UK - China Initiative to Develop Predictive Multi-Scale Ocean Modelling as a Key Aspect of a Joint Environmental Modelling Centre

中英倡议开发多尺度海洋预测模型作为联合环境模型中心的关键部分

基本信息

批准号：
NE/J015938/1
负责人：
Peter Allison
金额：
$ 29.58万
依托单位：
Imperial College London
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2012
资助国家：
英国
起止时间：
2012 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=NE%2FJ015938%2F1
关键词：
UK China Initiative Develop Predictive

项目摘要

China is increasingly taking the lead in solutions to environmental problems and this will continue as substantial Chinese investment is scheduled for this purpose. The Institute of Atmospheric Physics (IAP) in Beijing is an internationally leading organisation in this area and will substantially benefit from this additional investment. The Applied Modelling and Computational Group at Imperial College London (AMCG-ICL) is an environmental modelling group developing next generation methods. We propose a two year starter project with a combination of training and scientific effort in the UK and China synchronized with a range of supporting activities that will build the foundation for a subsequently self supporting (combination of UK and Chinese funds) 'International Research Centre'. The Centre will combine our world leading technologies and manpower to accelerate research excellence and delivery of numerical modelling insights and solutions to grand challenge environmental problems in the UK and China way beyond the capability of the UK alone. A relatively small investment would leverage China's massive past, current and future investments in IAP and past UK investments in next generation environmental flow models (particularly the multi-scale ocean model Fluidity-ICOM). This collaboration will develop a world leading predictive modelling framework. The starter project funded here will provide the focus for the training and collaboration so as to apply IAP's data assimilation methods to our multi-scale ocean model Fluidity-ICOM.Next Generation Ocean-Atmosphere Model: A Grand Challenge in Earth System Science is modelling the global circulation across the full range of relevant spatial and temporal scales. For climate prediction, this means resolving both basin scale and smaller scale features such as boundary currents, mixing; chemical interactions and transport, overflows, and mesoscale eddies. Such simulations will lie well beyond the capability of traditional ocean and atmosphere models. It is now generally recognised that the next generation of ocean models will be based on unstructured mesh technology as currently this is the only feasible way of resolving the important range of scales in coastal regions. As identified by the NERC strategy document 'oceans 2025 WP9', unstructured mesh ocean models are the key ocean modelling technology for the future modelling of multi-scale ocean to estuary and smaller scale modelling. Among existing unstructured mesh models, ICOM-Fluidity is the only model that can be used for simulation of flow on all scales using adaptive mesh resolution and is therefore an ideal platform for the next generation of data assimilation models to be developed on. One result of the training program will be that ICOM-Fluidity will be used to form a forward model of the China Sea. There will be a large amount of data to assimilate into the model e.g. satellite, argo floats and ship tracks. Ensemble Kalman Filter EnKF and gradient or adjoint based data assimilation methods will be used with ICOM-Fluidity to provide forecasts and to interpolate available data.Planned activities that will support the IAP - AMCG-ICL research:1) Training courses, workshops and Summer schools.2) PhD students, PDRAs and senior staff time to apply (and help develop) the model e.g. set up the UK and China sea model and develop uncertainty, reduced order and data assimilation methods.3) Exchanges of academic staff and PhD students.4) Development of a new substantial funding grant in China and the UK to fund the centre.5) Strengthened UK link and development of further initiatives with the Chinese Academy of Sciences.6) Formalised visiting status for key Imperial College researchers

