WAVELET ANALYSIS OF A MICROBAROGRAPH NETWORK

This paper presents a wavelet-based algorithm for the detection, ident ification, and extraction of gravity waves from atmospheric pressure t races, The main data processing tool is a nonlinear adaptive filter ba sed on the selective reconstruction of a waveform from its wavelet coe fficients, The time-frequency localization of the wavelet transform pr ovides an ideal framework for the decomposition of long-period gravity waves (30 min-6 h), which are characterized by a generally broad spec trum and few oscillation cycles, The procedure is iterative and allows the exhaustive processing of all the events present in a fixed time p eriod, The waveform of each disturbance is reconstructed with high acc uracy. This minimizes the influence of the data-processing technique o n the estimate of horizontal speed and direction of propagation, obtai ned by maximization of the cross-correlation functions between the rec onstructed waveforms at the different stations, The introduction of co herency criteria through the network of seven stations allows us to se parate the events into two classes. The first includes the events that propagate with very small distortion through the network, while the s econd includes less coherent but still highly energetic events, The si ze of the network and the algorithm developed for the analysis is well suited for the identification and the extraction of those mesoscale d isturbances that have a particularly strong influence on the weather a s well as on the forecast.