Bayesian Modeling in the Wavelet Domain with Applications in Atmospheric Turbulence

小波域贝叶斯建模及其在大气湍流中的应用

基本信息

批准号：
0072585
负责人：
Gabriel Katul
金额：
$ 1.86万
依托单位：
Duke University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2000
资助国家：
美国
起止时间：
2000-09-15 至 2003-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=0072585&HistoricalAwards=false
关键词：
Bayesian Modeling Wavelet Domain Applications

项目摘要

This proposal builds on a previous NSF sponsored project DMS-9626159, awarded to Duke University. It involves development of statistical concepts, theories and methods for multiresolution models and analyses of measurements and structures in atmospheric turbulence. It draws on recent developments in Bayesian multiscale modeling to understand the time-scale aspects of turbulence where the notions of scale and hierarchy are intrinsic. Multiresolution statistical modeling approaches are applied to vast geophysical measurements arising from air quality field experiments and simulations in order to identify key structural properties of atmospheric turbulence responsible for the transport of scalars, such as ozone. To achieve these objectives a team of researchers with expertise in statistical modeling and multiscale methods (M. Pensky and B. Vidakovic) and measurement and modeling of atmospheric turbulence (G. Katul) is assembled.The aplicability of this project is improved fundamental understanding of atmospheric transport via novel statistical techniques. Atmospheric transport, key to describing air-quality, is a complex phenomena involving modeling of turbulence which is responsible for the dispersion of polutants. The applicability of the proposed methodology will be directly tested on high frequency velocity, temperature, and ozone concentration measurements collected at Duke Forest, Durham, North Carolina. Partial support for this award is provided by the Physical Meterology Program in the Division of Atmospheric Sciences.

该提案建立在之前 NSF 赞助的项目 DMS-9626159 的基础上，该项目授予杜克大学。它涉及多分辨率模型的统计概念、理论和方法的发展以及大气湍流测量和结构的分析。它利用贝叶斯多尺度建模的最新发展来理解湍流的时间尺度方面，其中尺度和层次结构的概念是固有的。多分辨率统计建模方法应用于空气质量现场实验和模拟产生的大量地球物理测量，以确定负责标量（例如臭氧）传输的大气湍流的关键结构特性。为了实现这些目标，组建了一支由统计建模和多尺度方法（M. Pensky 和 B. Vidakovic）以及大气湍流测量和建模（G. Katul）方面专业知识的研究人员组成的团队。该项目的适用性提高了对以下问题的基本理解：通过新颖的统计技术进行大气传输。大气传输是描述空气质量的关键，是一种复杂的现象，涉及对造成污染物扩散的湍流进行建模。该方法的适用性将直接在北卡罗来纳州达勒姆杜克森林收集的高频速度、温度和臭氧浓度测量值上进行测试。该奖项的部分支持由大气科学部的物理计量项目提供。