A neurocomputing model for SODAR structure classification

The manual identification of different types of atmospheric microstructures recorded by SODAR (SOund Detection And Ranging) is a tedious task and can be performed only by an expert with broad experience. To avoid this manual task, a neural network based method of SODAR structure classification system is proposed. This method is developed based on past observations of various meteorological parameters such as temperature, relative humidity and vapour pressure, along with different features computed from the SODAR structure data, which are images representing the dynamics of the planetary boundary layer (PBL). The patterns of these images indicate the structure of different thermal patterns of the atmosphere. We propose a neural network model whose architecture combines multilayer perceptron networks (MLPs) to realize better performance after capturing the seasonality and other related effects in the atmospheric data. We also demonstrate that the use of appropriate features can further improve performance of the prediction system. These observations inspired us to use a feature selection neural network which can select good features online while learning the prediction task. The feature selection neural network is used as a preprocessor to select good features. The combined use of feature selection neural network and MLP, i.e. FSMLP (feature selection multilayer perception) results in a neural network system that uses only very few inputs but can produce a good classifier. Here we develop a real-time system that classifies the SODAR patterns automatically.