A new inverse method and application to ocean data

A new method is proposed to inverse normalization data of hidden variables in a dynamical system by embedding a time series in multidimensional spaces and applying a normalization analysis to the conditional probability densi ty of points in the reconstructed phase spaces, The method is robust in the application to Lorenz system and 4-dimensional Rossler system by testing q uantitatively and qualitatively the correlation coefficient between inverse data and original data in time domain and in frequency domain, respectivel y. By applying the method to analyzing the South China Sea data, the normal ization data of wind speed is extracted from the sea surface temperature ti me series.