Copernicus Marine Environment Monitoring Service (CMEMS) evolution: new and innovative ocean data assimilation techniques

Project results are expected to contribute to all of the following expected outcomes:

• To improve ocean monitoring and predictions, and to remain at the forefront at the international level;
• To ensure CMEMS users take full advantage from advances in observations, reanalysis, analysis and forecasting systems;
• To strengthen data assimilation development exchanges between operational centres and to facilitate scientific community contributions.

The areas of R&I to address the above expected outcomes include:

• Coupled data assimilation (e.g. between ocean and biogeochemistry, ocean and sea-ice, ocean and waves and atmosphere) to control in a more consistent way the ocean state variables across ocean components or forcings and to get more benefits from observations;
• Development of multi-scale methods capable of assimilating high-resolution and high-frequency observations as well as of constraining larger scales;
• Development of methods to produce reliable estimation of analysis and forecast uncertainties;
• Use of Artificial Intelligence techniques in data assimilation schemes (e.g. use of emulators for ensemble generation, model error estimation, bias correction, separation of scale and model parameter estimation);
• Use of new types of observations (e.g. new Sentinel Expansion and Next Generation missions, new in-situ observations) or higher-resolution of existing data streams;
• Development of methods and tools to systematically assess the observing system impact in data assimilation systems (e.g. analysis and forecast sensitivity to observation);
• The development of software infrastructure that can accommodate different assimilation methods (including artificial intelligence techniques), and facilitate the sharing of algorithms and optimization of computer codes (assimilation schemes) on high-performance computers;
• The development of validation and intercomparison protocols to estimate and quantify the benefits of improved assimilation methods and integration of new observations (e.g. data challenges, use of OSSEs).

The development of new types of observations from satellites and in-situ and the improvement of space/time sampling of existing observations require a step change in data assimilation techniques to fully benefit from these new sources of data. Current techniques are facing hard limits to progress and need new approaches to make the best use of observation and advances in modelling (e.g. resolution). Emerging coupling, multi-scale, ensemble and artificial intelligence techniques represent new opportunities for significant improvements in ocean data assimilation. The proposal should include some demonstrations of downstream applications that would benefit from these improvements.

The transfer of research results to operations should receive active attention during the project to strengthen the readiness for an operational deployment in the future. Appropriate involvement and/or interaction with the relevant Entrusted Entities of the Copernicus services, the conditions for making available, for re-using and exploiting the results (including IPR) by the said entities must be addressed during the project implementation. Software should be open licensed.

Potential contributions to the European Digital Twin Ocean and the GEO Blue Planet initiative should also receive attention during the project, given that the CMEMS is and should remain an important player in these initiatives. International cooperation is hence encouraged in this topic.

Actions are encouraged to build on or cooperate with previous or ongoing Horizon funded projects contributing to different components of the global ocean observing value chain, for example - but not limited to - LandSeaLot, EPOC, EERIE, ObsSea4Clim, etc.

In this topic, the integration of the gender dimension (sex and gender analysis) in research and innovation content should be addressed only if relevant in relation to the objectives of the research effort.