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Intense precipitation phenomena occurring over the Tyrrhenian area between Tuscany, Corse Sardinia, and Liguria very
often cause floods with considerable socio-economic damages. The need of monitoring such events has led to the
implementation of an observing weather radar network: it firstly started with an S-band radar in Corse, three C-band
radars in Liguria, Tuscany and Sardinia. Recently, the implementation of an X–band network of three radars in Tuscany
and two further C-band radars in Sardinia completed the network. This work shows how this network can be used for the
characterization of weather events, following their development and dynamics and providing some information about
their possible evolution. Furthermore, the use of meteorological satellites observations can upscale the area of interest to
the mesoscale level and provide an enlarged temporal overview. For instance, the Meteosat Second Generation satellites
provide useful information about the air mass distribution, convective phenomena occurrence and microphysics in the
observed scene, by combining different spectral channels. Finally, ground based observations are meaningful for
assessing the observing capabilities of other instruments and for characterizing the effects on soil surface. For some
selected case studies, the different observing instruments were compared and a methodology to integrate them
synergically is presented and tested. Weather radars correctly detect the rainfall systems and their motion in all the case
studies. Clearly, the higher spatial resolution of X-band radars allows detecting the different precipitation areas with
great spatial details, while C- and S-band radars can detect phenomena at higher distances. Satellites images have lower
spatial resolutions but especially thanks to the RSS (Rapid Scan Service) they can help to detect the growing or
dissipating stage of the whole phenomena. Moreover the ground-based network confirms its relevance in improving the
identification of the precipitation intensity and in reducing the number of false alarms.
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