Preventing Catastrophic Filter Divergence Using Adaptive Additive Inflation for Baroclinic Turbulence
Preventing Catastrophic Filter Divergence Using Adaptive Additive Inflation for Baroclinic Turbulence
复制标题
使用斜压湍流的自适应累加膨胀来防止灾难性的滤波器发散
DOI：
--
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
D. Qi
中科院分区：
文献类型：
--
作者：
Yoonsang Lee;A. Majda;D. Qi
摘要基于系综的滤波或数据同化方法已被证明是大气和海洋科学中不可或缺的工具，因为它们允许计算成本低，低维系综状态近似极高维湍流动力系统。对于稀疏、精确和不频繁的观测，这在地球物理系统的数据同化中是典型的，集合滤波方法可能遭受灾难性的滤波器发散，这经常驱使滤波器预测机器无穷大。一个两层准地转方程，这是一个经典的理想化地球物理湍流模式，被用来证明突变过滤发散。研究了Tong等人提出的自适应协方差膨胀和协方差局部化的数学理论，以稳定集成方法并防止灾难性的滤波发散。两种预报模式-粗粒度的海洋代码，忽略了小尺度参数化，和随机超参数化（...
AbstractEnsemble-based filtering or data assimilation methods have proved to be indispensable tools in atmosphere and ocean science as they allow computationally cheap, low-dimensional ensemble state approximation for extremely high-dimensional turbulent dynamical systems. For sparse, accurate, and infrequent observations, which are typical in data assimilation of geophysical systems, ensemble filtering methods can suffer from catastrophic filter divergence, which frequently drives the filter predictions to machine infinity. A two-layer quasigeostrophic equation, which is a classical idealized model for geophysical turbulence, is used to demonstrate catastrophic filter divergence. The mathematical theory of adaptive covariance inflation by Tong et al. and covariance localization are investigated to stabilize the ensemble methods and prevent catastrophic filter divergence. Two forecast models—a coarse-grained ocean code, which ignores the small-scale parameterization, and stochastic superparameterization (...