Synchronising Earth Observation and Modelling Frameworks Towards a Digital Twin Ocean (SyncED-Ocean)
Lead Research Organisation:
Plymouth Marine Laboratory
Department Name: Plymouth Marine Lab
Abstract
SyncED-Ocean will deliver a digital twin (DT) pilot demonstrator of a coastal ocean ecosystem that uses and optimises EO data for assimilation to marine system models, augmented by marine autonomous systems that enable agile and adaptive connectivity between the real and virtual components of our DT. To develop the required data and computational architecture, we will focus on significantly improving on current predictive capability of harmful algal blooms (HABs) and subsequent impacts on ocean oxygen content in UK coastal seas. Both phenomena represent natural hazards, threatening serious risk to ocean health, biodiversity and productivity, and evidence is growing that each are being exacerbated in coastal seas under climate change. As such, we will firmly address each of the described priority areas. Our demonstrator will deliver complete data pipelines (as described in the IMFe report) towards providing an agile DT capable of underpinning decision support tools for future research, policy and commercial applications focused on improved management of our environment and the natural capital and ecosystem services it supports.
We will build on previous success from the project team in combining EO, autonomous in situ and ocean model data, increasing trust in the chosen observational datasets to provide the best representation of the 'true state' of the real-world, to suitably inform the virtual component within our DT. Space, time and coverage biases from individual data sources will be addressed through collective representation of the broad range of scales relevant to coastal ocean ecosystems using this combined and optimised dataset. An adaptive sampling framework will reconnect the virtual twin via a smart observing system that directs mobile autonomous ocean gliders to best inform and improve the DT towards its HAB and oxygen prediction objectives.
Our data and computational architecture will be developed around EO and autonomy community interfaces based on FAIR and open source principles and the DT framework will be developed in close collaboration with both environmental and digital experts and communities to ensure its core functionalities are scalable, modular and built-upon community best practices. This will enable our DT architecture to be reconfigured and deployed to support a wide range of future environmental DTs, contributing to a lasting legacy of this requested investment.
We will build on previous success from the project team in combining EO, autonomous in situ and ocean model data, increasing trust in the chosen observational datasets to provide the best representation of the 'true state' of the real-world, to suitably inform the virtual component within our DT. Space, time and coverage biases from individual data sources will be addressed through collective representation of the broad range of scales relevant to coastal ocean ecosystems using this combined and optimised dataset. An adaptive sampling framework will reconnect the virtual twin via a smart observing system that directs mobile autonomous ocean gliders to best inform and improve the DT towards its HAB and oxygen prediction objectives.
Our data and computational architecture will be developed around EO and autonomy community interfaces based on FAIR and open source principles and the DT framework will be developed in close collaboration with both environmental and digital experts and communities to ensure its core functionalities are scalable, modular and built-upon community best practices. This will enable our DT architecture to be reconfigured and deployed to support a wide range of future environmental DTs, contributing to a lasting legacy of this requested investment.