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.