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Weather radars are used to measure theelectromagnetic radiation backscattered by cloud raindrops. Clouds thatbackscatter more electromagnetic radiation consist of larger droplets ofrain and therefore they produce more rain. The idea is to estimate rain rateby using weather radar as an alternative to rain-gauges measuring rainfallon the ground. In an experiment during two days in June and August 1997 overthe Italian-Swiss Alps, data from weather radar and surrounding rain-gaugeswere collected at the same time. The statistical KNN and the neural SOMclassifiers were implemented for the classification task using the radardata as input and the rain-gauge measurements as output. The proposed systemmanaged to identify matching pattern waveforms and the rainfall rate on theground was estimated based on the radar reflectivities with a satisfactoryerror rate, outperforming the traditional <i>Z</i>/<i>R</i> relationship. It isanticipated that more data, representing a variety of possiblemeteorological conditions, will lead to improved results. The results inthis work show that an estimation of rain rate based on weather radarmeasurements treated with statistical and neural classifiers is possible

Statistical and neural classifiers in estimating rain rate from weather radar measurements