Bayesian Modeling in the Wavelet Domain with Applications in Atmospheric Turbulence

该提案建立在先前NSF赞助的授予杜克大学的项目DMS-9626159的基础上。 它涉及多分辨率模式的统计概念、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，其中尺度和层次的概念是内在的。 多分辨率统计建模方法被应用到大量的地球物理测量所产生的空气质量现场实验和模拟，以确定大气湍流的标量，如臭氧的运输负责的关键结构特性。 为了实现这些目标，一个在统计建模和多尺度方法（M。彭斯基和B。Vidakovic）和大气湍流的测量和模拟（G. Katul）的集合。该项目的可应用性是通过新的统计技术提高对大气传输的基本理解。 大气传输是描述空气质量的关键，是一个复杂的现象，涉及到湍流的模拟，负责污染物的扩散。 所提出的方法的适用性将直接测试高频速度，温度和臭氧浓度测量收集在杜克森林，达勒姆，北卡罗来纳州。该奖项的部分支持由大气科学部的物理气象学计划提供。

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.