Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
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Weather radar data is susceptible to several artifacts due to anamalous propagation, ground clutter, electronic interference, sun angle, second-trip echoes and biological contaminants such as insects, bats and birds. Several methods of censoring radar reflectivity data have been devised and described in the literature. However, they all rely on analyzing the local texture and vertical profile of reflectivity fields. The local texture of reflectivity fields suffices to remove most artifacts, except for biological echoes. Biological echoes have proved difficult to remove because they can have the same returned power and vertical profile as stratiform rain or snow. In this paper, we describe a soft-computing technique based on clustering, segmentation and a two-stage neural network to censor all non-precipitating artifacts in weather radar reflectivity data. We demonstrate that the technique is capable of discrimination between light snow, stratiform rain and deep biological "bloom".