Pseudo-Orbit Data Assimilation. Part I: The Perfect Model Scenario
Pseudo-Orbit Data Assimilation. Part I: The Perfect Model Scenario
复制标题
伪轨道数据同化。
DOI：
10.1175/jas-d-13-032.1
复制
发表时间：
2014
影响因子：
3.1
通讯作者：
Leonard A. Smith
中科院分区：
文献类型：
--
作者：
H. Du;Leonard A. Smith
状态估计是许多气象任务的核心。基于伪轨道的数据同化为天气预报模型等非线性系统的数据同化提供了一种有吸引力的替代方法。在完美的模型场景中，噪声观测妨碍了对当前状态的精确估计。在这种情况下，集合卡尔曼滤波方法受到其动态线性的基本假设的阻碍，而变分方法在实践中可能由于其成本函数的局部最小而失败。伪轨道数据同化方法通过增强动态方程信息与观测信息之间的平衡来改善状态估计。在两个确定性混沌系统：二维池田图和18维Lorenz96流的完美模型场景中，探索了这种方法在数值天气预报中的潜在用途。经验结果表明，与两种最常见的传统数据同化方法（集成卡尔曼滤波和四维变分同化）相比，该方法的性能有所提高。
State estimation lies at the heart of many meteorological tasks. Pseudo-orbit-based data assimilation provides an attractive alternative approach to data assimilation in nonlinear systems such as weather forecasting models. In the perfect model scenario, noisy observations prevent a precise estimate of the current state. In this setting, ensemble Kalman filter approaches are hampered by their foundational assumptions of dynamical linearity, while variational approaches may fail in practice owing to local minima in their cost function. The pseudo-orbit data assimilation approach improves state estimation by enhancing the balance between the information derived from the dynamic equations and that derived from the observations. The potential use of this approach for numerical weather prediction is explored in the perfect model scenario within two deterministic chaotic systems: the two-dimensional Ikeda map and 18-dimensional Lorenz96 flow. Empirical results demonstrate improved performance over that of the two most common traditional approaches of data assimilation (ensemble Kalman filter and four-dimensional variational assimilation).
影响因子：
3.1
作者：
Smith L
通讯作者：
Smith L