Neural and Statistical Classification of Weather Radar Measurements for the Prediction of Rainfall Rate

Weather radars are used to measure the electromagnetic radiation backscattered by cloud raindrops. Clouds that backscatter more electromagnetic radiation consist of larger droplets of rain and therefore they produce more rain. The idea is to predict rainfall rate by using weather radar instead of rain gauges measuring rainfall on the ground. In an experiment during two days in June and August 1997 over the Italian-Swiss Alps, data from a weather radar and surrounding rain gauges were collected at the same time. The neural SOM and the statistical KNN classifier were implemented for the classification task using the radar data as input and the rain-gauge measurements as output. The rainfall rate on the ground was predicted based on the radar reflections with an average error rate of 23%. The results in this work show that the prediction of rainfall rate based on weather radar measurements is possible