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

• 批准号: 0004131
• 负责人: Brani Vidakovic
• 金额: $ 10.74万
• 依托单位: Georgia Tech Research Corporation
• 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 2004-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0004131&HistoricalAwards=false
• 关键词: Bayesian; Modeling; Wavelet; Domain; Applications

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.

该提案建立在之前由美国国家科学基金会资助的项目DMS-9626159的基础上，该项目授予杜克大学。它涉及发展多分辨率模型的统计概念、理论和方法，以及对大气湍流测量和结构的分析。它借鉴了贝叶斯多尺度模型的最新发展，以理解湍流的时间尺度方面，其中尺度和层次的概念是内在的。多分辨率统计建模方法被应用于由空气质量现场实验和模拟产生的大量地球物理测量，以确定大气湍流的关键结构特性，这些特性负责标量（如臭氧）的传输。为了实现这些目标，一个具有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）专业知识的研究小组被召集起来。该项目的适用性是通过新的统计技术提高对大气输送的基本理解。大气输送是描述空气质量的关键，它是一种复杂的现象，涉及对造成污染物扩散的湍流的建模。所提出的方法的适用性将直接在北卡罗莱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数据上进行测试。该奖项的部分支持由大气科学部的物理气象项目提供。