Efficient ensemble data assimilation for coupled models with the Parallel Data Assimilation Framework: example of AWI-CM (AWI-CM-PDAF 1.0)

• Published in: Geoscientific Model Development
• Main Authors: Nerger, Lars, Tang, Qi, Mu, Longjiang
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2020
• Subjects: article; Verlagsveröffentlichung; Sea ice
• Online Access: https://doi.org/10.5194/gmd-13-4305-2020; https://noa.gwlb.de/receive/cop_mods_00053105; https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00052758/gmd-13-4305-2020.pdf; https://gmd.copernicus.org/articles/13/4305/2020/gmd-13-4305-2020.pdf

Data assimilation integrates information from observational measurements with numerical models. When used with coupled models of Earth system compartments, e.g., the atmosphere and the ocean, consistent joint states can be estimated. A common approach for data assimilation is ensemble-based methods which utilize an ensemble of state realizations to estimate the state and its uncertainty. These methods are far more costly to compute than a single coupled model because of the required integration of the ensemble. However, with uncoupled models, the ensemble methods also have been shown to exhibit a particularly good scaling behavior. This study discusses an approach to augment a coupled model with data assimilation functionality provided by the Parallel Data Assimilation Framework (PDAF). Using only minimal changes in the codes of the different compartment models, a particularly efficient data assimilation system is generated that utilizes parallelization and in-memory data transfers between the models and the data assimilation functions and hence avoids most of the file reading and writing, as well as model restarts during the data assimilation process. This study explains the required modifications to the programs with the example of the coupled atmosphere–sea-ice–ocean model AWI-CM (AWI Climate Model). Using the case of the assimilation of oceanic observations shows that the data assimilation leads only to small overheads in computing time of about 15 % compared to the model without data assimilation and a very good parallel scalability. The model-agnostic structure of the assimilation software ensures a separation of concerns in which the development of data assimilation methods can be separated from the model application.