Abstract
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.
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