In-depth development and assessment of covariance models for multivariate nonstationary processes on a sphere

球面上多元非平稳过程协方差模型的深入开发和评估

• 批准号: 1208421
• 负责人: Mikyoung Jun
• 金额: $ 10万
• 依托单位: Texas A&M University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-07-01 至 2016-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1208421&HistoricalAwards=false
• 关键词: depth; development; assessment; covariance; models

With the advancement of science and technology, many geophysical problems involve data sets on a global scale with multiple variables of interest. As a consequence, there is a pressing need for flexible nonstationary covariance models for multivariate processes defined on a sphere. On the other hand, the development of covariance models for geostatistical data should accompany methodologies for thorough goodness-of-fit diagnostics of the proposed models. The goal of this project is to provide in-depth development of multivariate spatial (-temporal) covariance models on a spherical domain that address certain nonstationary properties of the covariance structures, in small and large scale, paired with thorough goodness-of-fit diagnostics of the proposed models. Proposed covariance models are flexible to capture complex nonstationarity, such as varying smoothness and geometric anisotropy. The investigator develops Bayesian and non-Bayesian methods for goodness-of-fit diagnostics and studies the effect of plug-in estimators of covariance models. The investigator applies the models developed in this project to the problem of validating multiple climate model outputs from the CMIP5 archive with thorough diagnostics of the model fits. Different covariance structures over land and the ocean, as well as their dependence on latitude, are thoroughly investigated. The investigator also explores efficient computation methods for large data sets. The motivation of this project is the scientific problem of combining multiple climate model outputs and studying the relationships between different climate variables. A great deal of effort is being invested in developing state-of-the-art climate models by climate scientists worldwide, and a new set of climate model outputs (CMIP5-Coupled Model Intercomparison Project Phase 5), that are the basis of the results in the fifth assessment report of the Intergovernmental Panel on Climate Change (IPCC), are becoming available. This project will provide climate scientists and climate modelers with useful tools to intercompare and combine climate models and test the improvement of the CMIP5 results over the previous CMIP3 results. Statistical models developed in this project will enable more accurate assessments of the relationships between multiple climate variables such as temperature and precipitation, and future climate change.

随着科学技术的进步，许多地球物理问题涉及到全球范围内的具有多个感兴趣变量的数据集。因此，迫切需要定义在球面上的多变量过程的灵活的非平稳协方差模型。另一方面，开发地质统计数据的协方差模型应伴随着对拟议模型进行彻底的拟合度诊断的方法学。这个项目的目标是在球域上深入开发多变量空间(时间)协方差模型，该模型解决了协方差结构的某些小尺度和大尺度的非平稳特性，并结合所提出的模型的彻底的拟合度诊断。所提出的协方差模型可以灵活地捕捉复杂的非平稳性，如变化的光滑性和几何各向异性。研究人员发展了贝叶斯和非贝叶斯方法用于拟合优度诊断，并研究了协方差模型的插件估计器的效果。研究人员将本项目中开发的模式应用于从CMIP5档案中验证多个气候模式输出的问题，并对模式匹配进行了彻底的诊断。深入研究了陆地和海洋上不同的协方差结构及其对纬度的依赖关系。研究人员还探索了针对大数据集的高效计算方法。该项目的动机是组合多个气候模式输出并研究不同气候变量之间的关系的科学问题。世界各地的气候科学家正在投入大量精力开发最先进的气候模型，作为政府间气候变化专门委员会(气专委)第五次评估报告结果的基础的一套新的气候模型产出(CMIP5--模式相互比较项目第五阶段)即将面世。该项目将为气候科学家和气候模型师提供有用的工具，以相互比较和组合气候模型，并测试CMIP5结果比以前CMIP3结果的改进情况。该项目开发的统计模型将能够更准确地评估温度和降水等多种气候变量与未来气候变化之间的关系。