Collaborative Research: Theory and Methods for Highly Multivariate Spatial Processes with Applications to Climate Data Science

合作研究：高度多元空间过程的理论和方法及其在气候数据科学中的应用

基本信息

批准号：
1811384
负责人：
Soutir Bandyopadhyay
金额：
$ 9.45万
依托单位：
Colorado School of Mines
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-08-01 至 2023-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1811384&HistoricalAwards=false
关键词：
Collaborative Research Theory Methods Highly

项目摘要

Geophysical, environmental and ecological datasets often include many variables observed over a set of irregular geographical locations. While spatial datasets are increasing in size, they are also increasing in complexity with many variables being simultaneously observed, recorded, modeled or derived. Current methods in spatial statistics are unable to cope with such highly multivariate datasets; this research addresses this gap in statistical science, aiming to establish a new framework for multivariate spatial models. The testbed for the new framework is in the field of climate data science. Understanding of the Earth system relies on coupled physical models that represent the dynamic evolution of the atmosphere, ocean, land use, rivers, glaciers and other processes. These models have led to vast amounts of climate model data that severely constrain storage resources. Moreover, statistical emulators are increasingly common and desirable alternatives to running complex physical models directly. Development and validation of compression and emulation algorithms require understanding and maintaining complex dependencies between physical variables, but current tools are univariate or pairwise-based. This research will provide statistical guidance for climate data science applications.This project focuses on a modeling framework for multivariate spatial processes, and relies on new theory incorporating graphical models in multiscale multivariate spatial process representations. Moreover, many multivariate datasets exhibit non-Gaussian behavior. A companion thrust of this work is in introducing and exploring empirical likelihood techniques for large multivariate spatial processes. Finally, the proposed models and estimation frameworks will be applied to a climate dataset from the Community Atmosphere Model.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

地球物理，环境和生态数据集通常包括在一组不规则的地理位置上观察到的许多变量。尽管空间数据集的大小增加，但它们的复杂性也在增加，许多变量同时观察，记录，建模或衍生。空间统计中的当前方法无法应对这种高度多元数据集；这项研究解决了统计科学中的这一差距，旨在为多元空间模型建立新的框架。新框架的测试床是在气候数据科学领域。对地球系统的理解取决于代表大气，海洋，土地使用，河流，冰川和其他过程的动态演变的物理模型。这些模型导致大量的气候模型数据严重限制了存储资源。此外，统计模拟器是直接运行复杂物理模型的越来越普遍和理想的选择。压缩和仿真算法的开发和验证需要理解和维持物理变量之间的复杂依赖性，但是当前工具是单变量或基于成对的。这项研究将为气候数据科学应用提供统计指南。本项目的重点是用于多元空间过程的建模框架，并依靠将图形模型纳入多尺度多变量空间过程表示的新理论。此外，许多多元数据集表现出非高斯行为。这项工作的伴侣推力是为大型多元空间过程引入和探索经验可能性技术。最后，提出的模型和估计框架将从社区氛围模型中应用于气候数据集。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来获得支持。