中国在解决环境问题方面日益发挥带头作用，而且随着中国计划为此目的进行大量投资，这种情况还将持续下去。北京大气物理研究所（IAP）是该领域的国际领先机构，将从这项额外投资中受益匪浅。伦敦帝国学院应用建模和计算小组 (AMCG-ICL) 是一个开发下一代方法的环境建模小组。我们提出了一个为期两年的启动项目，结合了英国和中国的培训和科学工作，并同步开展一系列支持活动，这将为随后的自负盈亏（英国和中国资金相结合）“国际研究中心”奠定基础。该中心将结合我们世界领先的技术和人力，加速卓越研究，提供数值模拟见解和解决方案，以应对英国和中国面临的环境问题，远远超出英国的能力。相对较小的投资将利用中国过去、现在和未来对 IAP 的大量投资以及英国过去对下一代环境流模型（特别是多尺度海洋模型 Fluidity-ICOM）的投资。此次合作将开发世界领先的预测建模框架。这里资助的启动项目将提供培训和合作的重点，以便将 IAP 的数据同化方法应用于我们的多尺度海洋模型 Fluidity-ICOM。下一代海洋-大气模型：地球系统科学的一大挑战是对跨越所有相关空间和时间尺度的全球循环。对于气候预测，这意味着要解决盆地尺度和较小尺度的特征，例如边界流、混合；化学相互作用和传输、溢出和中尺度涡流。这种模拟将远远超出传统海洋和大气模型的能力。现在人们普遍认为，下一代海洋模型将基于非结构化网格技术，因为这是目前解决沿海地区重要尺度范围的唯一可行方法。正如 NERC 战略文件“海洋 2025 WP9”所指出的，非结构化网格海洋模型是未来多尺度海洋到河口建模和较小尺度建模的关键海洋建模技术。在现有的非结构化网格模型中，ICOM-Fluidity是唯一可以使用自适应网格分辨率来模拟所有尺度流动的模型，因此是开发下一代数据同化模型的理想平台。培训计划的成果之一将是利用 ICOM-Fluidity 来构建中国海的正演模型。将有大量数据需要吸收到模型中，例如卫星、argo 浮标和船舶轨迹。集成卡尔曼滤波器 EnKF 和基于梯度或伴随的数据同化方法将与 ICOM-Fluidity 一起使用，以提供预测并插入可用数据。支持 IAP - AMCG-ICL 研究的计划活动：1) 培训课程、讲习班和暑期学校.2) 博士生、PDRA 和高级职员有时间应用（并帮助开发）模型，例如建立英国和中国的海洋模型，并开发不确定性、降序和数据同化方法。3) 学术人员和博士生的交流。4) 在中国和英国制定一项新的实质性拨款资助该中心。5)加强英国与中国科学院的联系并制定进一步的举措。6) 帝国理工学院主要研究人员的正式访问身份

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Reduced order modelling of an unstructured mesh air pollution model and application in 2D/3D urban street canyons

非结构化网格空气污染模型的降阶建模及其在 2D/3D 城市街道峡谷中的应用

DOI：
10.1016/j.atmosenv.2014.07.021
发表时间：
2014-10-01
期刊：
Atmospheric Environment
影响因子：
5
作者：
F. Fang;T. Zhang;D. Pavlidis;C. Pain;A. Buchan;Ionel M. Navon
通讯作者：
Ionel M. Navon

An ensemble method for sensor optimisation applied to falling liquid films

应用于下降液膜的传感器优化的集成方法

DOI：
10.1016/j.ijmultiphaseflow.2014.06.013
发表时间：
2014-12-01
期刊：
International Journal of Multiphase Flow
影响因子：
3.8
作者：
Zhizhao Che;F. Fang;J. Percival;C. Pain;O. Matar;M. Navon
通讯作者：
M. Navon

Non-linear model reduction for the Navier-Stokes equations using residual DEIM method

使用残差 DEIM 方法简化 Navier-Stokes 方程的非线性模型

DOI：
10.1016/j.jcp.2014.01.011
发表时间：
2014-04-01
期刊：
J. Comput. Phys.
影响因子：
0
作者：
D. Xiao;F. Fang;A. Buchan;C. Pain;Ionel M. Navon;Juan Du;G. Hu
通讯作者：
G. Hu

Reduced order borehole induction modelling

降阶钻孔感应建模

DOI：
10.1080/10618562.2014.923846
发表时间：
2014-03-01
期刊：
International Journal of Computational Fluid Dynamics
影响因子：
1.3
作者：
N. Ardjm;pour;pour;C. Pain;F. Fang;J. Singer;M. Player;Xu Xu;Ionel M. Navon;J. Carter
通讯作者：
J. Carter

A POD reduced-order model for eigenvalue problems with application to reactor physics

特征值问题的 POD 降阶模型及其在反应堆物理中的应用