Statistical Signal Processing Approach For Rain Estimation Based On Measurements From Network Management Systems

In this paper we apply statistical signal processing methodologies on a real-world application of using Commercial Microwave Links (CMLs) as opportunistic sensors for rain monitoring. We formulate an appropriate parameter estimation problem, taking advantage on the empirically evaluated statistics of the rain, and present a new methodology for rain estimation given only the quantized minimum and maximum radio signal level measurements, which are being logged regularly by the network management systems. Our method transforms measurements taken from any single CML, without the need for training series, nor any prior or side information, into rainfall estimates, that is - to a virtual rain gauge. The operation of the proposed method was demonstrated using actual CMLs in Israel in a semi-arid climate zone, and shows that the achieved rain estimates agrees with near-by dedicated rain gauges.