Global S&T Development Trend Analysis Platform of Resources and Environment
项目编号 | NE/R006768/1 |
CAMPUS (Combining Autonomous observations and Models for Predicting and Understanding Shelf seas) | |
Peter Challenor | |
主持机构 | University of Exeter |
项目开始年 | 2018 |
2018-04-01 | |
项目结束日期 | 2021-03-31 |
资助机构 | UK-NERC |
项目类别 | Research Grant |
项目经费 | 315019(GBP) |
国家 | 英国 |
语种 | 英语 |
英文摘要 | 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. |
来源学科分类 | Natural Environment Research |
文献类型 | 项目 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/87062 |
专题 | 环境与发展全球科技态势 |
推荐引用方式 GB/T 7714 | Peter Challenor.CAMPUS (Combining Autonomous observations and Models for Predicting and Understanding Shelf seas).2018. |
条目包含的文件 | 条目无相关文件。 |
个性服务 |
推荐该条目 |
保存到收藏夹 |
查看访问统计 |
导出为Endnote文件 |
谷歌学术 |
谷歌学术中相似的文章 |
[Peter Challenor]的文章 |
百度学术 |
百度学术中相似的文章 |
[Peter Challenor]的文章 |
必应学术 |
必应学术中相似的文章 |
[Peter Challenor]的文章 |
相关权益政策 |
暂无数据 |
收藏/分享 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论