Precipitation classification using measurements from commercial microwave links

Commercial wireless microwave links have been recently proven to be an effective tool for precipitation monitoring, mainly for accurate rainfall estimation and high-resolution rainfall mapping. This paper focuses on the challenge of precipitation classification from the measurements of received signal level (RSL) in several commercial wireless microwave links, by suggesting a tree of classification based on the physical features that distinguish between different phenomena. Wet periods are first identified, followed by a classification of the wet periods into pure rain or sleet. The classification is based on the kernel Fisher discriminant analysis, followed by a decision-making process. The suggested procedure is tested on real data, and its performance is evaluated. It is shown that the proposed classification is in very good agreement (85%) with that of a special-purpose meteorological device called disdrometer.