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A Field Assessment of a Rain Estimation System Based on Satellite-to-Earth Microwave Links

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TitleA Field Assessment of a Rain Estimation System Based on Satellite-to-Earth Microwave Links
Publication TypeJournal Article
Year of Publication2019
AuthorsColli, M, Stagnaro, M, Caridi, A, Lanza, LG, Randazzo, A, Pastorino, M, Caviglia, DD, Delucchi, A
JournalIEEE Transactions on Geoscience and Remote Sensing

This paper describes the results obtained by the application of an innovative environmental monitoring technique able to estimate rainfall intensity in real time by processing the attenuation of microwave satellite link signal measured by low cost sensors. The satellite that has been used during our work, Turksat 42° E, belongs to the plethora of satellites operating for television and radio channel broadcasting. Each sensor exploits off the shelf components, is equipped with a radio frequency power-measuring unit, and provides connectivity to the server over a wide area network. A validation of the approach with a field comparison experiment at the urban scale, comprising three measurement sites equipped with such sensors, was established since autumn 2016 in the municipality of Genoa, Italy. Point-scale rainfall intensity measurements made by two calibrated tipping-bucket rain gauges constitute the reference for the comparative analysis of the microwave sensors performance. The dynamic calibration of the rain gauges was carried out by using an automatic calibration rig and the measurements have been processed with advanced algorithms to reduce counting errors. The experimental setup allowed a full characterization of the microwave signal trends as a function of different precipitation. The results showed a strong correlation between the microwave signal attenuation and the reference rainfall observations and demonstrated the possibility to retrieve ten-minute rain accumulations from the microwave links by adopting a proper electromagnetic model. The comparison between the different measuring systems is performed by computing the time series statistics and the frequency of the rain conditions for each precipitation event.