Coupled data assimilation is presented in detail. Starting from a coupled modeling system, a classification of coupled data assimilation based on coupling strength is defined. This includes uncoupled, weakly coupled, and strongly coupled data assimilation, and the coupling strength is quantified using mutual information. The most interesting aspects of coupled data assimilation can be related to a strongly coupled system in which the information exchange is maximized. The challenges of strongly coupled data assimilation include the account of the complex control variable and error covariance. The mentioned challenges can considerably increase in realistic high-dimensional applications. Additional issues that can hamper strongly coupled data assimilation include non-Gaussian errors and potentially different spatiotemporal scales of coupled system components. To improve understanding of strongly coupled data assimilation, a simple two-component system is introduced and analyzed. The theoretical assessment is followed by real-world examples of strongly coupled forecast error covariance. Finally, the coupled covariance localization is analyzed and a practical method to address it is described.