Echoes in radar reflectivity data do not always correspond to precipitating particles. Echoes on radar may be due to biological targets such as insects, birds or wind-borne particles, due to anomalous propagation (AP) or ground clutter (GC) or due to test and interference patterns that inadvertently seep into the final products. Although weather forecasters can usually identify, and account for, the presence of such contamination, automated weather radar algorithms are drastically affected.Several horizontal and vertical features have been proposed to discriminate between precipitation echoes and echoes that do not correspond to precipitation. None of these features by themselves can discriminate between precipitating and non-precipitating areas. In this paper, we use a neural network to combine the individual features, some of which have already been proposed in the literature and some of which we introduce in this paper, into a single discriminator that can distinguish between "good" and "bad" echoes (i.e., precipitation and non-precipitation respectively). The method of computing the horizontal features leads to statistical anomalies in their distributions near the edges of echoes. We describe how to avoid presenting such range gates to the neural network. The gate-by-gate discrimination provided by the neural network is followed by more holistic postprocessing based on spatial contiguity constraints and object identification to yield quality-controlled radar reflectivity scans that have most of the bad echo removed, while leaving most of the good echo untouched. A possible multi-sensor extension, utilizing satellite data and surface observations, to the radar-only technique is also demonstrated. We demonstrate the resulting technique is highly skilled, and that its skill exceeds that of the currently operational algorithm.
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