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Comparison of reduced-order, sequential and variational data assimilation methods in the tropical Pacific Ocean

机译：热带太平洋降序，序贯和变分数据同化方法的比较

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摘要

This paper presents a comparison of two reduced-order, sequential, and variational data assimilation methods: the singular evolutive extended Kalman filter (SEEK) and the reduced 4D-Var (R-4D-Var). A hybridization of the two, combining the variational framework and the sequential evolution of covariance matrices, is also preliminarily investigated and assessed in the same experimental conditions. The comparison is performed using the twin-experiment approach on a model of the tropical Pacific domain. The assimilated data are simulated temperature profiles at the locations of the TAO/TRITON array moorings. It is shown that, in a quasilinear regime, both methods produce similarly good results. However, the hybrid approach provides slightly better results and thus appears as potentially fruitful. In a more nonlinear regime, when tropical instability waves develop, the global nature of the variational approach helps control model dynamics better than the sequential approach of the SEEK filter. This aspect is probably enhanced by the context of the experiments in that there is a limited amount of assimilated data and no model error.
机译：本文介绍了两种降序，顺序和变分数据同化方法的比较：奇异演化的扩展卡尔曼滤波器（SEEK）和简化的4D-Var（R-4D-Var）。在相同的实验条件下，还初步研究和评估了两者的混合，结合了变异框架和协方差矩阵的顺序演化。使用双实验方法对热带太平洋区域的模型进行比较。吸收的数据是TAO / TRITON阵列系泊位置处的模拟温度曲线。结果表明，在准线性状态下，两种方法均产生相似的良好结果。但是，混合方法提供了更好的结果，因此似乎很有用。在更非线性的情况下，当热带不稳定波发展时，与SEEK滤波器的顺序方法相比，变分方法的全局性质可以更好地控制模型动力学。实验方面可能会增强这一方面，因为同化数据量有限且没有模型错误。

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