Application of a recursive filter to a three-dimensional variational ocean data assimilation system
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In order to improve the efficiency of the Ocean Variational Assimilation System (OVALS), which has been widely used in various applications, an improved OVALS (OVALS2) is developed based on the recursive filter (RF) algorithm. The first advantage of OVALS2 is that memory storage can be substantially reduced in practice because it implicitly computes the background error covariance matrix; the second advantage is that there is no inversion of the background error covariance by preconditioning the control variable. For comparing the effectiveness between OVALS2 and OVALS, a set of experiments was implemented by assimilating expendable bathythermograph (XBT) and ARGO data into the Tropical Pacific circulation model. The results show that the efficiency of OVALS2 is much higher than that of OVALS. The computational time and the computer storage in the assimilation process were reduced by 83% and 77%, respectively. Additionally, the corresponding results produced by the RF are almost as good as those obtained by OVALS. These results prove that OVALS2 is suitable for operational numerical oceanic forecasting.
Key wordsrecursive filter background error covariance the Ocean Variational Assimilation System (OVALS)
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