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)

基本信息

  • 批准号:
    NE/R006822/2
  • 负责人:
  • 金额:
    $ 23.8万
  • 依托单位:
  • 依托单位国家:
    英国
  • 项目类别:
    Research Grant
  • 财政年份:
    2019
  • 资助国家:
    英国
  • 起止时间:
    2019 至 无数据
  • 项目状态:
    已结题

项目摘要

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.
大陆架海具有重大的社会意义,提供各种各样的货物(如渔业、可再生能源、运输)和服务(如碳和营养物循环及生物多样性)。管理联合王国的海洋,以保持清洁、健康、安全、富有生产力和生物多样性的海洋是政府的一个关键目标,根据联合王国海洋战略框架、《生物多样性公约》和批准《奥斯陆-巴黎公约》,有义务获得良好环境状况就是证明。履行这些义务需要关于我们海洋状况的全面信息,而这又需要将数值模型和观测方案结合起来。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,气象局运行着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。通过使用数据同化来提高这些预报的质量;使用观测结果来推动模型模拟,产生组合观测和模型产品,预测最准确的海洋状态的过程。海洋自主飞行器(MAV)是一项迅速成熟的技术,现在已被常规部署,用于支持研究和作为海洋观测系统的一个组成部分。当与固定点观测站、机会船和卫星遥感结合使用时,微型飞行器的战略部署为我们的观测网络提供了重大改进的前景。海洋滑翔机特别有能力通过部署提供高分辨率的深度分辨数据集,这些数据集可以持续数月并覆盖100公里,从而收集足够的信息,以便用于模型的同化。我们将改进模型系统和观测网络之间的数据交换,为部署联合王国高成本海洋观测能力的改进战略提供信息。特别是,我们将利用数学和统计模型来开发和测试“智能”自主性-自主系统能够有选择地搜索和监控海洋系统中的显式特征。通过开发利用自主数据的数据同化技术,我们的模型系统将能够更好地模拟春季水华、有害藻类水华和氧气消耗等偶发事件,这些事件目前还没有很好地捕捉到,是理解生态系统变异性的关键,因此也是量化GES的关键。在这样做的过程中,CAMPUS将为观测和建模技术的结合使用提供一个步骤,通过自主技术(滑翔机)、其他观测和货架范围的数值模型相结合提供。这将提供对关键海洋变量的更好分析,更好地预测偶发事件,以及“智能”观测系统,以改善遵守欧洲指令的证据基础,并支持英国的工业战略。

项目成果

期刊论文数量(5)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Control of a phytoplankton bloom by wind-driven vertical mixing and light availability
通过风驱动的垂直混合和光照控制浮游植物的繁殖
  • DOI:
    10.1002/lno.11734
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    4.5
  • 作者:
    Hopkins J
  • 通讯作者:
    Hopkins J
Improved consistency between the modelling of ocean optics, biogeochemistry and physics, and its impact on the North-West European Shelf seas
提高海洋光学、生物地球化学和物理学建模之间的一致性及其对西北欧陆架海洋的影响
  • DOI:
    10.1002/essoar.10506737.1
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Skakala J
  • 通讯作者:
    Skakala J
The effect of uncertain river forcing on the thermohaline properties of the North West European Shelf Seas
  • DOI:
    10.1016/j.ocemod.2023.102196
  • 发表时间:
    2023-04
  • 期刊:
  • 影响因子:
    3.2
  • 作者:
    S. Zedler;J. Polton;Robert R. King;S. Wakelin
  • 通讯作者:
    S. Zedler;J. Polton;Robert R. King;S. Wakelin
Designing a Large Scale Autonomous Observing Network: A Set Theory Approach
  • DOI:
    10.3389/fmars.2022.879003
  • 发表时间:
    2022-06
  • 期刊:
  • 影响因子:
    0
  • 作者:
    David Byrne;J. Polton;Joseph Ribeiro;L. Fernand;J. Holt
  • 通讯作者:
    David Byrne;J. Polton;Joseph Ribeiro;L. Fernand;J. Holt
Sensitivity of Shelf Sea Marine Ecosystems to Temporal Resolution of Meteorological Forcing
  • DOI:
    10.1029/2019jc015922
  • 发表时间:
    2020-06
  • 期刊:
  • 影响因子:
    0
  • 作者:
    H. Powley;J. Bruggeman;J. Hopkins;Tim Smyth;J. Blackford
  • 通讯作者:
    H. Powley;J. Bruggeman;J. Hopkins;Tim Smyth;J. Blackford
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Jason Holt其他文献

