Synchronising Earth Observation and Modelling Frameworks Towards a Digital Twin Ocean (SyncED-Ocean)

Synce-Ocean将提供一个沿海海洋生态系统的数字孪生(DT)试点演示器，它使用和优化EO数据以同化到海洋系统模型，并由海洋自主系统增强，使我们的DT的真实组件和虚拟组件之间能够灵活和自适应地连接。为了开发所需的数据和计算架构，我们将重点显著提高当前有害藻华(HAB)的预测能力，以及随后对英国沿海海洋氧含量的影响。这两种现象都是自然灾害，对海洋健康、生物多样性和生产力构成严重威胁，越来越多的证据表明，在气候变化的影响下，这两种现象在沿海海域都在加剧。因此，我们将坚定地处理所述的每一个优先领域。我们的演示者将提供完整的数据管道(如IMFe报告中所述)，以提供一个灵活的DT，能够为未来的研究、政策和商业应用提供决策支持工具，重点是改善我们的环境及其支持的自然资本和生态系统服务的管理。我们将在项目团队之前的成功基础上，结合EO、自主现场和海洋模型数据，增加对所选观测数据集的信任，以提供真实世界“真实状态”的最佳表示，适当地为DT中的虚拟组件提供信息。将利用这一合并和优化的数据集，通过综合表示与沿海海洋生态系统有关的各种尺度，解决来自个别数据源的空间、时间和覆盖偏差问题。自适应采样框架将通过智能观测系统重新连接虚拟双胞胎，该系统指导移动的自主海洋滑翔机最好地向DT提供信息并改进其HAB和氧气预测目标。我们的数据和计算架构将基于公平和开源原则围绕EO和自治社区接口开发，DT框架将与环境和数字专家和社区密切合作开发，以确保其核心功能是可扩展的、模块化的和建立在社区最佳实践之上的。这将使我们的DT架构能够重新配置和部署，以支持未来广泛的环境DT，为这一请求投资的持久遗产做出贡献。

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.