高斯尺度混合集合卡尔曼滤波中的自适应协方差膨胀
Adaptive covariance inflation in the ensemble Kalman filter by Gaussian scale mixtures
摘要：
本文研究了乘性膨胀：集合卡尔曼滤波（EnKF）中状态协方差的互补标度。首先，在EnKF的误差源编目和讨论有关通货膨胀，非线性作为抽样误差的来源给予特别关注。作为回应，被称为EnKF-N的“有限大小”的改进通过高斯尺度混合重新推导，再次展示了它如何产生自适应通货膨胀。现有的自适应通货膨胀估计方法进行了审查，并从比较分析中获得了一些见解。选择一种这样的自适应膨胀方法来补充EnKF-N，以形成适合于存在模型误差并且不完全参数化的上下文的混合。基准是从实验中获得的两尺度洛伦兹模型及其慢尺度截断。所提出的混合EnKF-N方法的自适应通货膨胀，产生系统的精度改善与现有的方法相比，虽然到一个中等程度。
英文摘要：
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.
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