Modelling terrigenous DOC across the north west European Shelf: Fate of riverine input and impact on air-sea COsub2/sub fluxes
模拟西北欧大陆架上陆源溶解有机碳:河流输入的归宿及其对海气二氧化碳通量的影响
  • DOI:
    10.1016/j.scitotenv.2023.168938
  • 发表时间:
    2024-02-20
  • 期刊:
  • 影响因子:
    8.000
  • 作者:
    Helen R. Powley;Luca Polimene;Ricardo Torres;Muchamad Al Azhar;Victoria Bell;David Cooper;Jason Holt;Sarah Wakelin;Yuri Artioli
  • 通讯作者:
    Yuri Artioli
OS-034 - A gene editing approach for chronic hepatitis B: elimination of hepatitis B virus in vivo by targeting cccDNA and integrated viral genomes with a sequence-specific ARCUS nuclease
  • DOI:
    10.1016/s0168-8278(23)00491-9
  • 发表时间:
    2023-06-01
  • 期刊:
  • 影响因子:
  • 作者:
    Cassandra Gorsuch;Paige Nemec;Mei Yu;Simin Xu;Dong Han;Jeff Smith;Janel Lape;Nicholas Van Buuren;Ricardo Ramirez;Robert Muench;Meghan Holdorf;Becket Feierbach;Greg Falls;Jason Holt;Wendy Shoop;Emma Sevigny;Forrest Karriker;Robert Brown;Amod Joshi;Tyler Goodwin
  • 通讯作者:
    Tyler Goodwin
arcos and arcospy: R and Python packages for accessing the DEA ARCOS database from 2006 - 2014
arcos 和 arcospy:用于访问 2006 年至 2014 年 DEA ARCOS 数据库的 R 和 Python 软件包
  • DOI:
  • 发表时间:
    2020
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Steven Rich;A. B. Tran;Aaron Williams;Jason Holt;Jeffery Sauer;Taylor M. Oshan
  • 通讯作者:
    Taylor M. Oshan
Multi-model comparison of trends and controls of near-bed oxygen concentration on the northwest European continental shelf under climate change
气候变化下西北欧洲大陆架近床氧浓度变化趋势及控制的多模型比较
  • DOI:
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    4.9
  • 作者:
    Giovanni Galli;S. Wakelin;J. Harle;Jason Holt;Y. Artioli
  • 通讯作者:
    Y. Artioli
On the Number of Eigenvalues of the Dirac Operator in a Bounded Interval
  • DOI:
    10.1007/s00023-024-01431-4
  • 发表时间:
    2024-04-06
  • 期刊:
  • 影响因子:
    1.300
  • 作者:
    Jason Holt;Oleg Safronov
  • 通讯作者:
    Oleg Safronov

Jason Holt的其他文献

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{{ truncateString('Jason Holt', 18)}}的其他基金

FOCUS: Future states Of the global Coastal ocean: Understanding for Solutions
焦点:全球沿海海洋的未来状态:了解解决方案
  • 批准号:
    NE/X006271/1
  • 财政年份:
    2022
  • 资助金额:
    $ 23.8万
  • 项目类别:
    Research Grant
Coastal-Oceans in Global Climate Models: Assessment and Analysis (CONGA)
全球气候模型中的沿海海洋:评估和分析(CONGA)
  • 批准号:
    NE/V008552/1
  • 财政年份:
    2021
  • 资助金额:
    $ 23.8万
  • 项目类别:
    Research Grant
Sources, impacts and solutions for plastics in South East Asia coastal environments
东南亚沿海环境中塑料的来源、影响和解决方案
  • 批准号:
    NE/V009591/1
  • 财政年份:
    2020
  • 资助金额:
    $ 23.8万
  • 项目类别:
    Research Grant
Resolving Climate Impacts on shelf and CoastaL sea Ecosystems (ReCICLE)
解决气候对陆架和沿海海洋生态系统的影响 (ReCICLE)
  • 批准号:
    NE/M003477/2
  • 财政年份:
    2019
  • 资助金额:
    $ 23.8万
  • 项目类别:
    Research Grant
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/R006822/1
  • 财政年份:
    2018
  • 资助金额:
    $ 23.8万
  • 项目类别:
    Research Grant
Coastal Resilience to flooding Impact through relocatable Storm surge forecasting Capability for developing nations (C-RISC)
沿海地区的洪水恢复能力 通过可重新定位的风暴潮预报的影响 发展中国家的能力 (C-RISC)
  • 批准号:
    NE/R009406/1
  • 财政年份:
    2017
  • 资助金额:
    $ 23.8万
  • 项目类别:
    Research Grant
Resolving Climate Impacts on shelf and CoastaL sea Ecosystems (ReCICLE)
解决气候对陆架和沿海海洋生态系统的影响 (ReCICLE)
  • 批准号:
    NE/M003477/1
  • 财政年份:
    2015
  • 资助金额:
    $ 23.8万
  • 项目类别:
    Research Grant
Integration of improved understanding of ecosystem service regulation into ERSEM model system
将加深对生态系统服务调节的理解纳入 ERSEM 模型系统
  • 批准号:
    NE/L003147/1
  • 财政年份:
    2014
  • 资助金额:
    $ 23.8万
  • 项目类别:
    Research Grant
Towards a Next generation Ocean Model in the Gung-Ho framework: 2D test cases (G-Ocean:2D)
在 Gung-Ho 框架中迈向下一代海洋模型:2D 测试用例 (G-Ocean:2D)
  • 批准号:
    NE/L012111/1
  • 财政年份:
    2014
  • 资助金额:
    $ 23.8万
  • 项目类别:
    Research Grant
Integrative Modelling for Shelf Seas Biogeochemistry
陆架海生物地球化学综合模拟
  • 批准号:
    NE/K001698/1
  • 财政年份:
    2013
  • 资助金额:
    $ 23.8万
  • 项目类别:
    Research Grant

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