Detection and Analysis of Microfronts and Associated Coherent Events Using Localized Transforms

Abstract

A general localized transforms is used to study the characteristics of conditional sampling techniques. A covariance transform is defined that measures the covariance between the signal and a dilated and translated generating function which has compact support. It is shown to be superior as a method of detecting events containing sharp edges such as fronts or microfronts when compared with energy-preserving transforms such as the Fourier and wavelet transform. A method of finding the most coherent scale with respect to dilation of the generating function is presented. A sample analysis using sonic data measured in shear driven boundary-layer atmospheric turbulence is presented. Abstract A general localized transforms is used to study the characteristics of conditional sampling techniques. A covariance transform is defined that measures the covariance between the signal and a dilated and translated generating function which has compact support. It is shown to be superior as a method of detecting events containing sharp edges such as fronts or microfronts when compared with energy-preserving transforms such as the Fourier and wavelet transform. A method of finding the most coherent scale with respect to dilation of the generating function is presented. A sample analysis using sonic data measured in shear driven boundary-layer atmospheric turbulence is presented.