A Python interface to the Fortran-based Parallel Data Assimilation Framework: pyPDAF v1.0.0

Abstract. Data assimilation (DA) is an essential component of numerical weather and climate prediction. Efficient implementation of DA benefits both operational prediction and research. Currently, a variety of DA software programs are available. One of the notable DA libraries is the Parallel Data Assimilation Framework (PDAF) designed for ensemble data assimilation. The DA framework is widely used with complex high-dimensional climate models and is applied for research on atmosphere, ocean, sea ice and marine ecosystem modelling, as well as operational ocean forecasting. Meanwhile, there exists increasing need for flexible and efficient DA implementations using Python due to the increasing amount of intermediate complexity models as well as machine learning based models coded in Python. To accommodate for such needs, here, we introduce a Python interface to PDAF, pyPDAF. The Python interface allows for flexible DA system development while retaining the efficient implementation of the core DA algorithms in the Fortran-based PDAF. The ideal use-case of pyPDAF is a DA system where the model integration is independent from the DA program, which reads the model forecast ensemble, produces a model analysis and update the restart files of the model, or a DA system where the model can be used in Python. With implementations of both PDAF and pyPDAF, this study demonstrates the use of pyPDAF and PDAF for coupled data assimilation (CDA) in a coupled atmosphere and ocean model, the Modular Arbitrary-Order Ocean-Atmosphere Model (MAOOAM). Using both weakly and strongly CDA, we demonstrate that pyPDAF allows for the utilisation of Python-based user-supplied functions in the Fortran-based DA framework. We also show that the Python-based user-supplied routine can be a main reason for the slow-down of the DA system based on pyPDAF. Our CDA experiments confirm the benefit of strongly coupled data assimilation compared to the weakly coupled data assimilation. We also demonstrate that the CDA not only improves the instantaneous analysis but also the long-term trend of the coupled dynamical system.