Related to our research projects we developed the data assimilation framework PDAF (Parallel Data Assimilation Framework). PDAF is one of the most widely used software frameworks for data assimilation.
PDAF simplifies the implementation of data assimilation systems based on existing numerical models so that one can faster get to the point to actually apply data assimilation. Further, PDAF allows to easily assess different data assimilation algorithms under identical conditions, which supports the development of new data assimilation methods. PDAF provides complete implementations of data assimilation algorithms, in particular ensemble Kalman filters, particle filters and 3-dimensional variational methods, which are optimized for application on parallel computers (see Nerger and Hiller, 2013 and Nerger et al., 2020).
PDAF is available as free open-source software and is continuously further advanced with new assimilation methods, and tools for data assimilation. Further PDAF was coupled to various different models and many of these coupling codes are available as open source code. More information on PDAF can be found on the AWI web page on PDAF and on the project web pages of PDAF where PDAF can also be downloaded.