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A Digital Twin Ocean: can we improve coastal ocean forecasts using targeted marine autonomy?
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DOI：10.5194/os-22-2083-2026.png)
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Abstract
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Abstract. 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 address 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 maxima and oxygen minima; 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.
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