Developing a common, flexible and efficient framework for weakly coupled ensemble data assimilation based on C-Coupler2.0

Abstract. Data assimilation (DA) provides better initial states of model runs by combining observational information and models. Ensemble-based DA methods that depend on the ensemble run of a model have been widely used. In response to the development of seamless prediction based on coupled models or even earth system models, coupled DA is now in the mainstream of DA development. In this paper, we focus on the technical challenges in developing a coupled ensemble DA system, which have not been satisfactorily addressed to date. We first propose a new DA framework DAFCC1 (Data Assimilation Framework based on C-Coupler2.0, version 1) for weakly coupled ensemble DA, which enables users to conveniently integrate a DA method into a model as a procedure that can be directly called by the model. DAFCC1 automatically and efficiently handles data exchanges between the model ensemble members and the DA method, and enables the DA method to utilize more processor cores in parallel execution. Based on DAFCC1, we then develop a sample weakly coupled ensemble DA system by combining the ensemble DA system GSI/EnKF and the coupled model FIO-AOW. This sample DA system and our evaluations demonstrate the effectiveness of DAFCC1 in both developing a weakly coupled ensemble DA system and accelerating the DA system.