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EMPIRICAL MODE DECOMPOSITION AND THE HILBERT-HUANG TRANSFORM


A new method, the Hilbert-Huang Transform, has been developed for analyzing nonlinear and nonstationary data by Dr. Norden Huang at Goddard Space Flight Center NASA.The full paper is published at Proc. Royal Society Long. A , 1998 Number 454, page 903-995.Here we briefly describe the methods and sample results applying this method to the atmospherical science data.

The key part of the method is the Empirical Mode Decomposition with which any complicated data set can bedecomposed into a finite and often small number of Intrinsic Mode Functions (IMF). An IMF is defined as any function having the same numbers ofzero-crossings and extrema, and also having symmetric envelopes defined by the local maxima and minima respectiely.The IMF also admits well-behaved Hilbert transform. This decompositionmethod is adaptive, and, therefore, highly efficient. Since thedecomposition is based on the local characteristic time scale of thedata, it is applicable to nonlinear and nonstationary processes.With the Hilbert transform, the Intrinsic Mode Functions yield instantaneous frequencies as functions of time that give sharpidentifications of imbedded structures. The final presentationofthe results is an energy-frequency-time distribution, designated as the Hilbert Spectrum. With this technique we can examine the detailed dynamic characteristics of a nonlinear system through instantaneous frequency rather than harmonics.Thus it constitutes a newview of nonlinear dynamics.

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