CAMPUS (Combining Autonomous observations and Models for Predicting and Understanding Shelf seas)

CAMPUS（结合自主观测和模型来预测和理解陆架海）

• 批准号: NE/R006768/1
• 负责人: Peter Challenor
• 金额: $ 40.14万
• 依托单位: UNIVERSITY OF EXETER
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FR006768%2F1
• 关键词: CAMPUS; Combining; Autonomous; observations; Models

Shelf seas are of major societal importance providing a diverse range of goods (e.g. fisheries, renewable energy, transport) and services (e.g. carbon and nutrient cycling and biodiversity). Managing UK seas to maintain clean, healthy, safe, productive and biologically diverse oceans and seas is a key governmental objective, as evidenced by the obligations to obtain Good Environmental Status (GES) under the UK Marine Strategy Framework, the Convention on Biological Diversity and ratification of the Oslo-Paris Convention (OSPAR) .. The delivery of these obligations requires comprehensive information about the state of our seas which in turn requires a combination of numerical models and observational programs. Computer modelling of marine ecosystems allows us to explore the recent past and predict future states of physical, chemical and biological properties of the sea, and how they vary in 3D space and time. In an analogous manner to the weather forecast, the Met Office runs a marine operational forecast system providing both short term forecast and multi-decadal historical data products. The quality of these forecasts is improved by using data assimilation; the process of predicting the most accurate ocean state using observations to nudge model simulations, producing a combined observation and model product. Marine autonomous vehicles (MAVs) are a rapidly maturing technology and are now routinely deployed both in support of research and as a component of an ocean observing system. When used in conjunction with fixed point observatories, ships of opportunity and satellite remote sensing, the strategic deployment of MAVs offers the prospect of substantial improvement in our observing network. Marine Gliders in particular have the capability to provide depth resolved data sets of high resolution from deployments that can endure several months and cover 100s kms, allowing the collection of sufficient information to be useful for assimilation into models. We will improve the exchange of data between model systems and observational networks to inform an improved strategy for the deployment of the UK's high-cost marine observing capability. In particular we will utilise mathematical and statistical models to develop and test "smart" autonomy - autonomous systems that are enabled to selectively search and monitor explicit features within the marine system. By developing data assimilation techniques to utilise autonomous data, our model systems will be able to better characterise episodic events such as the spring bloom, harmful algal blooms and oxygen depletion, which are currently not well captured and are key to understanding ecosystem variability and therefore quantifying GES.In doing so CAMPUS will provide a step change in the combined use of observation and modelling technologies, delivered through a combination of autonomous technologies (gliders), other observations and shelf-wide numerical models. This will provide improved analysis of key ocean variables, better predictions of episodic events, and 'smart' observing systems in order to improve the evidence base for compliance with European directives and support the UK industrial strategy.

陆架海具有重大的社会意义，可提供各种货物(如渔业、可再生能源、运输)和服务(如碳和营养循环和生物多样性)。管理联合王国海洋以保持清洁、健康、安全、多产和生物多样性的海洋是一项关键的政府目标，英国海洋战略框架、生物多样性公约和批准奥斯陆-巴黎公约(奥斯陆-巴黎公约)规定的获得良好环境状况的义务证明了这一点。履行这些义务需要关于我们海洋状况的全面信息，而这又需要数值模型和观测方案的结合。海洋生态系统的计算机模拟使我们能够探索最近的过去和预测海洋的物理、化学和生物特性的未来状态，以及它们在3D空间和时间中的变化。与天气预报类似，气象局运行一个海洋业务预报系统，提供短期预报和数十年历史数据产品。这些预报的质量通过使用数据同化来改进；数据同化是使用观测来预测最准确的海洋状态的过程，以推动模型模拟，产生组合的观测和模型产品。海洋自动航行器(MAV)是一项迅速成熟的技术，现在被常规地部署在支持研究和作为海洋观测系统的一个组成部分。当与定点观测站、机遇号船和卫星遥感一起使用时，MAVS的战略部署为我们的观测网络提供了实质性改善的前景。海洋滑翔机尤其有能力从部署中提供高分辨率的深度分辨数据集，这种部署可以持续几个月，覆盖100公里，使收集的足够信息有助于同化到模型中。我们将改进模型系统和观测网络之间的数据交换，为部署英国高成本的海洋观测能力提供改进的战略。特别是，我们将利用数学和统计模型来开发和测试“智能”自主系统，这些系统能够有选择地搜索和监控海洋系统中的明确特征。通过开发数据同化技术来利用自主数据，我们的模式系统将能够更好地描述春季水华、有害藻华和氧气耗竭等周期性事件，这些事件目前没有得到很好的捕捉，是理解生态系统变异性并因此量化GE的关键。通过这样做，校园将在观测和建模技术的组合使用方面产生一步变化，通过自主技术(滑翔机)、其他观测和整个陆架范围的数值模式的组合来提供。这将改进对关键海洋变量的分析，更好地预测事件，并建立“智能”观测系统，以改善遵守欧洲指令的证据基础，并支持英国的产业战略。