Uncertainty quantification for the linking of spatio-temporal output of computer model hierarchies and the real world

计算机模型层次结构的时空输出与现实世界联系的不确定性量化

• 批准号: EP/K019112/1
• 负责人: Daniel Williamson
• 金额: $ 27.96万
• 依托单位: UNIVERSITY OF EXETER
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2013
• 资助国家: 英国
• 起止时间: 2013 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FK019112%2F1
• 关键词: Uncertainty; quantification; linking; spatio; temporal

Quantification of uncertainty introduced by using computer models to study complex physical systems is a fundamental problem for modern science. Though a statistical methodology exists to perform the uncertainty quantification (UQ), the technology is not yet at the level required by high-end users with very slow and expensive computer models, such as the latest climate models. The research proposed will provide methodological developments in two key areas that will facilitate UQ for high-end models. The first is a methodology for modelling spatio-temporal output of computer models dynamically. The methods developed will allow statistical models that represent the uncertainty in the spatio-temporal output of a computer simulator, for any choice of its input parameters, to be created that, when sampled from, will allow the modelled spatial field to evolve in time in a way that mimics the simulator and that reports the uncertainty in the representation. This will represent an important step forward for researchers in a variety of scientific disciplines, such as climate, where the evolution of spatial fields in time is of great interest. The proposed work will be developed from methods that the applicant has worked on for univariate time series and will combine techniques for the Bayesian analysis of multiple time series from the literature of state space modelling, with UQ methods that have explored basis expansions of spatial fields with Gaussian process emulation in order to make methodological advances.The second involves using the first methodology in order to develop methods for using a hierarchy of related, but lower resolution models combined with whatever runs currently exist on the high-end, state of the art, simulator, to model spatio-temporal output of the high-end simulator dynamically. This will be done by adapting and extending current methods for linking two hierarchical simulators statistically.The research will apply these methods to the Nucleus for European Modelling of the Ocean (NEMO) framework of ocean models. This framework contains a hierarchy of four ocean models, with the high-end model taking months to run at a single setting of the parameters on a super-computer due to the fine spatial resolution of the solver, and the fastest version running quickly on a desktop computer so that large ensembles can be generated. The high-end model, known as ORCA12, forms the ocean component of the UK's current climate model, HadGEM3-H. Working with collaborators at the National Oceanography Centre (NOC), the methodologies will be applied to existing and specially designed ensembles within the hierarchy in order to model key spatio-temporal fields of interest to the collaborators in ORCA12. This will aid them in understanding the response of the outputs of this important model and assist in its future development.A further goal will be to facilitate uncertainty quantification for key spatio-temporal fields in the real ocean using the statistical model for ORCA12 and standard UQ methods.

利用计算机模型研究复杂物理系统所引入的不确定性的量化是现代科学的一个基本问题。虽然存在一种统计方法来执行不确定性量化（UQ），但该技术尚未达到高端用户所需的水平，这些用户使用非常缓慢和昂贵的计算机模型，例如最新的气候模型。拟议的研究将在两个关键领域提供方法学发展，这将有助于高端车型的UQ。第一个是一种方法，用于动态地模拟计算机模型的时空输出。所开发的方法将允许统计模型，表示在时空输出的计算机模拟器的不确定性，其输入参数的任何选择，被创建，当采样时，将允许模拟的空间场在时间上演变的方式，模仿模拟器和报告的不确定性的表示。这将是一个重要的一步，为研究人员在各种科学学科，如气候，在那里的演变空间场的时间是非常感兴趣的。所提出的工作将从申请人已经针对单变量时间序列进行的方法中发展，并且将联合收割机技术与来自状态空间建模的文献的用于多个时间序列的贝叶斯分析相结合，与UQ的方法，探索了基础扩展的空间领域与高斯过程仿真，以使方法的进步。第二涉及使用第一种方法，以开发方法，相关但较低分辨率的模型的层次结构与当前存在于高端、最先进的模拟器上的任何运行相结合，以动态地对高端模拟器的时空输出进行建模。这将通过调整和扩展目前的方法来实现，用于在统计上连接两个分层模拟器，研究将把这些方法应用于欧洲海洋模拟核心（NEMO）海洋模型框架。该框架包含四个海洋模型的层次结构，由于求解器的精细空间分辨率，高端模型在超级计算机上以单个参数设置运行需要数月时间，而最快的版本在台式计算机上快速运行，以便可以生成大型集合。高端模型被称为ORCA 12，构成了英国当前气候模型HadGEM 3-H的海洋部分。与国家海洋学中心（NOC）的合作者合作，这些方法将应用于层次结构中现有的和专门设计的集合，以便对ORCA 12中合作者感兴趣的关键时空领域进行建模。这将有助于他们了解这个重要模式输出的响应，并有助于其未来的发展。进一步的目标是利用ORCA 12的统计模型和标准UQ方法，促进真实的海洋关键时空场的不确定性量化。

项目成果

THE ART AND SCIENCE OF CLIMATE MODEL TUNING

• DOI: 10.1175/bams-d-15-00135.1
• 发表时间: 2017-03-01
• 期刊: BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY
• 影响因子: 8
• 作者: Hourdin, Frederic;Mauritsen, Thorsten;Williamson, Daniel
• 通讯作者: Williamson, Daniel

Efficient calibration for high-dimensional computer model output using basis methods

• DOI: 10.1615/int.j.uncertaintyquantification.2022039747
• 发表时间: 2019-06
• 期刊: International Journal for Uncertainty Quantification
• 影响因子: 1.7
• 作者: James M. Salter;D. Williamson

Tuning without over-tuning: parametric uncertainty quantification for the NEMO ocean model

• DOI: 10.5194/gmd-10-1789-2017
• 发表时间: 2016-08
• 期刊: Geoscientific Model Development
• 影响因子: 5.1
• 作者: D. Williamson;A. Blaker;B. Sinha

Uncertainty Quantification for Computer Models With Spatial Output Using Calibration-Optimal Bases

• DOI: 10.1080/01621459.2018.1514306
• 发表时间: 2019-03-20
• 期刊: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• 影响因子: 3.7
• 作者: Salter, James M.;Williamson, Daniel B.;Kharin, Viatcheslav
• 通讯作者: Kharin, Viatcheslav

Posterior Belief Assessment: Extracting Meaningful Subjective Judgements from Bayesian Analyses with Complex Statistical Models

后置信念评估：从复杂统计模型的贝叶斯分析中提取有意义的主观判断

• DOI: 10.48550/arxiv.1512.00969
• 发表时间: 2015
• 作者: Williamson D