A Lagrangian trajectory filter for constituent data assimilation
A Lagrangian trajectory filter for constituent data assimilation
复制标题
用于成分数据同化的拉格朗日轨迹滤波器
DOI：
--
复制
发表时间：
2004
期刊：
影响因子：
--
通讯作者：
R. Renka
中科院分区：
文献类型：
--
作者：
P. Lyster;S. Cohn;Banglin Zhang;Lang‐Ping Chang;R. Ménard;K. Olson;R. Renka
我们发展了一种新的数值算法，拉格朗日滤波，用于求解卡尔曼滤波方程，用于直接同化随气流传播的轨迹上的分量观测。这是状态估计问题的特征解的有限维近似，可以被认为是轨迹映射等方法的扩展。由于状态及其估计的保守性--沿轨道的误差方差和协方差，拉格朗日滤波为研究和解决成分数据同化问题提供了一个自然的框架。由于这些性质，对过滤器的行为获得了相当大的洞察力。由于其简单的误差协方差传播步骤，拉格朗日滤波所需的浮点运算也明显少于欧拉卡尔曼滤波。我们对平流层等熵线上的二维流动和高层大气研究卫星的甲烷同化观测进行了实施。结果与欧拉滤波的结果进行了对比验证。版权所有©2004英国皇家气象学会
We have developed a new numerical algorithm, the Lagrangian filter, for solving the Kalman filter equations for assimilation of constituent observations directly on trajectories that propagate with the flow. This is a finite‐dimensional approximation of the solution of the state estimation problem by characteristics, and may be thought of as an extension of methods such as trajectory mapping. The Lagrangian filter provides a natural framework for the study and solution of the constituent data assimilation problem because of the conservative properties of the state and its estimation‐error variance and covariance along trajectories. Considerable insight into the behaviour of the filter is gained as a result of these properties. The Lagrangian filter also requires significantly fewer floating point operations than the Eulerian Kalman filter because of its simple error covariance propagation step. We implemented it for two‐dimensional flow on isentropes in the stratosphere and assimilated methane observations from the Upper Atmosphere Research Satellite. Results are validated against those of the Eulerian filter. Copyright © 2004 Royal Meteorological Society