Quality Control of Weather Radar Data Using Texture Features and a Neural Network

V Lakshmanan, Kurt Hondl, Gregory Stumpf, Travis Smith


Weather radar data is subject to many contaminants, mainly due to non-precipitating targets (such as insects and wind-borne particles) and due to anamalous propagation (AP) or ground clutter. Although weather forecasters can usually identify, and account for, the presence of such contamination, automated weather algorithms are affected drastically. We discuss several local texture features and image processing steps that can be used to discriminate some of these types of contaminants. None of these features by themselves can discriminate between precipitating and nonprecipitating areas. A neural network is used for this purpose. We discuss training this neural network using a million-point data set, and accounting for the fact that even this data set is necessarily incomplete.

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