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Rain pattern detection by means of packet wavelets

The recent advances in microwave telecommunications and the need for more precise weather forecasting systems are two of the many fields where it is extremely important to be able to analyze quickly, precisely and, possibly, in a fully or partially automatic way, the data obtained by systems like meteorological radars. This work presents a wavelet packet based algorithm, combined with a C-means classifier, for rain patterns detection and tracking from this data. The use of this kind of classification chain is motivated by the high efficiency and low computational load of the wavelet transform algorithm and by the observation that a large class of natural textures can be modeled as quasi-periodic signal, whose dominant frequencies are located in the middle frequency channels, easily provided by this transform. The chain was applied to a radar data sequence of a rain event of Northern Italy. The storm dynamics were studied at different meso-scales.