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Stochastic Assimilation for the Next Generation Ocean Model Applications

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New assimilation techniques for the generation of ocean models

An EU-funded European network of experts has created state-of-the-art ocean monitoring and forecasting models, providing scientists with the best available tools for investigating the effects of climate change.

Climate Change and Environment
Food and Natural Resources

MYOCEAN was the first European project dedicated to implementation of the Copernicus marine core service for ocean monitoring and forecasting. The SANGOMA (Stochastic assimilation for the next generation ocean model applications) project was established to advance existing data assimilation techniques to support future operational systems. Data assimilation techniques combined observational data with numerical models to improve the model state and its predictions. The most common application of data assimilation was weather forecasting. The state and predictions of an ocean model could also be significantly improved by data assimilation — for example, by utilising satellite observations of sea surface temperature or height. Researchers studied the impact of existing and new satellite observations on model estimates and their potential in an ensemble-based data assimilation software system. Sea surface salinity and temperature data were found to be important for large-scale ocean models. Coastal altimetry, high-frequency radar and glider data were of interest in regional models. The findings served as a starting point to develop a library of data assimilation algorithms and related analysis tools to share with the data assimilation community. A platform on SourceForge, a web-based source code repository that helps manage free and open source software development, was established to facilitate collaboration during the course of the SANGOMA project. Diagnostics and other utility tools allowed analysis of the performance of ensemble-based data assimilation algorithms. Moreover, perturbation tools were used to generate ensembles of model states. A prediction of an error at a future point in time could be computed by integrating each ensemble state independently by the model. The integrations are typically performed until observations are available. SANGOMA thus provided new developments in data assimilation to ensure that future operational systems made use of state-of-the-art data assimilation and related analysis tools. Moreover, the systems enabled efficient operational testing of the latest data assimilation methods, and quick comparison of assimilation methods for operational use. The project’s dedicated website provides access to validated products, including documented performances on a variety of test cases. In addition, consolidated versions have been made available to the science community and Marine Forecasting Centres with advice for the implementation of best practice implementation. Finally, workshops and summer schools on advanced assimilation methods and modular systems have ensured fast and efficient training for the next generation oceanographers.


MYOCEAN, Copernicus, SANGOMA, stochastic assimilation, ocean model

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