SEQUENTIAL DATA ASSIMILATION WITH A NONLINEAR QUASI-GEOSTROPHIC MODEL USING MONTE-CARLO METHODS TO FORECAST ERROR STATISTICS
SEQUENTIAL DATA ASSIMILATION WITH A NONLINEAR QUASI-GEOSTROPHIC MODEL USING MONTE-CARLO METHODS TO FORECAST ERROR STATISTICS
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DOI：
10.1029/94jc00572
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发表时间：
1994-05-15
影响因子：
3.6
通讯作者：
EVENSEN, G
中科院分区：
文献类型：
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作者：
EVENSEN, G
讨论了一种新的序列资料同化方法。它是基于预测的误差统计使用蒙特卡洛方法，一个更好的替代解决传统的和计算要求极高的近似误差协方差方程中使用的扩展卡尔曼滤波器。在扩展卡尔曼滤波器中发现的无界误差增长，这是由误差协方差方程中的过度简化闭合引起的，被完全消除。只要海洋模型的姿态良好，就可以处理开放边界。由于不需要存储和演化误差协方差矩阵本身，因此避免了与误差协方差方程相关联的众所周知的数值不稳定性。结果也比由扩展卡尔曼滤波器提供的结果更好，因为不存在闭合问题，因此提高了预测误差统计的质量。该方法对于更复杂的原始方程模型也是可行的。合理的精度的计算负荷只是扩展卡尔曼滤波器所需的一小部分，并且由存储给出，比如说，100个模型状态的集合大小为100，因此CPU要求的顺序的成本为100个模型积分。因此，所提出的方法可以用于现实的非线性海洋模型在现有的计算机上的大域，它也非常适合于并行计算机和集群的工作站，每个处理器集成了几个成员的合奏。
A new sequential data assimilation method is discussed. It is based on forecasting the error statistics using Monte Carlo methods, a better alternative than solving the traditional and computationally extremely demanding approximate error covariance equation used in the extended Kalman filter. The unbounded error growth found in the extended Kalman filter, which is caused by an overly simplified closure in the error covariance equation, is completely eliminated. Open boundaries can be handled as long as the ocean model is well posed. Well-known numerical instabilities associated with the error covariance equation are avoided because storage and evolution of the error covariance matrix itself are not needed. The results are also better than what is provided by the extended Kalman filter since there is no closure problem and the quality of the forecast error statistics therefore improves. The method should be feasible also for more sophisticated primitive equation models. The computational load for reasonable accuracy is only a fraction of what is required for the extended Kalman filter and is given by the storage of, say, 100 model states for an ensemble size of 100 and thus CPU requirements of the order of the cost of 100 model integrations. The proposed method can therefore be used with realistic nonlinear ocean models on large domains on existing computers, and it is also well suited for parallel computers and clusters of workstations where each processor integrates a few members of the ensemble.