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Regional and basin scale applications of ensemble adjustment Kalman filter and 4D-Var ocean data assimilation systems

机译:集合调整卡尔曼滤波器和4D-VAR海洋数据同化系统的区域和盆地尺度应用

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The performance of two common approaches to data assimilation, an Ensemble Adjustment Kalman Filter (EAKF) and a 4-dimensional variational (4D-Var) method, is quantified in a popular community ocean model, the Regional Ocean Modeling Systems (ROMS). Two distinct circulation environments are considered: the California Current System (CCS), which is an eastern boundary upwelling regime, and the Indian Ocean (IO) characterized by an equatorial waveguide subject to the energetic seasonal reversals of the Indian and Asian Monsoons. In the case of the CCS, experiments were performed using synthetic observations, so-called Observing System Simulation Experiments (OSSEs). An extensive suite of CCS OSSEs were conducted to explore the performance of both data assimilation approaches to system configuration. For the EAKF, this includes the method for generating the seed ensemble, ensemble size, localization scales, and the length of the assimilation window. In the case of 4DVar, the influence of assimilation window length, and the formulation of the background error covariance were explored. The performance of the EAKF was found to be influenced most by the size of the ensemble and by the method used to generate the initial seed ensemble where centering of the ensemble was found to yield improvement. For 4D-Var, the assimilation window length is by far the most critical factor, with an increase in system performance as the window length is extended. In general, the EAKF and 4D-Var systems converge to similar solutions over time, which are independent of the starting point. The EAKF employs a First-Guess at Appropriate Time (FGAT) strategy, and some experiments indicate that short FGAT windows can be problematic due to the introduction of frequent initialization shocks. While the EAKF generally out-performs 4D-Var in the OSSEs, analysis of the innovations from the two systems through time indicates that they track each other closely.Additional Observing System Experiments (OSEs) were performed in the CCS and IO configurations of ROMS using real ocean observations. In this case, the comparison of the EAKF and 4D-Var state estimates with independent observations indicates that the EAKF and 4D-Var state estimates diverge over time, although the 4D-Var estimates are somewhat better by some measures. The relative performance of the EAKF and 4D-Var systems is similar across the wide-range of circulation regimes that characterize the CCS and IO, suggesting that the results presented here are a robust indicator of expected performance in other regions of the world ocean.
机译:两个常见的数据同化方法,一个集合调整卡尔曼滤波器(SEAHF)和4维变分(4D-VAL)方法的性能在一个受欢迎的社区海洋模型中量化,区域海洋模型(ROM)。考虑了两个不同的循环环境:加州当前系统(CCS),即东部边界升高的制度,以及印度洋(IO),其特征在于赤道波导,受到印度和亚洲季风的能量季节性逆转。在CCS的情况下,使用合成观察,所谓的观察系统模拟实验(OSSES)进行实验。进行了广泛的CCS OSSES套件,以探讨数据同化方法对系统配置的性能。对于EAKF,这包括用于生成种子集合,集合大小,定位尺度的方法以及同化窗口的长度。在4DVAR的情况下,探讨了同化窗口长度的影响,以及背景误差协方差的制定。发现EAKF的性能受到集合的大小的影响,并且通过用于产生初始种子集合的方法,其中发现集中的初始种子集合以产生改善。对于4d-var,同化窗口长度是最关键的因素,随着窗口长度延伸,系统性能增加。通常,EAKF和4D-VAR系统随着时间的推移收敛到类似的解决方案,它们与起始点无关。 EAKF在适当的时间(FGAT)策略中雇用了一个猜测,并且一些实验表明,由于引入频繁初始化冲击,短FGAT窗口可能是有问题的。虽然EAKF通常在OSSE中出现4D-VAR而,通过时间分析来自两个系统的创新表明它们彼此密切地跟踪。在CCS和IO的配置中,在CCS和IOS的配置中进行了彼此的互相跟踪真正的海洋观察。在这种情况下,具有独立观察的EAKF和4D-VAR状态估计的比较表明,EAKF和4D-VAR状态随时间估计偏差,尽管4D-VAR估计有些措施有所更好。 EAKF和4D-VAR系统的相对性能在表征CCS和IO的广泛循环制度中类似,这表明这里提出的结果是世界海洋其他地区的预期绩效的强大指标。

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