Wavelet Analysis of Meteorological Data

Introduction The investigation of the properties of meteorological data is important for the analysis and classification of measured data, generation of synthetic data for simulation, and statistical description of solar systems. Since the temporal variations of meteorological data are nondeterministic, they are usually described by the conventional tools for stochastic functions like probability density function, auto- and cross-correlation functions, power density spectrum and correlation coefficients. An important feature of meteorological data that is not to be overlooked is that they contain information on different time scales. The most obvious examples are the seasonal and daily cycles with fixed and well known periods. Depending on the location, also variations on the scale of several days to several weeks occur. They do not possess sharply defined periods and may be present only at certain times of the year. As an example, temperature variations on this scale are imp