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SST data assimilation experiments using an adaptive variational method

An adaptive variational data assimilation method is proposed by Zhu and Kamachi[1]. This method can adaptively adjust the model state without knowing explicitly the model error covariance matrix. The method enables very flexible ways to form some reduced order problems. A proper reduced order problem not only reduces computational burden but also leads to corrections that are more consistent with the model dynamics that trends to produce better forecast. These features make the adaptive variational method a good candidate for SST data assimilation because the model error of an ocean model is usually difficult to estimate. We applied this method to an SST data assimilation problem using the LOTUS data sets and an ocean mixed layer model (Mellor-Yamada level 2.5). Results of assimilation experiments showed good skill of improvement subsurface temperatures by assimilating surface observation alone.