The Application Of Hilbert-Huang Transform To The Clime Model

In recent years, the theory and method of the signal processing have been developed rapidly. The analysis of the non-stationary signal is a new developing field in the modern signal processing. A new method, Hilbert-Huang Transform (HHT), was proposed by Huang et al. in 1998. It is especially effective for adaptively analyzing non-stationary and nonlinear data. The key part of the method is the empirical mode decomposition (EMD) method. It can decompose any complicated data set into a finite and often-small number of intrinsic mode functions (IMF) that admit well-behaved Hilbert transforms. Since the decomposition is based on the local characteristic time scale of the data, it is applicable to nonlinear and non-stationary processes. With the Hilbert transform, the IMF yields instantaneous frequencies and the amplitude functions. The final presentation of the results is an energy-frequency-time distribution, named as the Hilbert spectrum. As yet this method has already been applied to such a lot of non-linear research fields as turbulence, earthquake, finance, etc. successfully. It has the theoretical research value and wide application space.The palaeoclimate quantitative reconstruction is one of the main topics of the PAGES research. Combined with pollen-climate response surface model,The different climate indexes of Daihai were achieved by the data of pollens and spores in surface sediments of Daihai Lake, Inner Mongolia. This paper applies Hilbert-Huang transform to analyze above results. Every IMF component includes different period characters. This is helpful to the further research. At the same, the experiment shows the advantage of HHT to analysis the nonlinear and non-stationary signal.