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

同步地球观测和建模框架以实现数字孪生海洋 (SyncED-Ocean)

• 批准号: NE/Z50337X/1
• 负责人: Matthew Palmer
• 金额: $ 85.56万
• 依托单位: Plymouth Marine Laboratory
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FZ50337X%2F1
• 关键词: Synchronising; Earth; Observation; Modelling; Frameworks

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.

同步海洋将提供一个沿海海洋生态系统的数字双胞胎（DT）试点演示器，该沿海海洋生态系统使用并优化了EO数据以吸收海洋系统模型，并通过海洋自主系统增强了我们的DT的真实和虚拟组件之间的敏捷和适应性连接性。为了开发所需的数据和计算体系结构，我们将重点关注有害藻华（HAB）的当前预测能力，以及随后对英国沿海海洋海洋氧含量的影响。这两种现象都代表着自然危害，威胁着海洋健康，生物多样性和生产力的严重风险，并且有证据表明，在气候变化下，沿海海中每种都会加剧。因此，我们将牢固地解决每个描述的优先领域。我们的演示者将提供完整的数据管道（如IMFE报告中所述），以提供一个能够为未来的研究，政策和商业应用程序提供支持的敏捷DT，这些敏捷的DT专注于改善我们环境的管理以及它所支持的自然资本和生态系统服务的管理。状态的“现实世界”，以适当地告知我们DT中的虚拟组件。通过此组合和优化的数据集，将通过集体代表与沿海海洋生态系统相关的广泛量表来解决各个数据源的空间和覆盖偏差。 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可扩展，模块化和建立的社区最佳实践。这将使我们的DT体系结构重新配置和部署，以支持广泛的未来环境DT，从而有助于这项要求的投资的持久遗产。