement known as the EnKF-N is re-derived via a Gaussian scale mixture, again demonstrating how it yields adaptive inflation. Existing methods for adaptive inflation estimation are reviewed, and several insights are gained from a comparative analysis. One such adaptive inflation method is selected to complement the EnKF-N to make a hybrid that is suitable for contexts where model error is present and imperfectly parameterized. Benchmarks are obtained from experiments with the two-scale Lorenz model and its slow-scale truncation. The proposed hybrid EnKF-N method of adaptive inflation is found to yield systematic accuracy improvements in comparison with the existing methods, albeit to a moderate degree.
科研通智能强力驱动
Strongly Powered by AbleSci AI