A Digital Twin Ocean: Can we improve Coastal Ocean Forecasts using targeted Marine Autonomy?

This study outlines the development and testing of a Digital Twin Ocean (DTO) framework, aimed at improving coastal ocean forecasts through the use of autonomous underwater gliders. A fleet of gliders were deployed in the western English Channel during August–September 2024 to collect measurements of temperature, salinity, chlorophyll and oxygen, aiming to track the movement of the harmful algal bloom Karenia mikimotoi. Measurements were assimilated into a very high resolution (1.5 km) numerical forecast model, with an implementation of biogeochemistry data assimilation for this purpose. The model forecast was then used by a probabilistic uncertainty model to plan a series of waypoints to navigate the glider fleet towards features of interest. By utilising a continuous feedback loop of measurement, prediction, guidance, and refinement a system with real time coupling between the real ocean environment and its digital counterpart has been established. Building upon a prior pilot study of Ford et al. (2022), this work improves every element of the system to addresses several limitations of the prior configuration. Whilst a bloom was present in the wider area, measurements and modeling suggest it didn't enter the glider operation zone. Despite this and other operational challenges the mission clearly demonstrates the benefits of such a system. The ability to simultaneously track multiple features of interest, namely chlorophyll and oxygen, would not have been possible with a single glider resulting in significant benefits to the system. Furthermore, the improvement to biogeochemical forecasting has been demonstrated through a series of post mission experiments, highlighting the advantages of high temporal resolution observations and increased spatial resolution of the model.