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.

货架海具有重要的社会重要性，可提供各种商品（例如渔业，可再生能源，运输）和服务（例如碳和营养循环和生物多样性）。管理英国以维持清洁，健康，安全，生产力和生物学上多样化的海洋和海洋是政府的关键目标，这证明了在英国海洋策略框架下获得良好环境地位（GES）的义务证明观察计划。海洋生态系统的计算机建模使我们能够探索最近的过去，并预测海洋物理，化学和生物学特性的未来状态，以及它们在3D时空和时间上的变化。大都会办公室以与天气预报相似的方式运行了一个海洋运营预测系统，可提供短期预测和多年历史数据产品。通过使用数据同化，这些预测的质量得到提高。使用观测值来预测最准确的海洋状态的过程，以推动模型模拟，从而产生合并的观察和模型产物。海洋自动驾驶汽车（MAV）是一项快速成熟的技术，现在通常部署既支持研究，也可以作为海洋观察系统的组成部分。当与固定点观测站，机会和卫星遥感的船舶结合使用时，MAV的战略部署可在我们的观察网络中实质性改善。尤其是海上滑翔机可以从部署中提供深度分辨率的深度数据集，这些数据集可以忍受几个月并覆盖100公里，从而使足够信息的收集有助于吸收到模型中。我们将改善模型系统和观察网络之间的数据交换，以告知改进的英国高成本海洋观察能力的策略。特别是，我们将利用数学和统计模型来开发和测试“智能”自治 - 自主系统可以选择性地搜索和监视海洋系统中的显式特征。通过开发数据同化技术来利用自主数据，我们的模型系统将能够更好地表征诸如春季盛开的情节事件，春季绽放，有害的藻华和氧气消耗，目前尚不很好地捕获，这些事件目前却没有充分捕获，并且是理解生态系统可变性的关键技术（滑翔机），其他观测值和范围范围的数值模型。这将提供对关键海洋变量的分析，更好地预测情节事件以及“智能”观察系统，以提高遵守欧洲指令的证据基础并支持英国工业战略。