… Data assimilation is the application of Bayes' theorem to … , we cannot expect that data
assimilation will preserve all of the … , observations, and evaluation data in a way that allows us to …

… The main purpose of this article is to describe a practical framework for data assimilation
that is both relatively easy to implement and computationally efficient, even for large, …

… of other grid points, becomes more efficient as the number of processors grows. We briefly
discuss the LETKF scheme, where the data assimilation is performed at the central grid point …

… In this study, our aim is to introduce an efficient data assimilation method using the AC
equation with an additional term. The AC equation has been extensively used in various fields …

Almost all research fields in geosciences use numerical models and observations and
combine these using data‐assimilation techniques. With ever‐increasing resolution and …

… modeling and efficient data assimilation. The … data assimilation. The comparison results
showed that the EKF-DBN outperforms BP-DBN and RNN in both computational efficiency and …

… Thus, DA methods are designed to achieve the best possible use of a never sufficient (albeit
constantly growing) amount of data, and to attain an efficient data-model fusion, in a short …

… data assimilation, coupled data assimilation, the 4D-Var algorithm itself, an improved
framework for the assimilation code, algorithmic efficiency… of the data assimilation algorithms, …

… data assimilation methodology in section Data Assimilation Methodology. In section Applications
of Data Assimilation … We then discuss the outlook and challenges for data assimilation …

… Variational Data Assimilation (DA) has been broadly used in … data. In recent years, the
integration of deep learning (DL) techniques in DA has shown promise in improving the efficiency …