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Chapter 12

Challenges and future perspectives in ocean prediction

CHAPTER
COORDINATORS
Fraser Davidson
CHAPTER
AUTHORS
Enrique Alvarez Fanjul, Alain Arnaud, Stefania Ciliberti, Marie Drevillon, Ronan Fablet, Yosuke Fujii, Isabel Garcia-Hermosa, Stéphanie Guinehut, Emma Heslop, Villy Kourafalou, Julien Le Sommer, Matt Martin, Andrew M. Moore, Nadia Pinardi, Elizabeth Remy, Paul Sandery, Jun She, Marcos G. Sotillo, and Joaquin Tintorè

12.8 Operational forecasting and scenarios in a digital ocean

A Digital Twin of the Ocean (DTO) is a highly accurate model of the ocean to monitor and predict environmental change, human impact, and vulnerability, with the support of an openly accessible and interoperable dataspace that can function as a central hub for informed decision making (Figure 12.3) (see for example 🔗9). Such an information system consists of one or more digital replicas of the state and temporal evolution of the oceanic system constrained by the available observations and the laws of physics, making imperative to integrate a set of models or software that pairs the digital world with physical assets, and to feed this set with information from sensors.

Figure 12.3. Schematic representation of Digital Twin of the Ocean concept.

A DTO aims to deliver a holistic and cost-effective solution for the integration of all information sources related to seas and oceans, like in situ-data and satellite information combined with IoT techniques, Citizen science, state-of-the-art  ocean modelling together with AI and HPC resources into a digital, consistent, high-resolution, multi-dimensional, and near real-time representation of the ocean. This will result in a shared capacity to access, manipulate, analyse, and visualise marine information. The knowledge generated by the DTO platform will empower scientists, citizens, governments, and industries to collectively share the responsibility to monitor, preserve and enhance marine and coastal habitats, while promoting action and sustainable measures in the framework of the blue economy (tourism, fishing, aquaculture, transport, renewable energy, etc.), contributing to a healthy and productive ocean.

12.8.1 Construction of an open DTO service platform

To properly address the construction of a digital twin, breakthroughs are needed in various aspects of the digital twin information system, including information completeness and quality, information access and intervention, as well as the underlying supporting infrastructure, tools, and services. The operational pilot of DTO, under development at European level, will encompass the production of a new quality of information, incorporating human systems in the prediction problem and leveraging advances in information theory and digital technologies. Ensembles of simulations combining models from different disciplines, informed by spatial correlations determined from high-resolution observations and by data-driven learning of unknown processes and missing constraints, will enable the DTO to reduce uncertainty in estimation and forecasting of ocean states, changes, and impacts.

Enhancing information quality requires a step change in computational complexity. This means adequate infrastructure including support of very high computing throughputs, concurrency, and extreme-scale hardware. However, it is important to conceal this complexity so that users can run and configure involved workflows and access the information but without requiring expert intervention. In addition, the underlying models and data need to be scientifically sound.

This will require a multi-layered software framework where tasks like simulations, observational data ingestion, and post-processing are treated as objects that are executed on federated computing infrastructures, feed data into virtual data repositories with standardised metadata, and from which a heavily machine-learning-based toolkit extracts information that can be manipulated in any possible way. The result should be the provision of on-demand, conveniently accessible modelling and simulation products, data and processes or MSaaS.

12.8.2 Underlying architecture

The multi-layered framework enabling this digital twin ocean pilot operational service comprises 3 major interrelated structural elements (Figure 12.4):

  • A DTO data access layer that mixes results and tools from ongoing projects and existing infrastructures with new developments targeting data ingestion, and data harmonising into a Data lake for subsequent use in the DTO engine;
  • A DTO engine comprising a set of modelling capabilities, including on-demand modelling and what-if scenario modelling that fill the observational gaps in space and time in a physically consistent way, and observation-driven learning of unknown processes and missing constraints, which will enable to reduce uncertainty in estimation and forecasting;
  • A DTO interactive service layer supplying tools, libraries, and interfaces to simplify running and configuration of workflows, as well as access to information, including its analysis and visualisation.
Figure 12.4. DTO Architecture.
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Chapter 12

Challenges and future perspectives in ocean prediction

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